diff --git "a/Day01-20/02.\347\254\254\344\270\200\344\270\252Python\347\250\213\345\272\217.md" "b/Day01-20/02.\347\254\254\344\270\200\344\270\252Python\347\250\213\345\272\217.md"
index c1458234a..c2fade7c9 100755
--- "a/Day01-20/02.\347\254\254\344\270\200\344\270\252Python\347\250\213\345\272\217.md"
+++ "b/Day01-20/02.\347\254\254\344\270\200\344\270\252Python\347\250\213\345\272\217.md"
@@ -96,7 +96,7 @@ Visual Studio Code 是由微软开发能够在 Windows、 Linux 和 macOS 等操
 
 按照行业惯例，我们学习任何一门编程语言写的第一个程序都是输出`hello, world`，因为这段代码是伟大的丹尼斯·里奇（C 语言之父，和肯·汤普森一起开发了 Unix 操作系统）和布莱恩·柯尼汉（awk 语言的发明者）在他们的不朽著作《*The C Programming Language*》中写的第一段代码，下面是对应的 Python 语言的版本。
 
-```Python
+```python
 print('hello, world')
 ```
 
@@ -104,7 +104,7 @@ print('hello, world')
 
 上面的代码只有一个语句，在这个语句中，我们用到了一个名为`print`的函数，它可以帮助我们输出指定的内容；`print`函数圆括号中的`'hello, world'`是一个字符串，它代表了一段文本内容；在 Python 语言中，我们可以用单引号或双引号来表示一个字符串。不同于 C、C++ 或 Java 这样的编程语言，Python 代码中的语句不需要用分号来表示结束，也就是说，如果我们想再写一条语句，只需要回车换行即可，代码如下所示。此外，Python 代码也不需要通过编写名为`main`的入口函数来使其运行，提供入口函数是编写可执行的 C、C++ 或 Java 代码必须要做的事情，这一点很多程序员都不陌生，但是在 Python 语言中它并不是必要的。
 
-```Python
+```python
 print('hello, world')
 print('goodbye, world')
 ```
@@ -134,7 +134,7 @@ Python 中有两种形式的注释：
 1. 单行注释：以`#`和空格开头，可以注释掉从`#`开始后面一整行的内容。
 2. 多行注释：三个引号（通常用双引号）开头，三个引号结尾，通常用于添加多行说明性内容。
 
-```Python
+```python
 """
 第一个Python程序 - hello, world
 
diff --git "a/Day01-20/03.Python\350\257\255\350\250\200\344\270\255\347\232\204\345\217\230\351\207\217.md" "b/Day01-20/03.Python\350\257\255\350\250\200\344\270\255\347\232\204\345\217\230\351\207\217.md"
index 977f446a5..924516ff6 100755
--- "a/Day01-20/03.Python\350\257\255\350\250\200\344\270\255\347\232\204\345\217\230\351\207\217.md"
+++ "b/Day01-20/03.Python\350\257\255\350\250\200\344\270\255\347\232\204\345\217\230\351\207\217.md"
@@ -16,7 +16,7 @@
 
 1. 整型（`int`）：Python 中可以处理任意大小的整数，而且支持二进制（如`0b100`，换算成十进制是4）、八进制（如`0o100`，换算成十进制是64）、十进制（`100`）和十六进制（`0x100`，换算成十进制是256）的表示法。运行下面的代码，看看会输出什么。
 
-    ```Python
+    ```python
     print(0b100)  # 二进制整数
     print(0o100)  # 八进制整数
     print(100)    # 十进制整数
@@ -25,7 +25,7 @@
 
 2. 浮点型（`float`）：浮点数也就是小数，之所以称为浮点数，是因为按照科学记数法表示时，一个浮点数的小数点位置是可变的，浮点数除了数学写法（如`123.456`）之外还支持科学计数法（如`1.23456e2`，表示$\small{1.23456 \times 10^{2}}$）。运行下面的代码，看看会输出什么。
 
-    ```Python
+    ```python
     print(123.456)    # 数学写法
     print(1.23456e2)  # 科学计数法
     ```
@@ -53,7 +53,7 @@
 
 下面通过例子来说明变量的类型和变量的使用。
 
-```Python
+```python
 """
 使用变量保存数据并进行加减乘除运算
 
@@ -71,7 +71,7 @@ print(a / b)  # 3.75
 
 在 Python 中可以使用`type`函数对变量的类型进行检查。程序设计中函数的概念跟数学上函数的概念非常类似，数学上的函数相信大家并不陌生，它包括了函数名、自变量和因变量。如果暂时不理解函数这个概念也不要紧，我们会在后续的内容中专门讲解函数的定义和使用。
 
-```Python
+```python
 """
 使用type函数检查变量的类型
 
@@ -98,7 +98,7 @@ print(type(d))  # <class 'bool'>
 
 下面的例子为大家演示了Python中类型转换的操作。
 
-```Python
+```python
 """
 变量的类型转换操作
 
diff --git "a/Day01-20/07.\345\210\206\346\224\257\345\222\214\345\276\252\347\216\257\347\273\223\346\236\204\345\256\236\346\210\230.md" "b/Day01-20/07.\345\210\206\346\224\257\345\222\214\345\276\252\347\216\257\347\273\223\346\236\204\345\256\236\346\210\230.md"
index 8c1808e20..5c06f09b3 100755
--- "a/Day01-20/07.\345\210\206\346\224\257\345\222\214\345\276\252\347\216\257\347\273\223\346\236\204\345\256\236\346\210\230.md"
+++ "b/Day01-20/07.\345\210\206\346\224\257\345\222\214\345\276\252\347\216\257\347\273\223\346\236\204\345\256\236\346\210\230.md"
@@ -6,7 +6,7 @@
 
 > **说明**：素数指的是只能被 1 和自身整除的正整数（不包括 1），之前我们写过判断素数的代码，这里相当于是一个升级版本。
 
-```Python
+```python
 """
 输出100以内的素数
 
@@ -29,7 +29,7 @@ for num in range(2, 100):
 
 > **说明**：斐波那契数列（Fibonacci sequence），通常也被称作黄金分割数列，是意大利数学家莱昂纳多·斐波那契（Leonardoda Fibonacci）在《计算之书》中研究理想假设条件下兔子成长率问题而引入的数列，因此这个数列也常被戏称为“兔子数列”。斐波那契数列的特点是数列的前两个数都是 1，从第三个数开始，每个数都是它前面两个数的和。按照这个规律，斐波那契数列的前 10 个数是：`1, 1, 2, 3, 5, 8, 13, 21, 34, 55`。斐波那契数列在现代物理、准晶体结构、化学等领域都有直接的应用。
 
-```Python
+```python
 """
 输出斐波那契数列中的前20个数
 
@@ -125,7 +125,7 @@ for x in range(0, 21):
 
 > **说明**：CRAPS又称花旗骰，是美国拉斯维加斯非常受欢迎的一种的桌上赌博游戏。该游戏使用两粒骰子，玩家通过摇两粒骰子获得点数进行游戏。简化后的规则是：玩家第一次摇骰子如果摇出了 7 点或 11 点，玩家胜；玩家第一次如果摇出 2 点、3 点或 12 点，庄家胜；玩家如果摇出其他点数则游戏继续，玩家重新摇骰子，如果玩家摇出了 7 点，庄家胜；如果玩家摇出了第一次摇的点数，玩家胜；其他点数玩家继续摇骰子，直到分出胜负。为了增加代码的趣味性，我们设定游戏开始时玩家有 1000 元的赌注，每局游戏开始之前，玩家先下注，如果玩家获胜就可以获得对应下注金额的奖励，如果庄家获胜，玩家就会输掉自己下注的金额。游戏结束的条件是玩家破产（输光所有的赌注）。
 
-```Python
+```python
 """
 Craps赌博游戏
 
diff --git "a/Day01-20/09.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\210\227\350\241\250-2.md" "b/Day01-20/09.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\210\227\350\241\250-2.md"
index 59476a0a0..c28c4cc8a 100644
--- "a/Day01-20/09.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\210\227\350\241\250-2.md"
+++ "b/Day01-20/09.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\210\227\350\241\250-2.md"
@@ -115,7 +115,7 @@ print(nums2)
 
 场景三： 有一个整数列表`nums1`，创建一个新的列表`nums2`，将`nums1`中大于`50`的元素放到`nums2`中。
 
-```Python
+```python
 nums1 = [35, 12, 97, 64, 55]
 nums2 = []
 for num in nums1:
@@ -126,7 +126,7 @@ print(nums2)
 
 使用列表生成式做同样的事情，代码如下所示。
 
-```Python
+```python
 nums1 = [35, 12, 97, 64, 55]
 nums2 = [num for num in nums1 if num > 50]
 print(nums2)
@@ -148,7 +148,7 @@ print(scores[0][1])
 
 如果想通过键盘输入的方式来录入5个学生3门课程的成绩并保存在列表中，可以使用如下所示的代码。
 
-```Python
+```python
 scores = []
 for _ in range(5):
     temp = []
diff --git "a/Day01-20/11.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\255\227\347\254\246\344\270\262.md" "b/Day01-20/11.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\255\227\347\254\246\344\270\262.md"
index 2c5c38639..d71877425 100755
--- "a/Day01-20/11.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\255\227\347\254\246\344\270\262.md"
+++ "b/Day01-20/11.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\255\227\347\254\246\344\270\262.md"
@@ -130,7 +130,7 @@ print(len('goodbye, world'))  # 14
 
 #### 索引和切片
 
-字符串的索引和切片操作跟列表几乎区别，因为字符串也是一种有序序列，可以通过正向或反向的整数索引访问其中的元素。但是有一点需要注意，因为**字符串是不可变类型**，所以**不能通过索引运算修改字符串中的字符**。
+字符串的索引和切片操作跟列表、元组几乎没有区别，因为字符串也是一种有序序列，可以通过正向或反向的整数索引访问其中的元素。但是有一点需要注意，因为**字符串是不可变类型**，所以**不能通过索引运算修改字符串中的字符**。
 
 ```python
 s = 'abc123456'
diff --git "a/Day01-20/12.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\351\233\206\345\220\210.md" "b/Day01-20/12.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\351\233\206\345\220\210.md"
index 967b5ef77..a4b745806 100755
--- "a/Day01-20/12.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\351\233\206\345\220\210.md"
+++ "b/Day01-20/12.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\351\233\206\345\220\210.md"
@@ -1,6 +1,6 @@
 ## 常用数据结构之集合
 
-在学习了列表和元组之后，我们再来学习一种容器型的数据类型，它的名字叫集合（set）。说到集合这个词大家一定不会陌生，在数学课本上就有这个概念。如果我们**把一定范围的、确定的、可以区别的事物当作一个整体来看待**，那么这个整体就是集合，集合中的各个事物称为集合的**元素**。通常，集合需要满足以下特性：
+在学习了列表和元组之后，我们再来学习一种容器型的数据类型，它的名字叫集合（set）。说到集合这个词大家一定不会陌生，在数学课本上就有这个概念。如果我们**把一定范围的、确定的、可以区别的事物当作一个整体来看待**，那么这个整体就是集合，集合中的各个事物称为集合的**元素**。通常，集合需要满足以下要求：
 
 1. **无序性**：一个集合中，每个元素的地位都是相同的，元素之间是无序的。
 2. **互异性**：一个集合中，任何两个元素都是不相同的，即元素在集合中只能出现一次。
@@ -8,7 +8,7 @@
 
 Python 程序中的集合跟数学上的集合没有什么本质区别，需要强调的是上面所说的无序性和互异性。无序性说明集合中的元素并不像列中的元素那样存在某种次序，可以通过索引运算就能访问任意元素，**集合并不支持索引运算**。另外，集合的互异性决定了**集合中不能有重复元素**，这一点也是集合区别于列表的地方，我们无法将重复的元素添加到一个集合中。集合类型必然是支持`in`和`not in`成员运算的，这样就可以确定一个元素是否属于集合，也就是上面所说的集合的确定性。**集合的成员运算在性能上要优于列表的成员运算**，这是集合的底层存储特性决定的，此处我们暂时不做讨论，大家记住这个结论即可。
 
-> **说明**：集合底层使用了哈希存储（散列存储），对哈希存储感兴趣的读者可以看看维基百科上“散列表”这个词条。
+> **说明**：集合底层使用了哈希存储（散列存储），对哈希存储不了解的读者可以先看看“Hello 算法”网站对[哈希表](https://www.hello-algo.com/chapter_hashing/)的讲解，感谢作者的开源精神。
 
 ### 创建集合
 
@@ -31,7 +31,9 @@ set5 = {num for num in range(1, 20) if num % 3 == 0 or num % 7 == 0}
 print(set5)
 ```
 
-需要提醒大家，集合中的元素必须是`hashable`类型，使用哈希存储的容器都会对元素提出这一要求。所谓`hashable`类型指的是能够计算出哈希码的数据类型，通常不可变类型都是`hashable`类型，如整数（`int`）、浮点小数（`float`）、布尔值（`bool`）、字符串（`str`）、元组（`tuple`）等。可变类型都不是`hashable`类型，因为可变类型无法计算出确定的哈希码，所以它们不能放到集合中。例如：我们不能将列表作为集合中的元素；同理，由于集合本身也是可变类型，所以集合也不能作为集合中的元素。我们可以创建出嵌套的列表，但是我们不能创建出嵌套的集合，这一点在使用集合的时候一定要引起注意。
+需要提醒大家，集合中的元素必须是`hashable`类型，所谓`hashable`类型指的是能够计算出哈希码的数据类型，通常不可变类型都是`hashable`类型，如整数（`int`）、浮点小数（`float`）、布尔值（`bool`）、字符串（`str`）、元组（`tuple`）等。可变类型都不是`hashable`类型，因为可变类型无法计算出确定的哈希码，所以它们不能放到集合中。例如：我们不能将列表作为集合中的元素；同理，由于集合本身也是可变类型，所以集合也不能作为集合中的元素。我们可以创建出嵌套列表（列表的元素也是列表），但是我们不能创建出嵌套的集合，这一点在使用集合的时候一定要引起注意。
+
+> **温馨提示**：如果不理解上面提到的哈希码、哈希存储这些概念，可以先放放，因为它并不影响你继续学习和使用 Python 语言。当然，如果是计算机专业的小伙伴，不理解哈希存储是很难被原谅的，要赶紧去补课了。
 
 ### 元素的遍历
 
@@ -47,7 +49,7 @@ for elem in set1:
 
 ### 集合的运算
 
-Python 为集合类型提供了非常丰富的运算符，主要包括：成员运算、交集运算、并集运算、差集运算、比较运算（相等性、子集、超集）等。
+Python 为集合类型提供了非常丰富的运算，主要包括：成员运算、交集运算、并集运算、差集运算、比较运算（相等性、子集、超集）等。
 
 #### 成员运算
 
@@ -130,7 +132,7 @@ print(set2.issuperset(set1))  # True
 
 ### 集合的方法
 
-刚才我们说过，Python 中的集合是可变类型，我们可以通过集合类型的方法向集合添加元素或从集合中删除元素。
+刚才我们说过，Python 中的集合是可变类型，我们可以通过集合的方法向集合添加元素或从集合中删除元素。
 
 ```python
 set1 = {1, 10, 100}
@@ -151,7 +153,7 @@ set1.clear()
 print(set1)  # set()
 ```
 
-> **说明**：删除集合元素的`remove`方法在元素不存在时会引发`KeyError`错误，所以上面的代码中我们先通过成员运算判断元素是否在集合中。集合类型还有一个`pop`方法可以从集合中随机删除一个元素，该方法在删除元素的同时会获得被删除的元素，而`remove`和`discard`方法仅仅是删除元素，不会获得被删除的元素。
+> **说明**：删除元素的`remove`方法在元素不存在时会引发`KeyError`错误，所以上面的代码中我们先通过成员运算判断元素是否在集合中。集合类型还有一个`pop`方法可以从集合中随机删除一个元素，该方法在删除元素的同时会返回（获得）被删除的元素，而`remove`和`discard`方法仅仅是删除元素，不会返回（获得）被删除的元素。
 
 集合类型还有一个名为`isdisjoint`的方法可以判断两个集合有没有相同的元素，如果没有相同元素，该方法返回`True`，否则该方法返回`False`，代码如下所示。
 
diff --git "a/Day01-20/13.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\255\227\345\205\270.md" "b/Day01-20/13.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\255\227\345\205\270.md"
index e548323a9..7d5849f67 100755
--- "a/Day01-20/13.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\255\227\345\205\270.md"
+++ "b/Day01-20/13.\345\270\270\347\224\250\346\225\260\346\215\256\347\273\223\346\236\204\344\271\213\345\255\227\345\205\270.md"
@@ -1,8 +1,8 @@
 ## 常用数据结构之字典
 
-迄今为止，我们已经为大家介绍了Python中的三种容器型数据类型（列表、元组、集合），但是这些数据类型仍然不足以帮助我们解决所有的问题。例如，我们需要一个变量来保存一个人的多项信息，包括：姓名、年龄、身高、体重、家庭住址、本人手机号、紧急联系人手机号，此时你会发现，我们之前学过的列表、元组和集合类型都不够好使。
+迄今为止，我们已经为大家介绍了 Python 中的三种容器型数据类型（列表、元组、集合），但是这些数据类型仍然不足以帮助我们解决所有的问题。例如，我们需要一个变量来保存一个人的多项信息，包括：姓名、年龄、身高、体重、家庭住址、本人手机号、紧急联系人手机号，此时你会发现，我们之前学过的列表、元组和集合类型都不够好使。
 
-```Python
+```python
 person1 = ['王大锤', 55, 168, 60, '成都市武侯区科华北路62号1栋101', '13122334455', '13800998877']
 person2 = ('王大锤', 55, 168, 60, '成都市武侯区科华北路62号1栋101', '13122334455', '13800998877')
 person3 = {'王大锤', 55, 168, 60, '成都市武侯区科华北路62号1栋101', '13122334455', '13800998877'}
@@ -20,7 +20,7 @@ Python 程序中的字典跟现实生活中的字典很像，它以键值对（
 
 Python 中创建字典可以使用`{}`字面量语法，这一点跟上一节课讲的集合是一样的。但是字典的`{}`中的元素是以键值对的形式存在的，每个元素由`:`分隔的两个值构成，`:`前面是键，`:`后面是值，代码如下所示。
 
-```Python
+```python
 xinhua = {
     '麓': '山脚下',
     '路': '道，往来通行的地方；方面，地区：南～货，外～货；种类：他俩是一～人',
@@ -44,7 +44,7 @@ print(person)
 
 当然，如果愿意，我们也可以使用内置函数`dict`或者是字典的生成式语法来创建字典，代码如下所示。
 
-```Python
+```python
 # dict函数(构造器)中的每一组参数就是字典中的一组键值对
 person = dict(name='王大锤', age=55, height=168, weight=60, addr='成都市武侯区科华北路62号1栋101')
 print(person)  # {'name': '王大锤', 'age': 55, 'height': 168, 'weight': 60, 'addr': '成都市武侯区科华北路62号1栋101'}
@@ -60,10 +60,16 @@ items3 = {x: x ** 3 for x in range(1, 6)}
 print(items3)  # {1: 1, 2: 8, 3: 27, 4: 64, 5: 125}
 ```
 
-想知道字典中一共有多少组键值对，仍然是使用`len`函数；如果想对字典进行遍历，可以用`for`循环，但是需要注意，`for`循环只是对字典的键进行了遍历，不过没关系，在学习了字典的索引运算后，我们可以通过字典的键获取到和这个键对应的值。
+想知道字典中一共有多少组键值对，仍然是使用`len`函数；如果想对字典进行遍历，可以用`for`循环，但是需要注意，`for`循环只是对字典的键进行了遍历，不过没关系，在学习了字典的索引运算后，我们可以通过字典的键访问它对应的值。
 
-```Python
-person = {'name': '王大锤', 'age': 55, 'height': 168, 'weight': 60, 'addr': '成都市武侯区科华北路62号1栋101'}
+```python
+person = {
+    'name': '王大锤',
+    'age': 55,
+    'height': 168,
+    'weight': 60,
+    'addr': '成都市武侯区科华北路62号1栋101'
+}
 print(len(person))  # 5
 for key in person:
     print(key)
@@ -71,9 +77,30 @@ for key in person:
 
 ### 字典的运算
 
-对于字典类型来说，成员运算和索引运算肯定是很重要的，前者可以判定指定的键在不在字典中，后者可以通过键获取对应的值或者向字典中添加新的键值对。值得注意的是，字典的索引不同于列表的索引，列表中的元素因为有属于自己有序号，所以列表的索引是一个整数；字典中因为保存的是键值对，所以字典需要用键去索引对应的值。需要**特别提醒**大家注意的是，**字典中的键必须是不可变类型**，例如整数（`int`）、浮点数（`float`）、字符串（`str`）、元组（`tuple`）等类型，这一点跟集合类型对元素的要求是一样的；很显然，之前我们讲的列表（`list`）和集合（`set`）不能作为字典中的键，字典类型本身也不能再作为字典中的键，因为字典也是可变类型，但是字典可以作为字典中的值。大家可以先看看下面的代码，了解一下字典的成员运算和索引运算。
+对于字典类型来说，成员运算和索引运算肯定是很重要的，前者可以判定指定的键在不在字典中，后者可以通过键访问对应的值或者向字典中添加新的键值对。值得注意的是，字典的索引不同于列表的索引，列表中的元素因为有属于自己有序号，所以列表的索引是一个整数；字典中因为保存的是键值对，所以字典需要用键去索引对应的值。需要**特别提醒**大家注意的是，**字典中的键必须是不可变类型**，例如整数（`int`）、浮点数（`float`）、字符串（`str`）、元组（`tuple`）等类型，这一点跟集合类型对元素的要求是一样的；很显然，之前我们讲的列表（`list`）和集合（`set`）不能作为字典中的键，字典类型本身也不能再作为字典中的键，因为字典也是可变类型，但是列表、集合、字典都可以作为字典中的值，例如：
 
-```Python
+```python
+person = {
+    'name': '王大锤',
+    'age': 55,
+    'height': 168,
+    'weight': 60,
+    'addr': ['成都市武侯区科华北路62号1栋101', '北京市西城区百万庄大街1号'],
+    'car': {
+        'brand': 'BMW X7',
+        'maxSpeed': '250',
+        'length': 5170,
+        'width': 2000,
+        'height': 1835,
+        'displacement': 3.0
+    }
+}
+print(person)
+```
+
+大家可以看看下面的代码，了解一下字典的成员运算和索引运算。
+
+```python
 person = {'name': '王大锤', 'age': 55, 'height': 168, 'weight': 60, 'addr': '成都市武侯区科华北路62号1栋101'}
 
 # 成员运算
@@ -118,7 +145,7 @@ for key, value in person.items():
     print(f'{key}:\t{value}')
 ```
 
-字典的`update`方法会用一个字典更新另一个字典中的键值对。例如，有两个字典`x`和`y`，当执行`x.update(y)`操作时，`x`跟`y`相同的键对应的值会`y`中的值被更新，而`y`中有但`x`中没有的键值对会直接添加到`x`中，代码如下所示。
+字典的`update`方法实现两个字典的合并操作。例如，有两个字典`x`和`y`，当执行`x.update(y)`操作时，`x`跟`y`相同的键对应的值会被`y`中的值更新，而`y`中有但`x`中没有的键值对会直接添加到`x`中，代码如下所示。
 
 ```python
 person1 = {'name': '王大锤', 'age': 55, 'height': 178}
@@ -127,7 +154,16 @@ person1.update(person2)
 print(person1)  # {'name': '王大锤', 'age': 25, 'height': 178, 'addr': '成都市武侯区科华北路62号1栋101'}
 ```
 
-可以通过`pop`或`popitem`方法从字典中删除元素，前者会返回键对应的值，但是如果字典中不存在指定的键，会引发`KeyError`错误；后者在删除元素时，会返回键和值组成的二元组。字典的`clear`方法会清空字典中所有的键值对，代码如下所示。
+如果使用 Python 3.9 及以上的版本，也可以使用`|`运算符来完成同样的操作，代码如下所示。
+
+```python
+person1 = {'name': '王大锤', 'age': 55, 'height': 178}
+person2 = {'age': 25, 'addr': '成都市武侯区科华北路62号1栋101'}
+person1 |= person2
+print(person1)  # {'name': '王大锤', 'age': 25, 'height': 178, 'addr': '成都市武侯区科华北路62号1栋101'}
+```
+
+可以通过`pop`或`popitem`方法从字典中删除元素，前者会返回（获得）键对应的值，但是如果字典中不存在指定的键，会引发`KeyError`错误；后者在删除元素时，会返回（获得）键和值组成的二元组。字典的`clear`方法会清空字典中所有的键值对，代码如下所示。
 
 ```python
 person = {'name': '王大锤', 'age': 25, 'height': 178, 'addr': '成都市武侯区科华北路62号1栋101'}
@@ -141,7 +177,7 @@ print(person)             # {}
 
 跟列表一样，从字典中删除元素也可以使用`del`关键字，在删除元素的时候如果指定的键索引不到对应的值，一样会引发`KeyError`错误，具体的做法如下所示。
 
-```Python
+```python
 person = {'name': '王大锤', 'age': 25, 'height': 178, 'addr': '成都市武侯区科华北路62号1栋101'}
 del person['age']
 del person['addr']
@@ -154,7 +190,7 @@ print(person)  # {'name': '王大锤', 'height': 178}
 
 **例子1**：输入一段话，统计每个英文字母出现的次数，按出现次数从高到低输出。
 
-```Python
+```python
 sentence = input('请输入一段话: ')
 counter = {}
 for ch in sentence:
@@ -204,7 +240,7 @@ x 出现了 1 次.
 
 > **说明**：可以用字典的生成式语法来创建这个新字典。
 
-```Python
+```python
 stocks = {
     'AAPL': 191.88,
     'GOOG': 1186.96,
@@ -226,4 +262,4 @@ print(stocks2)
 
 ### 总结
 
-Python 程序中的字典跟现实生活中字典非常像，允许我们**以键值对的形式保存数据**，再**通过键索引对应的值**。这是一种非常**有利于数据检索**的数据类型。再次提醒大家注意，**字典中的键必须是不可变类型**，字典中的值可以是任意类型。
+Python 程序中的字典跟现实生活中字典非常像，允许我们**以键值对的形式保存数据**，再**通过键访问对应的值**。字典是一种非常**有利于数据检索**的数据类型，但是需要再次提醒大家，**字典中的键必须是不可变类型**，列表、集合、字典等类型的数据都不能作为字典的键。
diff --git "a/Day01-20/16.\345\207\275\346\225\260\344\275\277\347\224\250\350\277\233\351\230\266.md" "b/Day01-20/16.\345\207\275\346\225\260\344\275\277\347\224\250\350\277\233\351\230\266.md"
index 66fb21438..692144d66 100755
--- "a/Day01-20/16.\345\207\275\346\225\260\344\275\277\347\224\250\350\277\233\351\230\266.md"
+++ "b/Day01-20/16.\345\207\275\346\225\260\344\275\277\347\224\250\350\277\233\351\230\266.md"
@@ -6,7 +6,7 @@
 
 我们回到之前讲过的一个例子，设计一个函数，传入任意多个参数，对其中`int`类型或`float`类型的元素实现求和操作。我们对之前的代码稍作调整，让整个代码更加紧凑一些，如下所示。
 
-```Python
+```python
 def calc(*args, **kwargs):
     items = list(args) + list(kwargs.values())
     result = 0
@@ -18,7 +18,7 @@ def calc(*args, **kwargs):
 
 如果我们希望上面的`calc`函数不仅仅可以做多个参数的求和，还可以实现更多的甚至是自定义的二元运算，我们该怎么做呢？上面的代码只能求和是因为函数中使用了`+=`运算符，这使得函数跟加法运算形成了耦合关系，如果能解除这种耦合关系，函数的通用性和灵活性就会更好。解除耦合的办法就是将`+`运算符变成函数调用，并将其设计为函数的参数，代码如下所示。
 
-```Python
+```python
 def calc(init_value, op_func, *args, **kwargs):
     items = list(args) + list(kwargs.values())
     result = init_value
@@ -55,7 +55,7 @@ print(calc(1, mul, 1, 2, 3, 4, 5))  # 120
 
 如果我们没有提前定义好`add`和`mul`函数，也可以使用 Python 标准库中的`operator`模块提供的`add`和`mul`函数，它们分别代表了做加法和做乘法的二元运算，我们拿过来直接使用即可，代码如下所示。
 
-```Python
+```python
 import operator
 
 print(calc(0, operator.add, 1, 2, 3, 4, 5))  # 15
@@ -64,7 +64,7 @@ print(calc(1, operator.mul, 1, 2, 3, 4, 5))  # 120
 
 Python 内置函数中有不少高阶函数，我们前面提到过的`filter`和`map`函数就是高阶函数，前者可以实现对序列中元素的过滤，后者可以实现对序列中元素的映射，例如我们要去掉一个整数列表中的奇数，并对所有的偶数求平方得到一个新的列表，就可以直接使用这两个函数来做到，具体的做法是如下所示。
 
-```Python
+```python
 def is_even(num):
     """判断num是不是偶数"""
     return num % 2 == 0
@@ -82,7 +82,7 @@ print(new_nums)  # [144, 64, 3600, 2704]
 
 当然，要完成上面代码的功能，也可以使用列表生成式，列表生成式的做法更为简单优雅。
 
-```Python
+```python
 old_nums = [35, 12, 8, 99, 60, 52]
 new_nums = [num ** 2 for num in old_nums if num % 2 == 0]
 print(new_nums)  # [144, 64, 3600, 2704]
@@ -110,7 +110,7 @@ print(new_strings)  # ['in', 'zoo', 'pear', 'apple', 'waxberry']
 
 在使用高阶函数的时候，如果作为参数或者返回值的函数本身非常简单，一行代码就能够完成，也不需要考虑对函数的复用，那么我们可以使用 lambda 函数。Python 中的 lambda 函数是没有的名字函数，所以很多人也把它叫做**匿名函数**，lambda 函数只能有一行代码，代码中的表达式产生的运算结果就是这个匿名函数的返回值。之前的代码中，我们写的`is_even`和`square`函数都只有一行代码，我们可以考虑用 lambda 函数来替换掉它们，代码如下所示。
 
-```Python
+```python
 old_nums = [35, 12, 8, 99, 60, 52]
 new_nums = list(map(lambda x: x ** 2, filter(lambda x: x % 2 == 0, old_nums)))
 print(new_nums)  # [144, 64, 3600, 2704]
@@ -120,7 +120,7 @@ print(new_nums)  # [144, 64, 3600, 2704]
 
 前面我们说过，Python 中的函数是“一等函数”，函数是可以直接赋值给变量的。在学习了 lambda 函数之后，前面我们写过的一些函数就可以用一行代码来实现它们了，大家可以看看能否理解下面的求阶乘和判断素数的函数。
 
-```Python
+```python
 import functools
 import operator
 
diff --git "a/Day01-20/17.\345\207\275\346\225\260\351\253\230\347\272\247\345\272\224\347\224\250.md" "b/Day01-20/17.\345\207\275\346\225\260\351\253\230\347\272\247\345\272\224\347\224\250.md"
index 6abd0f50b..0a5fbcbb7 100755
--- "a/Day01-20/17.\345\207\275\346\225\260\351\253\230\347\272\247\345\272\224\347\224\250.md"
+++ "b/Day01-20/17.\345\207\275\346\225\260\351\253\230\347\272\247\345\272\224\347\224\250.md"
@@ -6,7 +6,7 @@
 
 Python 语言中，装饰器是“**用一个函数装饰另外一个函数并为其提供额外的能力**”的语法现象。装饰器本身是一个函数，它的参数是被装饰的函数，它的返回值是一个带有装饰功能的函数。通过前面的描述，相信大家已经听出来了，装饰器是一个高阶函数，它的参数和返回值都是函数。但是，装饰器的概念对编程语言的初学者来说，还是让人头疼的，下面我们先通过一个简单的例子来说明装饰器的作用。假设有名为`downlaod`和`upload`的两个函数，分别用于文件的上传和下载，如下所示。
 
-```Python
+```python
 import random
 import time
 
@@ -33,7 +33,7 @@ upload('Python从入门到住院.pdf')
 
 现在有一个新的需求，我们希望知道调用`download`和`upload`函数上传下载文件到底用了多少时间，这应该如何实现呢？相信很多小伙伴已经想到了，我们可以在函数开始执行的时候记录一个时间，在函数调用结束后记录一个时间，两个时间相减就可以计算出下载或上传的时间，代码如下所示。
 
-```Python
+```python
 start = time.time()
 download('MySQL从删库到跑路.avi')
 end = time.time()
@@ -62,7 +62,7 @@ def record_time(func):
 
 看懂这个结构后，我们就可以把记录时间的功能写到这个装饰器中，代码如下所示。
 
-```Python
+```python
 import time
 
 
@@ -85,7 +85,7 @@ def record_time(func):
 
 写装饰器虽然颇费周折，但是这是个一劳永逸的骚操作，将来再有记录函数执行时间的需求时，我们只需要添加上面的装饰器即可。使用上面的装饰器函数有两种方式，第一种方式就是直接调用装饰器函数，传入被装饰的函数并获得返回值，我们可以用这个返回值直接替代原来的函数，那么在调用时就已经获得了装饰器提供的额外的能力（记录执行时间），大家试试下面的代码就明白了。
 
-```Python
+```python
 download = record_time(download)
 upload = record_time(upload)
 download('MySQL从删库到跑路.avi')
@@ -94,7 +94,7 @@ upload('Python从入门到住院.pdf')
 
 在 Python 中，使用装饰器很有更为便捷的**语法糖**（编程语言中添加的某种语法，这种语法对语言的功能没有影响，但是使用更加方法，代码的可读性也更强，我们将其称之为“语法糖”或“糖衣语法”），可以用`@装饰器函数`将装饰器函数直接放在被装饰的函数上，效果跟上面的代码相同。我们把完整的代码为大家罗列出来，大家可以再看看我们是如何定义和使用装饰器的。
 
-```Python
+```python
 import random
 import time
 
@@ -133,7 +133,7 @@ upload('Python从入门到住院.pdf')
 
 如果在代码的某些地方，我们想去掉装饰器的作用执行原函数，那么在定义装饰器函数的时候，需要做一点点额外的工作。Python 标准库`functools`模块的`wraps`函数也是一个装饰器，我们将它放在`wrapper`函数上，这个装饰器可以帮我们保留被装饰之前的函数，这样在需要取消装饰器时，可以通过被装饰函数的`__wrapped__`属性获得被装饰之前的函数。
 
-```Python
+```python
 import random
 import time
 
@@ -181,7 +181,7 @@ upload.__wrapped__('Python从新手到大师.pdf')
 
 Python 中允许函数嵌套定义，也允许函数之间相互调用，而且一个函数还可以直接或间接的调用自身。函数自己调用自己称为递归调用，那么递归调用有什么用处呢？现实中，有很多问题的定义本身就是一个递归定义，例如我们之前讲到的阶乘，非负整数`N`的阶乘是`N`乘以`N-1`的阶乘，即 $\small{N! = N \times (N-1)!}$ ，定义的左边和右边都出现了阶乘的概念，所以这是一个递归定义。既然如此，我们可以使用递归调用的方式来写一个求阶乘的函数，代码如下所示。
 
-```Python
+```python
 def fac(num):
     if num in (0, 1):
         return 1
@@ -190,7 +190,7 @@ def fac(num):
 
 上面的代码中，`fac`函数中又调用了`fac`函数，这就是所谓的递归调用。代码第2行的`if`条件叫做递归的收敛条件，简单的说就是什么时候要结束函数的递归调用，在计算阶乘时，如果计算到`0`或`1`的阶乘，就停止递归调用，直接返回`1`；代码第4行的`num * fac(num - 1)`是递归公式，也就是阶乘的递归定义。下面，我们简单的分析下，如果用`fac(5)`计算`5`的阶乘，整个过程会是怎样的。
 
-```Python
+```python
 # 递归调用函数入栈
 # 5 * fac(4)
 # 5 * (4 * fac(3))
@@ -211,7 +211,7 @@ print(fac(5))    # 120
 
 再举一个之前讲过的生成斐波那契数列的例子，因为斐波那契数列前两个数都是`1`，从第三个数开始，每个数是前两个数相加的和，可以记为`f(n) = f(n - 1) + f(n - 2)`，很显然这又是一个递归的定义，所以我们可以用下面的递归调用函数来计算第​`n`个斐波那契数。
 
-```Python
+```python
 def fib1(n):
     if n in (1, 2):
         return 1
@@ -224,7 +224,7 @@ for i in range(1, 21):
 
 需要提醒大家，上面计算斐波那契数的代码虽然看起来非常简单明了，但执行性能是比较糟糕的。大家可以试一试，把上面代码`for`循环中`range`函数的第二个参数修改为`51`，即输出前50个斐波那契数，看看需要多长时间，也欢迎大家在评论区留下你的代码执行时间。至于为什么这么慢，大家可以自己思考一下原因。很显然，直接使用循环递推的方式获得斐波那契数列是更好的选择，代码如下所示。
 
-```Python
+```python
 def fib2(n):
     a, b = 0, 1
     for _ in range(n):
diff --git "a/Day01-20/18.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\345\205\245\351\227\250.md" "b/Day01-20/18.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\345\205\245\351\227\250.md"
index 59e9e7b28..365fa94a8 100755
--- "a/Day01-20/18.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\345\205\245\351\227\250.md"
+++ "b/Day01-20/18.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\345\205\245\351\227\250.md"
@@ -28,7 +28,7 @@
 
 在 Python 语言中，我们可以使用`class`关键字加上类名来定义类，通过缩进我们可以确定类的代码块，就如同定义函数那样。在类的代码块中，我们需要写一些函数，我们说过类是一个抽象概念，那么这些函数就是我们对一类对象共同的动态特征的提取。写在类里面的函数我们通常称之为**方法**，方法就是对象的行为，也就是对象可以接收的消息。方法的第一个参数通常都是`self`，它代表了接收这个消息的对象本身。
 
-```Python
+```python
 class Student:
 
     def study(self, course_name):
@@ -42,7 +42,7 @@ class Student:
 
 在我们定义好一个类之后，可以使用构造器语法来创建对象，代码如下所示。
 
-```Python
+```python
 stu1 = Student()
 stu2 = Student()
 print(stu1)    # <__main__.Student object at 0x10ad5ac50>
@@ -54,7 +54,7 @@ print(hex(id(stu1)), hex(id(stu2)))    # 0x10ad5ac50 0x10ad5acd0
 
 接下来，我们尝试给对象发消息，即调用对象的方法。刚才的`Student`类中我们定义了`study`和`play`两个方法，两个方法的第一个参数`self`代表了接收消息的学生对象，`study`方法的第二个参数是学习的课程名称。Python中，给对象发消息有两种方式，请看下面的代码。
 
-```Python
+```python
 # 通过“类.方法”调用方法
 # 第一个参数是接收消息的对象
 # 第二个参数是学习的课程名称
@@ -74,7 +74,7 @@ stu2.play()                             # 学生正在玩游戏.
 
 我们对上面的`Student`类稍作修改，给学生对象添加`name`（姓名）和`age`（年龄）两个属性。
 
-```Python
+```python
 class Student:
     """学生"""
 
@@ -94,7 +94,7 @@ class Student:
 
 修改刚才创建对象和给对象发消息的代码，重新执行一次，看看程序的执行结果有什么变化。
 
-```Python
+```python
 # 调用Student类的构造器创建对象并传入初始化参数
 stu1 = Student('骆昊', 44)
 stu2 = Student('王大锤', 25)
@@ -117,7 +117,7 @@ stu2.play()                    # 王大锤正在玩游戏.
 
 > **要求**：定义一个类描述数字时钟，提供走字和显示时间的功能。
 
-```Python
+```python
 import time
 
 
@@ -167,7 +167,7 @@ while True:
 
 >  **要求**：定义一个类描述平面上的点，提供计算到另一个点距离的方法。
 
-```Python
+```python
 class Point:
     """平面上的点"""
 
diff --git "a/Day01-20/19.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\350\277\233\351\230\266.md" "b/Day01-20/19.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\350\277\233\351\230\266.md"
index 7ba887df0..c6b540f03 100755
--- "a/Day01-20/19.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\350\277\233\351\230\266.md"
+++ "b/Day01-20/19.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\350\277\233\351\230\266.md"
@@ -6,7 +6,7 @@
 
 在很多面向对象编程语言中，对象的属性通常会被设置为私有（private）或受保护（protected）的成员，简单的说就是不允许直接访问这些属性；对象的方法通常都是公开的（public），因为公开的方法是对象能够接受的消息，也是对象暴露给外界的调用接口，这就是所谓的访问可见性。在 Python 中，可以通过给对象属性名添加前缀下划线的方式来说明属性的访问可见性，例如，可以用`__name`表示一个私有属性，`_name`表示一个受保护属性，代码如下所示。
 
-```Python
+```python
 class Student:
 
     def __init__(self, name, age):
@@ -30,7 +30,7 @@ Python 语言属于动态语言，维基百科对动态语言的解释是：“
 
 在 Python 中，我们可以动态为对象添加属性，这是 Python 作为动态类型语言的一项特权，代码如下所示。需要提醒大家的是，对象的方法其实本质上也是对象的属性，如果给对象发送一个无法接收的消息，引发的异常仍然是`AttributeError`。
 
-```Python
+```python
 class Student:
 
     def __init__(self, name, age):
@@ -44,7 +44,7 @@ stu.sex = '男'  # 给学生对象动态添加sex属性
 
 如果不希望在使用对象时动态的为对象添加属性，可以使用 Python 语言中的`__slots__`魔法。对于`Student`类来说，可以在类中指定`__slots__ = ('name', 'age')`，这样`Student`类的对象只能有`name`和`age`属性，如果想动态添加其他属性将会引发异常，代码如下所示。
 
-```Python
+```python
 class Student:
     __slots__ = ('name', 'age')
 
@@ -64,7 +64,7 @@ stu.sex = '男'
 
 举一个例子，定义一个三角形类，通过传入三条边的长度来构造三角形，并提供计算周长和面积的方法。计算周长和面积肯定是三角形对象的方法，这一点毫无疑问。但是在创建三角形对象时，传入的三条边长未必能构造出三角形，为此我们可以先写一个方法来验证给定的三条边长是否可以构成三角形，这种方法很显然就不是对象方法，因为在调用这个方法时三角形对象还没有创建出来。我们可以把这类方法设计为静态方法或类方法，也就是说这类方法不是发送给三角形对象的消息，而是发送给三角形类的消息，代码如下所示。
 
-```Python
+```python
 class Triangle(object):
     """三角形"""
 
@@ -134,7 +134,7 @@ print(f'面积: {t.area}')
 
 面向对象的编程语言支持在已有类的基础上创建新类，从而减少重复代码的编写。提供继承信息的类叫做父类（超类、基类），得到继承信息的类叫做子类（派生类、衍生类）。例如，我们定义一个学生类和一个老师类，我们会发现他们有大量的重复代码，而这些重复代码都是老师和学生作为人的公共属性和行为，所以在这种情况下，我们应该先定义人类，再通过继承，从人类派生出老师类和学生类，代码如下所示。
 
-```Python
+```python
 class Person:
     """人"""
 
diff --git "a/Day01-20/20.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\345\272\224\347\224\250.md" "b/Day01-20/20.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\345\272\224\347\224\250.md"
index 9fa08a51a..00fa3faae 100755
--- "a/Day01-20/20.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\345\272\224\347\224\250.md"
+++ "b/Day01-20/20.\351\235\242\345\220\221\345\257\271\350\261\241\347\274\226\347\250\213\345\272\224\347\224\250.md"
@@ -10,7 +10,7 @@
 
 牌的属性显而易见，有花色和点数。我们可以用 0 到 3 的四个数字来代表四种不同的花色，但是这样的代码可读性会非常糟糕，因为我们并不知道黑桃、红心、草花、方块跟 0 到 3 的数字的对应关系。如果一个变量的取值只有有限多个选项，我们可以使用枚举。与 C、Java 等语言不同的是，Python 中没有声明枚举类型的关键字，但是可以通过继承`enum`模块的`Enum`类来创建枚举类型，代码如下所示。
 
-```Python
+```python
 from enum import Enum
 
 
@@ -21,14 +21,14 @@ class Suite(Enum):
 
 通过上面的代码可以看出，定义枚举类型其实就是定义符号常量，如`SPADE`、`HEART`等。每个符号常量都有与之对应的值，这样表示黑桃就可以不用数字 0，而是用`Suite.SPADE`；同理，表示方块可以不用数字 3， 而是用`Suite.DIAMOND`。注意，使用符号常量肯定是优于使用字面常量的，因为能够读懂英文就能理解符号常量的含义，代码的可读性会提升很多。Python 中的枚举类型是可迭代类型，简单的说就是可以将枚举类型放到`for-in`循环中，依次取出每一个符号常量及其对应的值，如下所示。
 
-```Python
+```python
 for suite in Suite:
     print(f'{suite}: {suite.value}')
 ```
 
 接下来我们可以定义牌类。
 
-```Python
+```python
 class Card:
     """牌"""
 
@@ -44,7 +44,7 @@ class Card:
 
 可以通过下面的代码来测试下`Card`类。
 
-```Python
+```python
 card1 = Card(Suite.SPADE, 5)
 card2 = Card(Suite.HEART, 13)
 print(card1)  # ♠5 
@@ -53,7 +53,7 @@ print(card2)  # ♥K
 
 接下来我们定义扑克类。
 
-```Python
+```python
 import random
 
 
@@ -85,7 +85,7 @@ class Poker:
 
 可以通过下面的代码来测试下`Poker`类。
 
-```Python
+```python
 poker = Poker()
 print(poker.cards)  # 洗牌前的牌
 poker.shuffle()
@@ -94,7 +94,7 @@ print(poker.cards)  # 洗牌后的牌
 
 定义玩家类。
 
-```Python
+```python
 class Player:
     """玩家"""
 
@@ -113,7 +113,7 @@ class Player:
 
 创建四个玩家并将牌发到玩家的手上。
 
-```Python
+```python
 poker = Poker()
 poker.shuffle()
 players = [Player('东邪'), Player('西毒'), Player('南帝'), Player('北丐')]
@@ -134,7 +134,7 @@ for player in players:
 
 修改后的`Card`类代码如下所示。
 
-```Python
+```python
 class Card:
     """牌"""
 
@@ -161,7 +161,7 @@ class Card:
 
 通过对上述需求的分析，可以看出部门经理、程序员、销售员都是员工，有相同的属性和行为，那么我们可以先设计一个名为`Employee`的父类，再通过继承的方式从这个父类派生出部门经理、程序员和销售员三个子类。很显然，后续的代码不会创建`Employee` 类的对象，因为我们需要的是具体的员工对象，所以这个类可以设计成专门用于继承的抽象类。Python 语言中没有定义抽象类的关键字，但是可以通过`abc`模块中名为`ABCMeta` 的元类来定义抽象类。关于元类的概念此处不展开讲解，当然大家不用纠结，照做即可。
 
-```Python
+```python
 from abc import ABCMeta, abstractmethod
 
 
@@ -179,7 +179,7 @@ class Employee(metaclass=ABCMeta):
 
 在上面的员工类中，有一个名为`get_salary`的方法用于结算月薪，但是由于还没有确定是哪一类员工，所以结算月薪虽然是员工的公共行为但这里却没有办法实现。对于暂时无法实现的方法，我们可以使用`abstractmethod`装饰器将其声明为抽象方法，所谓**抽象方法就是只有声明没有实现的方法**，**声明这个方法是为了让子类去重写这个方法**。接下来的代码展示了如何从员工类派生出部门经理、程序员、销售员这三个子类以及子类如何重写父类的抽象方法。
 
-```Python
+```python
 class Manager(Employee):
     """部门经理"""
 
@@ -213,7 +213,7 @@ class Salesman(Employee):
 
 我们通过下面的代码来完成这个工资结算系统，由于程序员和销售员需要分别录入本月的工作时间和销售额，所以在下面的代码中我们使用了 Python 内置的`isinstance`函数来判断员工对象的类型。我们之前讲过的`type`函数也能识别对象的类型，但是`isinstance`函数更加强大，因为它可以判断出一个对象是不是某个继承结构下的子类型，你可以简单的理解为`type`函数是对对象类型的精准匹配，而`isinstance`函数是对对象类型的模糊匹配。
 
-```Python
+```python
 emps = [Manager('刘备'), Programmer('诸葛亮'), Manager('曹操'), Programmer('荀彧'), Salesman('张辽')]
 for emp in emps:
     if isinstance(emp, Programmer):
diff --git "a/Day21-30/21.\346\226\207\344\273\266\350\257\273\345\206\231\345\222\214\345\274\202\345\270\270\345\244\204\347\220\206.md" "b/Day21-30/21.\346\226\207\344\273\266\350\257\273\345\206\231\345\222\214\345\274\202\345\270\270\345\244\204\347\220\206.md"
index fb51ef340..4d46f941f 100755
--- "a/Day21-30/21.\346\226\207\344\273\266\350\257\273\345\206\231\345\222\214\345\274\202\345\270\270\345\244\204\347\220\206.md"
+++ "b/Day21-30/21.\346\226\207\344\273\266\350\257\273\345\206\231\345\222\214\345\274\202\345\270\270\345\244\204\347\220\206.md"
@@ -32,7 +32,7 @@
 
 下面的例子演示了如何读取一个纯文本文件（一般指只有字符原生编码构成的文件，与富文本相比，纯文本不包含字符样式的控制元素，能够被最简单的文本编辑器直接读取）。
 
-```Python
+```python
 file = open('致橡树.txt', 'r', encoding='utf-8')
 print(file.read())
 file.close()
@@ -42,7 +42,7 @@ file.close()
 
 除了使用文件对象的`read`方法读取文件之外，还可以使用`for-in`循环逐行读取或者用`readlines`方法将文件按行读取到一个列表容器中，代码如下所示。
 
-```Python
+```python
 file = open('致橡树.txt', 'r', encoding='utf-8')
 for line in file:
     print(line, end='')
@@ -57,7 +57,7 @@ file.close()
 
 如果要向文件中写入内容，可以在打开文件时使用`w`或者`a`作为操作模式，前者会截断之前的文本内容写入新的内容，后者是在原来内容的尾部追加新的内容。
 
-```Python
+```python
 file = open('致橡树.txt', 'a', encoding='utf-8')
 file.write('\n标题：《致橡树》')
 file.write('\n作者：舒婷')
@@ -69,7 +69,7 @@ file.close()
 
 请注意上面的代码，如果`open`函数指定的文件并不存在或者无法打开，那么将引发异常状况导致程序崩溃。为了让代码具有健壮性和容错性，我们可以**使用Python的异常机制对可能在运行时发生状况的代码进行适当的处理**。Python中和异常相关的关键字有五个，分别是`try`、`except`、`else`、`finally`和`raise`，我们先看看下面的代码，再来为大家介绍这些关键字的用法。
 
-```Python
+```python
 file = None
 try:
     file = open('致橡树.txt', 'r', encoding='utf-8')
@@ -160,7 +160,7 @@ BaseException
 
 在Python中，可以使用`raise`关键字来引发异常（抛出异常对象），而调用者可以通过`try...except...`结构来捕获并处理异常。例如在函数中，当函数的执行条件不满足时，可以使用抛出异常的方式来告知调用者问题的所在，而调用者可以通过捕获处理异常来使得代码从异常中恢复，定义异常和抛出异常的代码如下所示。
 
-```Python
+```python
 class InputError(ValueError):
     """自定义异常类型"""
     pass
@@ -177,7 +177,7 @@ def fac(num):
 
 调用求阶乘的函数`fac`，通过`try...except...`结构捕获输入错误的异常并打印异常对象（显示异常信息），如果输入正确就计算阶乘并结束程序。
 
-```Python
+```python
 flag = True
 while flag:
     num = int(input('n = '))
@@ -194,7 +194,7 @@ while flag:
 
 用`with`上下文语法改写后的代码如下所示。
 
-```Python
+```python
 try:
     with open('致橡树.txt', 'r', encoding='utf-8') as file:
         print(file.read())
@@ -210,7 +210,7 @@ except UnicodeDecodeError:
 
 读写二进制文件跟读写文本文件的操作类似，但是需要注意，在使用`open`函数打开文件时，如果要进行读操作，操作模式是`'rb'`，如果要进行写操作，操作模式是`'wb'`。还有一点，读写文本文件时，`read`方法的返回值以及`write`方法的参数是`str`对象（字符串），而读写二进制文件时，`read`方法的返回值以及`write`方法的参数是`bytes-like`对象（字节串）。下面的代码实现了将当前路径下名为`guido.jpg`的图片文件复制到`吉多.jpg`文件中的操作。
 
-```Python
+```python
 try:
     with open('guido.jpg', 'rb') as file1:
         data = file1.read()
@@ -225,7 +225,7 @@ print('程序执行结束.')
 
 如果要复制的图片文件很大，一次将文件内容直接读入内存中可能会造成非常大的内存开销，为了减少对内存的占用，可以为`read`方法传入`size`参数来指定每次读取的字节数，通过循环读取和写入的方式来完成上面的操作，代码如下所示。
 
-```Python
+```python
 try:
     with open('guido.jpg', 'rb') as file1, open('吉多.jpg', 'wb') as file2:
         data = file1.read(512)
diff --git "a/Day21-30/22.\345\257\271\350\261\241\347\232\204\345\272\217\345\210\227\345\214\226\345\222\214\345\217\215\345\272\217\345\210\227\345\214\226.md" "b/Day21-30/22.\345\257\271\350\261\241\347\232\204\345\272\217\345\210\227\345\214\226\345\222\214\345\217\215\345\272\217\345\210\227\345\214\226.md"
index 336547545..39658ec7b 100755
--- "a/Day21-30/22.\345\257\271\350\261\241\347\232\204\345\272\217\345\210\227\345\214\226\345\222\214\345\217\215\345\272\217\345\210\227\345\214\226.md"
+++ "b/Day21-30/22.\345\257\271\350\261\241\347\232\204\345\272\217\345\210\227\345\214\226\345\222\214\345\217\215\345\272\217\345\210\227\345\214\226.md"
@@ -67,7 +67,7 @@ let obj = {
 
 在Python中，如果要将字典处理成JSON格式（以字符串形式存在），可以使用`json`模块的`dumps`函数，代码如下所示。
 
-```Python
+```python
 import json
 
 my_dict = {
@@ -91,7 +91,7 @@ print(json.dumps(my_dict))
 
 如果要将字典处理成JSON格式并写入文本文件，只需要将`dumps`函数换成`dump`函数并传入文件对象即可，代码如下所示。
 
-```Python
+```python
 import json
 
 my_dict = {
@@ -121,7 +121,7 @@ with open('data.json', 'w') as file:
 
 我们可以通过下面的代码，读取上面创建的`data.json`文件，将JSON格式的数据还原成Python中的字典。
 
-```Python
+```python
 import json
 
 with open('data.json', 'r') as file:
@@ -215,7 +215,7 @@ pip install requests
 
 获取国内新闻并显示新闻标题和链接。
 
-```Python
+```python
 import requests
 
 resp = requests.get('http://api.tianapi.com/guonei/?key=APIKey&num=10')
diff --git "a/Day21-30/23.Python\350\257\273\345\206\231CSV\346\226\207\344\273\266.md" "b/Day21-30/23.Python\350\257\273\345\206\231CSV\346\226\207\344\273\266.md"
index d012482d3..ce639830c 100755
--- "a/Day21-30/23.Python\350\257\273\345\206\231CSV\346\226\207\344\273\266.md"
+++ "b/Day21-30/23.Python\350\257\273\345\206\231CSV\346\226\207\344\273\266.md"
@@ -17,7 +17,7 @@ CSV文件可以使用文本编辑器或类似于Excel电子表格这类工具打
 
 现有五个学生三门课程的考试成绩需要保存到一个CSV文件中，要达成这个目标，可以使用Python标准库中的`csv`模块，该模块的`writer`函数会返回一个`csvwriter`对象，通过该对象的`writerow`或`writerows`方法就可以将数据写入到CSV文件中，具体的代码如下所示。
 
-```Python
+```python
 import csv
 import random
 
@@ -44,7 +44,7 @@ with open('scores.csv', 'w') as file:
 
 需要说明的是上面的`writer`函数，除了传入要写入数据的文件对象外，还可以`dialect`参数，它表示CSV文件的方言，默认值是`excel`。除此之外，还可以通过`delimiter`、`quotechar`、`quoting`参数来指定分隔符（默认是逗号）、包围值的字符（默认是双引号）以及包围的方式。其中，包围值的字符主要用于当字段中有特殊符号时，通过添加包围值的字符可以避免二义性。大家可以尝试将上面第5行代码修改为下面的代码，然后查看生成的CSV文件。
 
-```Python
+```python
 writer = csv.writer(file, delimiter='|', quoting=csv.QUOTE_ALL)
 ```
 
@@ -63,7 +63,7 @@ writer = csv.writer(file, delimiter='|', quoting=csv.QUOTE_ALL)
 
 如果要读取刚才创建的CSV文件，可以使用下面的代码，通过`csv`模块的`reader`函数可以创建出`csvreader`对象，该对象是一个迭代器，可以通过`next`函数或`for-in`循环读取到文件中的数据。
 
-```Python
+```python
 import csv
 
 with open('scores.csv', 'r') as file:
diff --git "a/Day21-30/24.Python\350\257\273\345\206\231Excel\346\226\207\344\273\266-1.md" "b/Day21-30/24.Python\350\257\273\345\206\231Excel\346\226\207\344\273\266-1.md"
index 5030aae44..6ddbc4199 100755
--- "a/Day21-30/24.Python\350\257\273\345\206\231Excel\346\226\207\344\273\266-1.md"
+++ "b/Day21-30/24.Python\350\257\273\345\206\231Excel\346\226\207\344\273\266-1.md"
@@ -16,7 +16,7 @@ pip install xlwt xlrd xlutils
 
 例如在当前文件夹下有一个名为“阿里巴巴2020年股票数据.xls”的 Excel 文件，如果想读取并显示该文件的内容，可以通过如下所示的代码来完成。
 
-```Python
+```python
 import xlrd
 
 # 使用xlrd模块的open_workbook函数打开指定Excel文件并获得Book对象（工作簿）
@@ -65,7 +65,7 @@ print(sheet.row_slice(3, 0, 5))
 
 写入 Excel 文件可以通过`xlwt` 模块的`Workbook`类创建工作簿对象，通过工作簿对象的`add_sheet`方法可以添加工作表，通过工作表对象的`write`方法可以向指定单元格中写入数据，最后通过工作簿对象的`save`方法将工作簿写入到指定的文件或内存中。下面的代码实现了将5 个学生 3 门课程的考试成绩写入 Excel 文件的操作。
 
-```Python
+```python
 import random
 
 import xlwt
@@ -93,7 +93,7 @@ wb.save('考试成绩表.xls')
 
 在写Excel文件时，我们还可以为单元格设置样式，主要包括字体（Font）、对齐方式（Alignment）、边框（Border）和背景（Background）的设置，`xlwt`对这几项设置都封装了对应的类来支持。要设置单元格样式需要首先创建一个`XFStyle`对象，再通过该对象的属性对字体、对齐方式、边框等进行设定，例如在上面的例子中，如果希望将表头单元格的背景色修改为黄色，可以按照如下的方式进行操作。
 
-```Python
+```python
 header_style = xlwt.XFStyle()
 pattern = xlwt.Pattern()
 pattern.pattern = xlwt.Pattern.SOLID_PATTERN
@@ -107,7 +107,7 @@ for index, title in enumerate(titles):
 
 如果希望为表头设置指定的字体，可以使用`Font`类并添加如下所示的代码。
 
-```Python
+```python
 font = xlwt.Font()
 # 字体名称
 font.name = '华文楷体'
@@ -126,7 +126,7 @@ header_style.font = font
 
 如果希望表头垂直居中对齐，可以使用下面的代码进行设置。
 
-```Python
+```python
 align = xlwt.Alignment()
 # 垂直方向的对齐方式
 align.vert = xlwt.Alignment.VERT_CENTER
@@ -137,7 +137,7 @@ header_style.alignment = align
 
 如果希望给表头加上黄色的虚线边框，可以使用下面的代码来设置。
 
-```Python
+```python
 borders = xlwt.Borders()
 props = (
     ('top', 'top_colour'), ('right', 'right_colour'),
@@ -153,7 +153,7 @@ header_style.borders = borders
 
 如果要调整单元格的宽度（列宽）和表头的高度（行高），可以按照下面的代码进行操作。
 
-```Python
+```python
 # 设置行高为40px
 sheet.row(0).set_style(xlwt.easyxf(f'font:height {20 * 40}'))
 titles = ('姓名', '语文', '数学', '英语')
@@ -170,7 +170,7 @@ for index, title in enumerate(titles):
 
 实现公式计算的代码如下所示。
 
-```Python
+```python
 import xlrd
 import xlwt
 from xlutils.copy import copy
diff --git "a/Day21-30/25.Python\350\257\273\345\206\231Excel\346\226\207\344\273\266-2.md" "b/Day21-30/25.Python\350\257\273\345\206\231Excel\346\226\207\344\273\266-2.md"
index e9e1d506a..02580f116 100755
--- "a/Day21-30/25.Python\350\257\273\345\206\231Excel\346\226\207\344\273\266-2.md"
+++ "b/Day21-30/25.Python\350\257\273\345\206\231Excel\346\226\207\344\273\266-2.md"
@@ -16,7 +16,7 @@ pip install openpyxl
 
 例如在当前文件夹下有一个名为“阿里巴巴2020年股票数据.xlsx”的 Excel 文件，如果想读取并显示该文件的内容，可以通过如下所示的代码来完成。
 
-```Python
+```python
 import datetime
 
 import openpyxl
@@ -63,7 +63,7 @@ for row_ch in range(2, sheet.max_row + 1):
 
 下面我们使用`openpyxl`来进行写 Excel 操作。
 
-```Python
+```python
 import random
 
 import openpyxl
@@ -93,7 +93,7 @@ wb.save('考试成绩表.xlsx')
 
 在使用`openpyxl`操作 Excel 时，如果要调整单元格的样式，可以直接通过单元格对象（`Cell`对象）的属性进行操作。单元格对象的属性包括字体（`font`）、对齐（`alignment`）、边框（`border`）等，具体的可以参考`openpyxl`的[官方文档](https://openpyxl.readthedocs.io/en/stable/index.html)。在使用`openpyxl`时，如果需要做公式计算，可以完全按照 Excel 中的操作方式来进行，具体的代码如下所示。
 
-```Python
+```python
 import openpyxl
 from openpyxl.styles import Font, Alignment, Border, Side
 
@@ -129,7 +129,7 @@ wb.save('考试成绩表.xlsx')
 
 通过`openpyxl`库，可以直接向 Excel 中插入统计图表，具体的做法跟在 Excel 中插入图表大体一致。我们可以创建指定类型的图表对象，然后通过该对象的属性对图表进行设置。当然，最为重要的是为图表绑定数据，即横轴代表什么，纵轴代表什么，具体的数值是多少。最后，可以将图表对象添加到表单中，具体的代码如下所示。
 
-```Python
+```python
 from openpyxl import Workbook
 from openpyxl.chart import BarChart, Reference
 
@@ -175,7 +175,7 @@ wb.save('demo.xlsx')
 
 运行上面的代码，打开生成的 Excel 文件，效果如下图所示。
 
-<img src="res/20210819235009.png" alt="image-20210819235009026" width="75%">
+<img src="res/20210819235009.png"  width="75%">
 
 ###  总结
 
diff --git "a/Day21-30/26.Python\346\223\215\344\275\234Word\345\222\214PowerPoint\346\226\207\344\273\266.md" "b/Day21-30/26.Python\346\223\215\344\275\234Word\345\222\214PowerPoint\346\226\207\344\273\266.md"
index 3bbfaa7b6..3281ca8f4 100755
--- "a/Day21-30/26.Python\346\223\215\344\275\234Word\345\222\214PowerPoint\346\226\207\344\273\266.md"
+++ "b/Day21-30/26.Python\346\223\215\344\275\234Word\345\222\214PowerPoint\346\226\207\344\273\266.md"
@@ -12,7 +12,7 @@ pip install python-docx
 
 按照[官方文档](https://python-docx.readthedocs.io/en/latest/)的介绍，我们可以使用如下所示的代码来生成一个简单的 Word 文档。
 
-```Python
+```python
 from docx import Document
 from docx.shared import Cm, Pt
 
@@ -91,7 +91,7 @@ document.save('demo.docx')
 
 对于一个已经存在的 Word 文件，我们可以通过下面的代码去遍历它所有的段落并获取对应的内容。
 
-```Python
+```python
 from docx import Document
 from docx.document import Document as Doc
 
@@ -125,7 +125,7 @@ for no, p in enumerate(doc.paragraphs):
 
 接下来我们读取该文件，将占位符替换为真实信息，就可以生成一个新的 Word 文档，如下所示。
 
-```Python
+```python
 from docx import Document
 from docx.document import Document as Doc
 
@@ -194,7 +194,7 @@ pip install python-pptx
 
 用 Python 操作 PowerPoint 的内容，因为实际应用场景不算很多，我不打算在这里进行赘述，有兴趣的读者可以自行阅读`python-pptx`的[官方文档](https://python-pptx.readthedocs.io/en/latest/)，下面仅展示一段来自于官方文档的代码。
 
-```Python
+```python
 from pptx import Presentation
 
 # 创建幻灯片对象
diff --git "a/Day21-30/27.Python\346\223\215\344\275\234PDF\346\226\207\344\273\266.md" "b/Day21-30/27.Python\346\223\215\344\275\234PDF\346\226\207\344\273\266.md"
index e67661e98..00ed13520 100755
--- "a/Day21-30/27.Python\346\223\215\344\275\234PDF\346\226\207\344\273\266.md"
+++ "b/Day21-30/27.Python\346\223\215\344\275\234PDF\346\226\207\344\273\266.md"
@@ -12,7 +12,7 @@ pip install PyPDF2
 
 `PyPDF2`没有办法从 PDF 文档中提取图像、图表或其他媒体，但它可以提取文本，并将其返回为 Python 字符串。
 
-```Python
+```python
 import PyPDF2
 
 reader = PyPDF2.PdfReader('test.pdf')
@@ -35,7 +35,7 @@ pdf2text.py test.pdf
 
 上面的代码中通过创建`PdfFileReader`对象的方式来读取 PDF 文档，该对象的`getPage`方法可以获得PDF文档的指定页并得到一个`PageObject`对象，通过`PageObject`对象的`rotateClockwise`和`rotateCounterClockwise`方法可以实现页面的顺时针和逆时针方向旋转，通过`PageObject`对象的`addBlankPage`方法可以添加一个新的空白页，代码如下所示。
 
-```Python
+```python
 reader = PyPDF2.PdfReader('XGBoost.pdf')
 writer = PyPDF2.PdfWriter()
 
@@ -54,7 +54,7 @@ with open('temp.pdf', 'wb') as file_obj:
 
 使用`PyPDF2`中的`PdfFileWrite`对象可以为PDF文档加密，如果需要给一系列的PDF文档设置统一的访问口令，使用Python程序来处理就会非常的方便。
 
-```Python
+```python
 import PyPDF2
 
 reader = PyPDF2.PdfReader('XGBoost.pdf')
@@ -73,7 +73,7 @@ with open('temp.pdf', 'wb') as file_obj:
 
 上面提到的`PageObject`对象还有一个名为`mergePage`的方法，可以两个 PDF 页面进行叠加，通过这个操作，我们很容易实现给PDF文件添加水印的功能。例如要给上面的“XGBoost.pdf”文件添加一个水印，我们可以先准备好一个提供水印页面的 PDF 文件，然后将包含水印的`PageObject`读取出来，然后再循环遍历“XGBoost.pdf”文件的每个页，获取到`PageObject`对象，然后通过`mergePage`方法实现水印页和原始页的合并，代码如下所示。
 
-```Python
+```python
 reader1 = PyPDF2.PdfReader('XGBoost.pdf')
 reader2 = PyPDF2.PdfReader('watermark.pdf')
 writer = PyPDF2.PdfWriter()
@@ -99,7 +99,7 @@ pip install reportlab
 
 下面通过一个例子为大家展示`reportlab`的用法。
 
-```Python
+```python
 from reportlab.lib.pagesizes import A4
 from reportlab.pdfbase import pdfmetrics
 from reportlab.pdfbase.ttfonts import TTFont
diff --git "a/Day21-30/28.Python\345\244\204\347\220\206\345\233\276\345\203\217.md" "b/Day21-30/28.Python\345\244\204\347\220\206\345\233\276\345\203\217.md"
index 88fa12b33..bfb171ec7 100755
--- "a/Day21-30/28.Python\345\244\204\347\220\206\345\233\276\345\203\217.md"
+++ "b/Day21-30/28.Python\345\244\204\347\220\206\345\233\276\345\203\217.md"
@@ -25,7 +25,7 @@ Pillow 中最为重要的是`Image`类，可以通过`Image`模块的`open`函
 
 1. 读取和显示图像
 
-   ```Python
+   ```python
    from PIL import Image
    
    # 读取图像获得Image对象
@@ -44,7 +44,7 @@ Pillow 中最为重要的是`Image`类，可以通过`Image`模块的`open`函
 
 2. 剪裁图像
 
-   ```Python
+   ```python
    # 通过Image对象的crop方法指定剪裁区域剪裁图像
    image.crop((80, 20, 310, 360)).show()
    ```
@@ -53,7 +53,7 @@ Pillow 中最为重要的是`Image`类，可以通过`Image`模块的`open`函
 
 3. 生成缩略图
 
-   ```Python
+   ```python
    # 通过Image对象的thumbnail方法生成指定尺寸的缩略图
    image.thumbnail((128, 128))
    image.show()
@@ -63,7 +63,7 @@ Pillow 中最为重要的是`Image`类，可以通过`Image`模块的`open`函
 
 4. 缩放和黏贴图像
 
-   ```Python
+   ```python
    # 读取骆昊的照片获得Image对象
    luohao_image = Image.open('luohao.png')
    # 读取吉多的照片获得Image对象
@@ -81,7 +81,7 @@ Pillow 中最为重要的是`Image`类，可以通过`Image`模块的`open`函
 
 5. 旋转和翻转
 
-   ```Python
+   ```python
    image = Image.open('guido.jpg')
    # 使用Image对象的rotate方法实现图像的旋转
    image.rotate(45).show()
@@ -95,7 +95,7 @@ Pillow 中最为重要的是`Image`类，可以通过`Image`模块的`open`函
 
 6. 操作像素
 
-   ```Python
+   ```python
    for x in range(80, 310):
        for y in range(20, 360):
            # 通过Image对象的putpixel方法修改图像指定像素点
@@ -107,7 +107,7 @@ Pillow 中最为重要的是`Image`类，可以通过`Image`模块的`open`函
 
 7. 滤镜效果
 
-   ```Python
+   ```python
    from PIL import ImageFilter
    
    # 使用Image对象的filter方法对图像进行滤镜处理
@@ -125,7 +125,7 @@ Pillow 中有一个名为`ImageDraw`的模块，该模块的`Draw`函数会返
 
 要绘制如上图所示的图像，完整的代码如下所示。
 
-```Python
+```python
 import random
 
 from PIL import Image, ImageDraw, ImageFont
diff --git "a/Day21-30/29.Python\345\217\221\351\200\201\351\202\256\344\273\266\345\222\214\347\237\255\344\277\241.md" "b/Day21-30/29.Python\345\217\221\351\200\201\351\202\256\344\273\266\345\222\214\347\237\255\344\277\241.md"
index 7183ebdf0..f60598a69 100755
--- "a/Day21-30/29.Python\345\217\221\351\200\201\351\202\256\344\273\266\345\222\214\347\237\255\344\277\241.md"
+++ "b/Day21-30/29.Python\345\217\221\351\200\201\351\202\256\344\273\266\345\222\214\347\237\255\344\277\241.md"
@@ -16,7 +16,7 @@
 
 用手机扫码上面的二维码可以通过发送短信的方式来获取授权码，短信发送成功后，点击“我已发送”就可以获得授权码。授权码需要妥善保管，因为一旦泄露就会被其他人冒用你的身份来发送邮件。接下来，我们就可以编写发送邮件的代码了，如下所示。
 
-```Python
+```python
 import smtplib
 from email.header import Header
 from email.mime.multipart import MIMEMultipart
@@ -50,7 +50,7 @@ smtp_obj.sendmail(
 
 下面的代码演示了如何发送带附件的邮件。
 
-```Python
+```python
 import smtplib
 from email.header import Header
 from email.mime.multipart import MIMEMultipart
@@ -95,7 +95,7 @@ smtp_obj.sendmail(
 
 为了方便大家用 Python 实现邮件发送，我将上面的代码封装成了函数，使用的时候大家只需要调整邮件服务器域名、端口、用户名和授权码就可以了。
 
-```Python
+```python
 import smtplib
 from email.header import Header
 from email.mime.multipart import MIMEMultipart
@@ -151,7 +151,7 @@ def send_email(*, from_user, to_users, subject='', content='', filenames=[]):
 
 接下来，我们可以通过`requests`库向平台提供的短信网关发起一个 HTTP 请求，通过将接收短信的手机号和短信内容作为参数，就可以发送短信，代码如下所示。
 
-```Python
+```python
 import random
 
 import requests
diff --git "a/Day21-30/30.\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\347\232\204\345\272\224\347\224\250.md" "b/Day21-30/30.\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\347\232\204\345\272\224\347\224\250.md"
index b0d781b50..90a4d3ef1 100755
--- "a/Day21-30/30.\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\347\232\204\345\272\224\347\224\250.md"
+++ "b/Day21-30/30.\346\255\243\345\210\231\350\241\250\350\276\276\345\274\217\347\232\204\345\272\224\347\224\250.md"
@@ -72,7 +72,7 @@ Python 提供了`re`模块来支持正则表达式相关操作，下面是`re`
 
 #### 例子1：验证输入用户名和QQ号是否有效并给出对应的提示信息。
 
-```Python
+```python
 """
 要求：用户名必须由字母、数字或下划线构成且长度在6~20个字符之间，QQ号是5~12的数字且首位不能为0
 """
@@ -102,7 +102,7 @@ if m1 and m2:
 
 <img src="res/20210803203134.png" style="zoom:100%;">
 
-```Python
+```python
 import re
 
 # 创建正则表达式对象，使用了前瞻和回顾来保证手机号前后不应该再出现数字
@@ -131,7 +131,7 @@ while m:
 
 #### 例子3：替换字符串中的不良内容
 
-```Python
+```python
 import re
 
 sentence = 'Oh, shit! 你是傻逼吗? Fuck you.'
@@ -144,7 +144,7 @@ print(purified)  # Oh, *! 你是*吗? * you.
 
 #### 例子4：拆分长字符串
 
-```Python
+```python
 import re
 
 poem = '窗前明月光，疑是地上霜。举头望明月，低头思故乡。'
diff --git "a/Day66-80/66.\346\225\260\346\215\256\345\210\206\346\236\220\346\246\202\350\277\260.md" "b/Day66-80/66.\346\225\260\346\215\256\345\210\206\346\236\220\346\246\202\350\277\260.md"
index 7ea14f11f..4898beef0 100755
--- "a/Day66-80/66.\346\225\260\346\215\256\345\210\206\346\236\220\346\246\202\350\277\260.md"
+++ "b/Day66-80/66.\346\225\260\346\215\256\345\210\206\346\236\220\346\246\202\350\277\260.md"
@@ -1,14 +1,24 @@
 ## 数据分析概述
 
-当今世界对信息技术的依赖程度在不断加深，每天都会有大量的数据产生，我们经常会感到数据越来越多，但是要从中发现有价值的信息却越来越难。这里所说的信息，可以理解为对数据集处理之后的结果，是从数据集中提炼出的可用于其他场合的结论性的东西，而**从原始数据中抽取出有价值的信息**的这个过程我们就称之为**数据分析**，它是数据科学工作的一部分。
+当今世界，各行各业对信息技术的依赖程度在不断加深，每天都会有大量的数据产生，我们常常会感到数据越来越多，但是要从中发现有价值的信息却越来越难。这里所说的信息，可以理解为对数据集处理之后的结果，是从数据集中提炼出的可用于支撑和指导决策的东西，而**从原始数据中抽取出有价值的信息**的这个过程我们就称之为**数据分析**，它是数据科学的重要组成部分。
 
 <img src="res/data_science.png" style="zoom:50%;">
 
-> 定义：**数据分析是有针对性的收集、加工、整理数据并采用统计、挖掘等技术对数据进行探索、分析、呈现和解释的科学**。
+> **定义1**：数据分析是有针对性的收集、加工、整理数据并采用统计、挖掘等技术对数据进行探索、分析、呈现和解释的科学。
+>
+> **定义2**：数据分析是通过收集、整理和分析数据，从中提取有价值的信息和洞察，以支持决策和优化过程的活动。（GPT-4o）
+>
+> **定义3**：数据分析是通过系统性的收集、整理、处理、检验和解释数据，从中提取有价值的信息、形成结论并支持决策的过程，其核心是利用统计、算法和逻辑方法揭示数据背后的规律、趋势或关联。（DeepSeek）
 
-### 数据分析师的职责和技能栈
+对于想从事数据分析工作的人来说，需要掌握两个部分的技能，一是“数据思维”，二是“分析工具”，如下图所示。
 
-HR在发布招聘需求时，通常将数据工程、数据分析、数据挖掘等岗位都统称为数据分析岗位，但是根据工作性质的不同，又可以分为偏工程的**数据治理方向**、偏业务的**数据分析方向**、偏算法的**数据挖掘方向**、偏开发的**数据开发方向**、偏产品的**数据产品经理**。我们通常所说的数据分析师主要是指**业务数据分析师**，很多数据分析师的职业生涯都是从这个岗位开始的，而且这个岗位也是招聘数量最多的岗位。业务数据分析师在公司通常不属于研发部门而属于运营部门，所以这个岗位也称为**数据运营**或**商业分析**，这类人员通常也被称为“BI工程师”。通常招聘信息对这个岗位的描述（JD）是：
+![](res/contents_of_data_analysis.png)
+
+上图中，分析工具部分其实是比较容易掌握的，像 SQL 或 Python 这样的编程语言，只要经过系统的学习和适量的练习，大部分人都是可以驾驭的；像 Power BI、Tableau 这样的商业智能工具，更是让我们通过“拖拉拽”操作就能完成数据的可视化并在此基础上产生商业洞察，上手难度会更低。相反，数据思维部分的内容对大多数新手来说是不太容易驾驭的，例如“统计思维”，很多人在读书的时候都学习过“概率论和统计学”这样的课程，但是当面对实际的业务场景时，却很难将这些知识映射到业务场景来解决现实的问题。此外，如果没有掌握基本的分析方法、没有理解常用的分析模型，没有相关业务知识的积累，即便我们拿到再多有用的数据，也会感觉无从下手，更不用说产生业务洞察发现商业价值了。所以，数据思维这个部分，除了系统的学习相关知识技能，还需要不断的在实际业务场景中积累和沉淀。
+
+### 数据分析师的职责
+
+HR在发布招聘需求时，通常将数据工程、数据分析、数据挖掘等岗位都统称为数据分析岗位，但是根据工作性质的不同，又可以分为偏工程的**数据治理方向**、偏业务的**商业分析方向**、偏算法的**数据挖掘方向**、偏应用的**数据开发方向**、偏产品的**数据产品经理**。我们通常所说的数据分析师主要是指**业务数据分析师**，很多数据分析师的职业生涯都是从这个岗位开始的，而且这个岗位也是招聘数量最多的岗位。有些公司会将业务数据分析师归属到具体的业务部门（市场、运营、产品等），有些公司有专门的数据部门（数据分析团队或数据科学团队），还有些公司数据分析师会直接服务高层决策，属于企业战略部门。正因如此，你在招聘网站上看到的把数据分析师称为**数据运营**、**商业分析师**、**BI工程师**就不会感到奇怪了。通常，我们在招聘网站看到的对业务数据分析师岗位职责（JD）的描述如下所示：
 
 1. 负责相关报表的输出。
 2. 建立和优化指标体系。
@@ -16,25 +26,31 @@ HR在发布招聘需求时，通常将数据工程、数据分析、数据挖掘
 4. 优化和驱动业务，推动数字化运营。
 5. 找出潜在的市场和产品的上升空间。
 
-根据上面的描述，作为业务数据分析师，我们的工作不是给领导一个简单浅显的结论，而是结合公司的业务，完成**监控数据**、**揪出异常**、**找到原因**、**探索趋势**等工作。作为数据分析师，不管是用 Python 语言、Excel、SPSS或其他的商业智能工具，工具只是达成目标的手段，**数据思维是核心技能**，从实际业务问题出发到最终**发现数据中的商业价值**是终极目标。数据分析师在很多公司只是一个基础岗位，精于业务的数据分析师可以向**数据分析经理**或**数据运营总监**等管理岗位发展；对于熟悉机器学习算法的数据分析师来说，可以向**数据挖掘工程师**或**算法专家**方向发展，而这些岗位除了需要相应的数学和统计学知识，在编程能力方面也比数据分析师有更高的要求，可能还需要有大数据存储和处理的相关经验。数据治理岗位主要是帮助公司建设数据仓库或数据湖，实现数据从业务系统、埋点系统、日志系统到分析库的转移，为后续的数据分析和挖掘提供基础设施。数据治理岗位对 SQL 和 HiveSQL 有着较高的要求，需要熟练的使用 ETL 工具，此外还需要对 Hadoop 生态圈有一个较好的认知。作为数据产品经理，除了传统产品经理的技能栈之外，也需要较强的技术能力，例如要了解常用的推荐算法、机器学习模型，能够为算法的改进提供依据，能够制定相关埋点的规范和口径，虽然不需要精通各种算法，但是要站在产品的角度去考虑数据模型、指标、算法等的落地。
+根据上面的描述，作为业务数据分析师，我们的工作不是给出一个简单浅显的结论，而是结合公司的业务，完成**监控数据**、**揪出异常**、**找到原因**、**探索趋势**等工作。不管你是用 Python 语言、Excel、Tableau、SPSS或其他的商业智能工具，工具只是达成目标的手段，**数据思维是核心技能**，从实际业务问题出发到最终**发现数据中的商业价值**是终极目标。数据分析师在很多公司只是一个基础岗位，精于业务的数据分析师可以向**数据分析经理**或**数据运营总监**等管理岗位发展；对于熟悉机器学习算法的数据分析师来说，可以向**数据挖掘工程师**或**算法专家**方向发展，这些岗位除了需要相应的数学和统计学知识，在编程能力方面也比数据分析师有更高的要求，可能还需要有大数据存储和处理的相关经验。
 
-以下是我总结的数据分析师的技能栈，仅供参考。
+这里顺便说一下其他几个方向，数据治理岗位主要是帮助公司建设数据仓库或数据湖，实现数据从业务系统、埋点系统、日志系统到数据仓库或数据湖的转移，为后续的数据分析和挖掘提供基础设施。数据治理岗位对 SQL 和 HiveSQL 有着较高的要求，需要熟练的使用 ETL 工具，此外还需要对 Hadoop 生态圈有较好的认知。作为数据产品经理，除了传统产品经理的技能栈之外，也需要较强的技术能力，例如要了解常用的推荐算法、机器学习模型，能够为算法的改进提供依据，能够制定相关埋点的规范和口径，虽然不需要精通各种算法，但是要站在产品的角度去考虑数据模型、指标、算法等的落地。
+
+### 数据分析师的技能栈
+
+数据分析师的技能栈也包括硬技能和软技能，以下是我对这个职位的理解，仅供参考。
 
 1. 计算机科学（数据分析工具、编程语言、数据库）
 2. 数学和统计学（数据思维、统计思维）
-3. 人工智能（机器学习中的数据挖掘算法）
+3. 人工智能（机器学习和深度学习算法）
 4. 业务理解能力（沟通、表达、经验）
-5. 总结和表述能力（商业PPT、文字总结）
+5. 总结和表述能力（总结、汇报、商业 PPT）
+
+当然，对于一个新手来收，不可能用短时间掌握整个技能栈的内容，但是随着这份工作的深入，上面提到的东西多多少少都会涉猎到，大家可以根据实际工作的需求去深耕其中的某个或某些技能。
 
-### 数据分析的流程
+### 数据分析通用流程
 
 我们提到数据分析这个词很多时候可能指的都是**狭义的数据分析**，这类数据分析主要目标就是生成可视化报表并通过这些报表来洞察业务中的问题，这类工作一般都是具有滞后性的。**广义的数据分析**还包含了数据挖掘的部分，不仅要通过数据实现对业务的监控和分析，还要利用机器学习算法，找出隐藏在数据背后的知识，并利用这些知识为将来的决策提供支撑，具备一定的前瞻性。
 
 基本的数据分析工作一般包含以下几个方面的内容，当然因为行业和工作内容的不同会略有差异。
 
 1. 确定目标（输入）：理解业务，确定指标口径
-2. 获取数据：数据仓库（SQL提数）、电子表格、三方接口、网络爬虫、开放数据集等
-3. 清洗数据：包括对缺失值、重复值、异常值的处理以及相关的预处理（格式化、离散化、二值化等）
+2. 获取数据：数据仓库、电子表格、三方接口、网络爬虫、开放数据集等
+3. 清洗数据：缺失值、重复值、异常值的处理以及其他预处理（格式化、离散化、二值化等）
 4. 数据透视：排序、统计、分组聚合、交叉表、透视表等
 5. 数据呈现（输出）：数据可视化，发布工作成果（数据分析报告）
 6. 分析洞察（后续）：解释数据的变化，提出对应的方案
@@ -50,17 +66,23 @@ HR在发布招聘需求时，通常将数据工程、数据分析、数据挖掘
 
 ### 数据分析相关库
 
-使用 Python 从事数据科学相关的工作是一个非常棒的选择，因为 Python 整个生态圈中，有大量的成熟的用于数据科学的软件包（工具库）。而且不同于其他的用于数据科学的编程语言（如：Julia、R），Python 除了可以用于数据科学，还能做很多其他的事情，可以说 Python 语言几乎是无所不能的。
+使用 Python 从事数据分析相关的工作是一个非常棒的选择，首先 Python 语言非常容易上手，而且整个 Python 生态圈中，有非常多成熟的用于数据科学的软件包和工具库。不同于其他的数据科学编程语言（如：Julia、R等），Python 除了可以用于数据科学，还能做很多其他的事情。
 
-#### 三大神器
+#### 经典的三大神器
 
-1. [NumPy](https://numpy.org/)：支持常见的数组和矩阵操作，通过`ndarray`类实现了对多维数组的封装，提供了操作这些数组的方法和函数集。由于 NumPy 内置了并行运算功能，当使用多核 CPU 时，Numpy会自动做并行计算。
-2. [Pandas](https://pandas.pydata.org/)：pandas 的核心是其特有的数据结构`DataFrame`和`Series`，这使得 pandas 可以处理包含不同类型数据的表格和时间序列，这一点是NumPy的`ndarray`做不到的。使用 pandas，可以轻松顺利的加载各种形式的数据，然后对数据进行切片、切块、处理缺失值、聚合、重塑和可视化等操作。
+1. [NumPy](https://numpy.org/)：支持常见的数组和矩阵操作，通过`ndarray`类实现了对多维数组的封装，提供了操作这些数组的方法和函数。由于 NumPy 内置了并行运算功能，当使用多核 CPU 时，NumPy 会自动做并行计算。
+2. [Pandas](https://pandas.pydata.org/)：pandas 的核心是其特有的数据结构`DataFrame`和`Series`，这使得 pandas 可以处理包含不同类型数据的表格和时间序列，这一点是 NumPy 的`ndarray`做不到的。使用 pandas，可以轻松顺利的加载各种形式的数据，然后对数据进行切片、切块、重塑、清洗、聚合、呈现等操作。
 3. [Matplotlib](https://matplotlib.org/)：matplotlib 是一个包含各种绘图模块的库，能够根据我们提供的数据创建高质量的图表。此外，matplotlib 还提供了 pylab 模块，这个模块包含了很多像 [MATLAB](https://www.mathworks.com/products/matlab.html) 一样的绘图组件。
 
 #### 其他相关库
 
 1. [SciPy](https://scipy.org/)：完善了 NumPy 的功能，封装了大量科学计算的算法，包括线性代数、统计检验、稀疏矩阵、信号和图像处理、最优化问题、快速傅里叶变换等。
-2. [Seaborn](https://seaborn.pydata.org/)：seaborn 是基于 matplotlib 的图形可视化工具，直接使用 matplotlib 虽然可以定制出漂亮的统计图表，但是总体来说还不够简单方便，seaborn 相当于是对 matplotlib 做了封装，让用户能够以更简洁有效的方式做出各种有吸引力的统计图表。
-3. [Scikit-learn](https://scikit-learn.org/)：scikit-learn 最初是 SciPy 的一部分，提供了大量机器学习可能用到的工具，包括数据预处理、监督学习（分类、回归）、无监督学习（聚类）、模式选择、交叉检验等。
-4. [Statsmodels](https://www.statsmodels.org/stable/index.html)：包含了经典统计学和经济计量学算法的库。
+2. [Polars](https://pola.rs/)：一个高性能的数据分析库，旨在提供比 pandas 更快的数据操作。它支持大规模数据处理，并能够利用多核 CPU 来加速计算，在处理大规模数据集时可以用来替代 pandas。
+3. [Seaborn](https://seaborn.pydata.org/)：seaborn 是基于 matplotlib 的图形可视化工具，直接使用 matplotlib 虽然可以定制出漂亮的统计图表，但是总体来说还不够简单方便，seaborn 相当于是对 matplotlib 做了封装，让用户能够以更简洁有效的方式做出各种有吸引力的统计图表。
+4. [Scikit-learn](https://scikit-learn.org/)：scikit-learn 最初是 SciPy 的一部分，提供了大量机器学习可能用到的工具，包括数据预处理、监督学习（分类、回归）、无监督学习（聚类）、模式选择、交叉检验等。
+5. [Statsmodels](https://www.statsmodels.org/stable/index.html)：包含了经典统计学和计量经济学算法的库，帮助帮助用户完成数据探索、回归分析、假设检验等任务。
+6. [PySpark](https://spark.apache.org/)：Apache Spark（大数据处理引擎）的 Python 版本，用于大规模数据处理和分布式计算，能够在分布式环境中高效地进行数据清洗、转化和分析。
+7. [Tensorflow](https://www.tensorflow.org/)：TensorFlow 是一个开源的深度学习框架，由 Google 开发，主要面向深度学习任务，常用于构建和训练机器学习模型（尤其是复杂的神经网络模型）。
+8. [Keras](https://keras.io/)：Keras 是一个高层次的神经网络 API，主要用于构建和训练深度学习模型。Keras 适合深度学习初学者和研究人员，因为它让构建和训练神经网络变得更加简单。
+9. [PyTorch](https://pytorch.org/)：PyTorch 是一个开源的深度学习框架，由 Facebook 开发，广泛用于研究和生产环境。PyTorch 是深度学习研究中的热门框架，在计算机视觉、自然语言处理等领域得到了广泛应用。
+10. [NLTK](https://www.nltk.org/) / [SpaCy](https://spacy.io/)：自然语言处理（NLP）库。
diff --git "a/Day66-80/data/2018\345\271\264\345\214\227\344\272\254\347\247\257\345\210\206\350\220\275\346\210\267\346\225\260\346\215\256.csv" "b/Day66-80/code/data/2018\345\271\264\345\214\227\344\272\254\347\247\257\345\210\206\350\220\275\346\210\267\346\225\260\346\215\256.csv"
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rename to "Day66-80/code/data/2018\345\271\264\345\214\227\344\272\254\347\247\257\345\210\206\350\220\275\346\210\267\346\225\260\346\215\256.csv"
diff --git "a/Day66-80/data/2020\345\271\264\351\224\200\345\224\256\346\225\260\346\215\256.xlsx" "b/Day66-80/code/data/2020\345\271\264\351\224\200\345\224\256\346\225\260\346\215\256.xlsx"
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diff --git "a/Day66-80/data/2022\345\271\264\350\202\241\347\245\250\346\225\260\346\215\256.xlsx" "b/Day66-80/code/data/2022\345\271\264\350\202\241\347\245\250\346\225\260\346\215\256.xlsx"
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diff --git "a/Day66-80/data/2023\345\271\264\345\214\227\344\272\254\347\247\257\345\210\206\350\220\275\346\210\267\346\225\260\346\215\256.csv" "b/Day66-80/code/data/2023\345\271\264\345\214\227\344\272\254\347\247\257\345\210\206\350\220\275\346\210\267\346\225\260\346\215\256.csv"
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rename to "Day66-80/code/data/2023\345\271\264\345\214\227\344\272\254\347\247\257\345\210\206\350\220\275\346\210\267\346\225\260\346\215\256.csv"
diff --git a/Day66-80/data/boston_house_price.csv b/Day66-80/code/data/boston_house_price.csv
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rename from Day66-80/data/boston_house_price.csv
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rename to "Day66-80/code/data/\346\237\220\346\213\233\350\201\230\347\275\221\347\253\231\346\213\233\350\201\230\346\225\260\346\215\256.csv"
diff --git a/Day66-80/code/day01.ipynb b/Day66-80/code/day01.ipynb
new file mode 100644
index 000000000..0a8d1c614
--- /dev/null
+++ b/Day66-80/code/day01.ipynb
@@ -0,0 +1,602 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c664c108-059f-402a-b216-5ba4caa2d98b",
+   "metadata": {},
+   "source": [
+    "## Python数据分析第1天\n",
+    "\n",
+    "### 热身练习\n",
+    "\n",
+    "如下列表保存着本公司从2022年1月到12月五个销售区域（南京、无锡、苏州、徐州、南通）的销售额（以百万元为单位），请利用这些数据完成以下操作：\n",
+    "\n",
+    "```python\n",
+    "sales_month = [f'{i:>2d}月' for i in range(1, 13)]\n",
+    "sales_area = ['南京', '无锡', '苏州', '徐州', '南通']\n",
+    "sales_data = [\n",
+    "    [32, 17, 12, 20, 28],\n",
+    "    [41, 30, 17, 15, 35],\n",
+    "    [35, 18, 13, 11, 24],\n",
+    "    [12, 42, 44, 21, 34],\n",
+    "    [29, 11, 42, 32, 50],\n",
+    "    [10, 15, 11, 12, 26],\n",
+    "    [16, 28, 48, 22, 28],\n",
+    "    [31, 40, 45, 30, 39],\n",
+    "    [25, 41, 47, 42, 47],\n",
+    "    [47, 21, 13, 49, 48],\n",
+    "    [41, 36, 17, 36, 22],\n",
+    "    [22, 25, 15, 20, 37]\n",
+    "]\n",
+    "```\n",
+    "\n",
+    "1. 统计本公司每个月的销售额。\n",
+    "2. 统计本公司销售额的月环比。\n",
+    "3. 统计每个销售区域全年的销售额。\n",
+    "4. 按销售额从高到低排序销售区域及其销售额。\n",
+    "5. 统计全年最高的销售额出现在哪个月哪个区域。\n",
+    "6. 找出哪个销售区域的业绩最不稳定。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9d87cfc-deb0-46eb-b98c-2799a4908bc8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_month = [f'{i:>2d}月' for i in range(1, 13)]\n",
+    "sales_area = ['南京', '无锡', '苏州', '徐州', '南通']\n",
+    "sales_data = [\n",
+    "    [32, 17, 12, 20, 28],\n",
+    "    [41, 30, 17, 15, 35],\n",
+    "    [35, 18, 13, 11, 24],\n",
+    "    [12, 42, 44, 21, 34],\n",
+    "    [29, 11, 42, 32, 50],\n",
+    "    [10, 15, 11, 12, 26],\n",
+    "    [16, 28, 48, 22, 28],\n",
+    "    [31, 40, 45, 30, 39],\n",
+    "    [25, 41, 47, 42, 47],\n",
+    "    [47, 21, 13, 49, 48],\n",
+    "    [41, 36, 17, 36, 22],\n",
+    "    [22, 25, 15, 20, 37]\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc581dfc-9108-46fa-ace2-60ace650434e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 魔法指令 - %whos - 查看变量\n",
+    "%whos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a50e4c3e-6dc1-426f-977b-aef9a5c9a02f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print = 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c0b54ca-1556-4a14-9a6a-b6bd6af5d822",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 魔法指令 - %xdel - 删除变量\n",
+    "%xdel print"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe8eb05f-f45b-491a-b98e-6f6c924997ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. 统计本公司每个月的销售额。\n",
+    "monthly_sales = []\n",
+    "for i, month in enumerate(sales_month):\n",
+    "    monthly_sales.append(sum(sales_data[i]))\n",
+    "    print(f'{month}销售额: {monthly_sales[i]}百万')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53e6bf88-e6a9-4ac9-a7fe-bd1d18ff88f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2. 统计本公司销售额的月环比。\n",
+    "for i in range(1, len(monthly_sales)):\n",
+    "    temp = (monthly_sales[i] - monthly_sales[i - 1]) / monthly_sales[i - 1]\n",
+    "    print(f'{sales_month[i]}: {temp:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5a130d6-b781-4ee3-a96b-d1fe5e3b4b90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. 统计每个销售区域全年的销售额。\n",
+    "arealy_sales = {}\n",
+    "for j, area in enumerate(sales_area):\n",
+    "    temp = [sales_data[i][j] for i in range(len(sales_month))]\n",
+    "    arealy_sales[area] = sum(temp)\n",
+    "    print(f'{area}: {arealy_sales[area]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7bd0510-5e68-4e58-ac3b-6c531f7abccb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 4. 按销售额从高到低排序销售区域及其销售额。\n",
+    "sorted_keys = sorted(arealy_sales, key=lambda x: arealy_sales[x], reverse=True)\n",
+    "for key in sorted_keys:\n",
+    "    print(f'{key}: {arealy_sales[key]}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4b2f3e8-c5c2-481e-b277-9623d30892ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 5. 统计全年最高的销售额出现在哪个月哪个区域。\n",
+    "max_value = sales_data[0][0]\n",
+    "max_i, max_j = 0, 0\n",
+    "for i in range(len(sales_month)):\n",
+    "    for j in range(len(sales_area)):\n",
+    "        temp = sales_data[i][j]\n",
+    "        if temp > max_value:\n",
+    "            max_value = temp\n",
+    "            max_i, max_j = i, j\n",
+    "print(sales_month[max_i], sales_area[max_j])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "647d0a87-b672-4e0c-81cc-a3bbb76dca11",
+   "metadata": {},
+   "source": [
+    "总体方差：\n",
+    "$$\n",
+    "\\sigma^{2} = \\frac{1}{N} \\sum_{i=1}^{N}(x_{i} - \\mu)^{2}\n",
+    "$$\n",
+    "\n",
+    "样本方差：\n",
+    "$$\n",
+    "s^{2} = \\frac{1}{n - 1} \\sum_{i=1}^{n}(x_{i} - \\bar{x})^{2}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b43fb247-32fc-4e10-a9ee-488fd1f56a9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 6. 找出哪个销售区域的业绩最不稳定。\n",
+    "import statistics as stats\n",
+    "\n",
+    "arealy_vars = []\n",
+    "for j, area in enumerate(sales_area):\n",
+    "    temp = [sales_data[i][j] for i in range(len(sales_month))]\n",
+    "    arealy_vars.append(stats.pvariance(temp))\n",
+    "sales_area[arealy_vars.index(max(arealy_vars))]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ea677d0-7a33-43e5-b10b-ddfcb82f7f6a",
+   "metadata": {},
+   "source": [
+    "### 三大神器\n",
+    "\n",
+    "1. numpy - Numerical Python - 核心是`ndarray`类型，可以用来表示N维数组，提供了一系列处理数据的运算、函数和方法。\n",
+    "2. pandas - Panel Data Set - 封装了和数据分析（加载、重塑、清洗、预处理、透视、呈现）相关的类型、函数和诸多的方法，为数据分析提供了一站式解决方案。它的核心有三个数据类型，分别是：`Series`、`DataFrame`、`Index`。\n",
+    "3. matplotlib - 封装了各种常用的统计图表，帮助我们实现数据呈现。\n",
+    "4. scipy - Scientific Python - 针对NumPy进行了很好的补充，提供了高级的数据运算的函数和方法。\n",
+    "5. scikit-learn - 封装了常用的机器学习（分类、聚类、回归等）算法，除此之外，还提供了数据预处理、特征工程、模型验证相关的函数和方法。\n",
+    "6. sympy - Symbolic Python - 封装了符号运算相关操作。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0db758cc-d83c-47c4-9a0b-c7ef5abd6c18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 魔法指令 - %pip - 调用包管理工具pip\n",
+    "# %pip install numpy pandas matplotlib openpyxl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8eb6970b-3907-4b84-af60-67cbf67f2e74",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fb76dec-cd51-4e79-9bd2-3b210ae20522",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6369df9-7577-496c-bfc1-2fce096c0162",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb5733cd-38f7-4afd-b45b-70c1439ab36b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将嵌套列表处理成二维数组\n",
+    "data = np.array(sales_data)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da304104-8cf0-4425-b3b4-dcb148ac4b3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 沿着1轴求和（每个月的销售额）\n",
+    "data.sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1507ac63-f53b-4e36-a7fb-b9c636fd81ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 沿着0轴求和（每个区域的销售）\n",
+    "data.sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26be450d-44ba-4d83-9351-c52a13c2c338",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 总体方差\n",
+    "data.var(axis=0).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "81e5b2a0-c86e-4720-909f-ce8b1b6fdd58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 样本方差\n",
+    "data.var(axis=0, ddof=1).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba4e0f0a-e711-4041-8834-1e3be86ce8a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 构造DataFrame对象（处理二维数据）\n",
+    "df = pd.DataFrame(data, columns=sales_area, index=sales_month)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d1a6a43-6dfc-41e3-98c8-be2681e0d547",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 求和（默认沿着0轴）\n",
+    "df.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a478ec0e-499f-4e31-b8c2-ba45e691b834",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 排序\n",
+    "df.sum().sort_values(ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f221833-855c-45ad-91b2-e3f4da627704",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 求和（指定沿着1轴）\n",
+    "df.sum(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80df8865-4ea0-4c72-a581-215cd953cfbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 计算月环比\n",
+    "df.sum(axis=1).pct_change()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea4579c3-11cd-4179-9c96-8dbe9a033da2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['合计'] = df.sum(axis=1)\n",
+    "df['月环比'] = df['合计'].pct_change()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c660052-dded-4a0a-8b72-7747d3cae816",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 渲染DataFrame\n",
+    "df.style.format(\n",
+    "    formatter={'月环比': '{:.2%}'},\n",
+    "    na_rep='------'\n",
+    ").bar(\n",
+    "    subset='合计'\n",
+    ").background_gradient(\n",
+    "    'RdYlBu', subset='月环比'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a092c12c-dab6-4272-b1cd-5218998fcd90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将DataFrame输出到Excel文件\n",
+    "df.to_excel('sales.xlsx', sheet_name='data')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54c3f505-e866-4c4e-a3f8-f55a71a95c3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 魔法指令 - %config - 修改配置\n",
+    "# %config InlineBackend.figure_format = 'svg'\n",
+    "get_ipython().run_line_magic('config', 'InlineBackend.figure_format = \"svg\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3951055d-d5d2-4e4e-bbe7-a1b40a6731e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制柱状图\n",
+    "plt.figure(figsize=(8, 4), dpi=200)\n",
+    "df.plot(ax=plt.gca(), kind='bar', y='合计', legend=False)\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.savefig('aa.png')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a5236f7-072b-466c-9be3-afbab394f5cb",
+   "metadata": {},
+   "source": [
+    "### 魔法指令"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5c6a18b-2863-4855-8ef7-2c0aa99b7d5c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 查看当前工作路径 - print working directory\n",
+    "%pwd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80a9f9e0-1528-40cf-910c-f3c8e5e7e3b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 查看指定路径文件列表 - list directory contents\n",
+    "%ls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "620a54ed-9c29-4058-9d20-c4df72ba4c62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 执行系统命令\n",
+    "%system date"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "659215ed-113a-4d8f-9036-0fcf47c96021",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 保存运行过的代码\n",
+    "%save temp.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fc9c4e4-1423-40f3-b4ee-db2ba2e5d125",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加载指定文件内容\n",
+    "%load temp.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58a08283-561c-43d4-8db6-74cde401b8a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计代码执行时间\n",
+    "%timeit (1, 2, 3, 4, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22a271ab-3f5c-4167-b89e-66a31e891cbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 查看历史输入\n",
+    "%hist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4ffa792-f1a0-4be9-b2aa-642ee0b9a1ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 查看魔法指令\n",
+    "%lsmagic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a15db907-c068-41d7-a24c-8f1c5c20d4ec",
+   "metadata": {},
+   "source": [
+    "### 获取帮助"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e037694-9357-46b9-864a-c5f93e1aa8c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11a97abd-d73d-493e-b727-9c4ded3e5060",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.normal?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66503921-cd69-4394-80ea-7fecf6ecdc33",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.r*?"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Day66-80/code/day02.ipynb b/Day66-80/code/day02.ipynb
new file mode 100644
index 000000000..9113ae0c9
--- /dev/null
+++ b/Day66-80/code/day02.ipynb
@@ -0,0 +1,10086 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "f11bbe2b-7325-4bf0-abfb-4b4e64292145",
+   "metadata": {},
+   "source": [
+    "## NumPy入门\n",
+    "\n",
+    "NumPy是Python数据科学三方库中最为重要的基石，提供了数据存储和运算的能力，其他很多跟数据科学相关的库底层都依赖了NumPy。NumPy的核心是名为`ndarray`的数据类型，用来表示任意维度的数组，相较于Python的`list`，它具有以下优势：\n",
+    "\n",
+    "1. 有更好的性能，可以利用硬件的并行计算能力和缓存优化，相较于`list`在处理数据的性能上有着数量级的差异。\n",
+    "2. 功能更加强大，`ndarray`提供了丰富的运算和方法来处理数据，NumPy中还针对数组操作封装了大量的函数。\n",
+    "3. 向量化操作，NumPy中的函数以及`ndarray`的方法都是对作用于整个数组，无需使用显示的循环，代码更加简单优雅。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "15630f70-be3c-4690-96a6-0b134a685efb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8a115f09-0477-4e62-a910-d9284f32fbd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %save hello.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8c3b5a1-9ad1-4511-b7f8-beacce89cf69",
+   "metadata": {},
+   "source": [
+    "### 创建数组对象\n",
+    "\n",
+    "1. 通过`array`/`asarray`函数将列表处理成数组对象\n",
+    "2. 通过`arange`函数指定起始值、终止值和跨度创建数组对象\n",
+    "3. 通过`linspace`函数指定起始值、终止值和元素个数创建等差数列\n",
+    "4. 通过`logspace`函数指定起始值（指数）、终止值（指数）、元素个数、底数（默认10）创建等比数列\n",
+    "5. 通过`fromstring`/`fromfile`函数从字符串或文件中读取数据创建数组对象\n",
+    "6. 通过`fromiter`函数通过迭代器获取数据创建数组对象\n",
+    "7. 通过生成随机元素的方式创建数组对象\n",
+    "8. 通过`zeros`/`zeros_like`函数创建全0元素的数组对象\n",
+    "9. 通过`ones`/`ones_like`函数创建全1元素的数组对象\n",
+    "10. 通过`full`函数指定元素值创建数组对象\n",
+    "11. 通过`eye`函数创建单位矩阵\n",
+    "12. 通过`tile`/`repeat`函数重复元素创建数组对象"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a035b671-2f91-473c-ac2b-e25291cf664b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5], dtype=int32)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法一：通过array函数将列表处理成数组对象\n",
+    "array1 = np.array([1, 2, 3, 4, 5], dtype='i4')\n",
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "28a8c2f7-c197-4d5e-9006-d99242edefee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "95eae152-6bfc-4707-bd04-50c7363e9315",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2 = np.array([[1, 2, 3], [4, 5, 6]])\n",
+    "array2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3c662a6d-d017-4a85-b93c-8538f334db22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法二：通过arange函数指定范围创建数组对象\n",
+    "array3 = np.arange(1, 10)\n",
+    "array3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "954605a1-1004-4595-ac90-523feac7f4e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  4,  7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46, 49,\n",
+       "       52, 55, 58, 61, 64, 67, 70, 73, 76, 79, 82, 85, 88, 91, 94, 97])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array4 = np.arange(1, 100, 3)\n",
+    "array4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e479f343-7033-4b73-a0ce-e7f04444e915",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 方法三：通过linspace函数创建等差数列\n",
+    "array5 = np.linspace(-2 * np.pi, 2 * np.pi, 120)\n",
+    "array6 = np.sin(array5)\n",
+    "array7 = np.cos(array5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "57b0e126-3276-4f10-b1d1-288597842d35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "62a6b0ed-acaf-45a1-90b0-7caf73184d09",
+   "metadata": {},
+   "outputs": [
+    {
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+       "    <dc:date>2024-09-19T22:45:17.121512</dc:date>\n",
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+       "   <g id=\"patch_6\">\n",
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+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"padbd8c9e70\">\n",
+       "   <rect x=\"39.2\" y=\"7.2\" width=\"446.4\" height=\"221.76\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8, 4))\n",
+    "# 绘制折线图\n",
+    "plt.plot(array5, array6, marker='.', color='darkgreen')\n",
+    "plt.plot(array5, array7, marker='.', color='coral')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "fc6ed6f5-6844-47df-8dde-fd9e598af48d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([   1,    2,    4,    8,   16,   32,   64,  128,  256,  512, 1024])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法四：通过logspace函数创建等比数列\n",
+    "array8 = np.logspace(0, 10, num=11, base=2, dtype='i8')\n",
+    "array8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "f3d2ba81-cf13-4d96-afef-f9221b4b4a68",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1,  11, 111,   2,  22, 222])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法五：通过fromstring/fromfile/fromregex函数从字符串读取数据创建数组\n",
+    "array9 = np.fromstring('1, 11, 111, 2, 22, 222', sep=',', dtype='i8')\n",
+    "array9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "1c691969-f93c-4e32-9ba5-ab5e074a6409",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.core.interactiveshell import InteractiveShell\n",
+    "\n",
+    "InteractiveShell.ast_node_interactivity = 'last_expr'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d8eb5b58-d129-459f-9969-543191fb1966",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2,  3,  5,  7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array10 = np.fromfile('res/prime.txt', dtype='i8', sep='\\n', count=15)\n",
+    "array10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ff01ce19-fc81-41db-88a2-647299ec940c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object fib at 0x11d65d7b0>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 面试官：请说一下Python中的迭代器是什么？它跟生成器是什么关系？\n",
+    "# 迭代器是实现了迭代器协议的对象。在Python中迭代器协议是两个魔术方法：__iter__、__next__\n",
+    "# 我们可以通过next函数或者for-in循环从迭代器中获取数据\n",
+    "# 迭代器的编写相对比较麻烦，所以在Python中可以用创建生成器的方式简化迭代器语法\n",
+    "\n",
+    "\n",
+    "def fib(count):\n",
+    "    a, b = 0, 1\n",
+    "    for _ in range(count):\n",
+    "        a, b = b, a + b\n",
+    "        yield a\n",
+    "\n",
+    "\n",
+    "gen = fib(50)\n",
+    "gen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5a2c15a6-5744-4eb6-8f94-3132f7e0b1b6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([          1,           1,           2,           3,           5,\n",
+       "                 8,          13,          21,          34,          55,\n",
+       "                89,         144,         233,         377,         610,\n",
+       "               987,        1597,        2584,        4181,        6765,\n",
+       "             10946,       17711,       28657,       46368,       75025,\n",
+       "            121393,      196418,      317811,      514229,      832040,\n",
+       "           1346269,     2178309,     3524578,     5702887,     9227465,\n",
+       "          14930352,    24157817,    39088169,    63245986,   102334155,\n",
+       "         165580141,   267914296,   433494437,   701408733,  1134903170,\n",
+       "        1836311903,  2971215073,  4807526976,  7778742049, 12586269025])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法六：通过fromiter函数从迭代器中读取数据创建数组对象\n",
+    "array11 = np.fromiter(fib(50), dtype='i8')\n",
+    "array11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ca62e6fb-f0c9-4f12-acdb-978507e37f94",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[90, 45, 91, 71],\n",
+       "       [85,  2, 98, 76],\n",
+       "       [58, 50, 72, 13],\n",
+       "       [66, 90, 26, 69],\n",
+       "       [23, 44, 68, 98]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法七：通过生成随机元素创建数组对象\n",
+    "array12 = np.random.randint(0, 101, (5, 4))\n",
+    "array12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2ec313a2-bdd6-492f-9176-172b1ec54534",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.35742922, 0.49173669, 0.14993948, 0.15556126, 0.48435648,\n",
+       "       0.57329703, 0.7256331 , 0.96709102, 0.79687864, 0.95782978])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array13 = np.random.random(10)\n",
+    "array13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c8ca9327-c30e-4068-b0bc-c18c49a48c89",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([155., 173., 172., ..., 171., 176., 157.])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array14 = np.random.normal(169, 8.5, 5000).round(0)\n",
+    "array14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e6f0fe78-41bf-45c9-a433-cf3cc40cdc11",
+   "metadata": {},
+   "outputs": [
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+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 绘制直方图\n",
+    "plt.hist(array14, bins=15, color='#6B8A7A')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "88de114b-c346-4150-b33c-f2d14dd84193",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0.]])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法八：通过zeros/zeros_like函数创建全0元素的数组对象\n",
+    "array15 = np.zeros((5, 4))\n",
+    "array15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "b03bc9d0-1274-46a4-a9b6-28f634a9d034",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 0, 0],\n",
+       "       [0, 0, 0]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array16 = np.zeros_like(array2)\n",
+    "array16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "db9f61e1-d50a-4f7f-a1b4-66798c0976ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.],\n",
+       "       [1., 1., 1., 1.]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法九：通过ones/ones_like函数创建全0元素的数组对象\n",
+    "array17 = np.ones((5, 4))\n",
+    "array17"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "07ddd6d3-2e91-4a1b-857d-5f8b6867904d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1, 1],\n",
+       "       [1, 1, 1]])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array18 = np.ones_like(array2)\n",
+    "array18"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "6b45e1db-8e1c-4af9-9495-c3a7f06b9311",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[100, 100, 100, 100],\n",
+       "       [100, 100, 100, 100],\n",
+       "       [100, 100, 100, 100],\n",
+       "       [100, 100, 100, 100],\n",
+       "       [100, 100, 100, 100]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法十：通过full函数指定值和形状创建数组对象\n",
+    "array19 = np.full((5, 4), 100)\n",
+    "array19"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "1ae4620a-11bd-464b-abc2-83f7f8b7e8ba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法十一：通过eye函数创建单位矩阵\n",
+    "# identify matrix --> I --> eye\n",
+    "array20 = np.eye(10)\n",
+    "array20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "0ae939c8-0977-42b0-8d8a-351f55b0471e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3,\n",
+       "       3, 3, 3, 3, 3, 3, 3, 3])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 方法十二：通过repeat/tile函数重复元素创建数组对象\n",
+    "array21 = np.repeat([1, 2, 3], 10)\n",
+    "array21"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c1a8d343-a30d-4144-baa8-b000a65c070d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1,\n",
+       "       2, 3, 1, 2, 3, 1, 2, 3])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array22 = np.tile([1, 2, 3], 10)\n",
+    "array22"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "96e8c639-1a14-453c-89a5-6fd74ca89a88",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 36,  33,  28],\n",
+       "        [ 36,  33,  28],\n",
+       "        [ 36,  33,  28],\n",
+       "        ...,\n",
+       "        [ 32,  31,  29],\n",
+       "        [ 32,  31,  27],\n",
+       "        [ 31,  32,  26]],\n",
+       "\n",
+       "       [[ 37,  34,  29],\n",
+       "        [ 38,  35,  30],\n",
+       "        [ 38,  35,  30],\n",
+       "        ...,\n",
+       "        [ 31,  30,  28],\n",
+       "        [ 31,  30,  26],\n",
+       "        [ 30,  31,  25]],\n",
+       "\n",
+       "       [[ 38,  35,  30],\n",
+       "        [ 38,  35,  30],\n",
+       "        [ 38,  35,  30],\n",
+       "        ...,\n",
+       "        [ 30,  29,  27],\n",
+       "        [ 30,  29,  25],\n",
+       "        [ 29,  30,  25]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[239, 178, 123],\n",
+       "        [237, 176, 121],\n",
+       "        [235, 174, 119],\n",
+       "        ...,\n",
+       "        [ 78,  68,  56],\n",
+       "        [ 76,  66,  54],\n",
+       "        [ 73,  65,  52]],\n",
+       "\n",
+       "       [[238, 177, 120],\n",
+       "        [236, 175, 118],\n",
+       "        [234, 173, 116],\n",
+       "        ...,\n",
+       "        [ 80,  70,  58],\n",
+       "        [ 78,  68,  56],\n",
+       "        [ 74,  67,  51]],\n",
+       "\n",
+       "       [[237, 176, 119],\n",
+       "        [236, 175, 118],\n",
+       "        [234, 173, 116],\n",
+       "        ...,\n",
+       "        [ 83,  71,  59],\n",
+       "        [ 81,  69,  57],\n",
+       "        [ 77,  68,  53]]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 补充：读图片获得一个三维数组对象\n",
+    "guido_image = plt.imread('res/guido.jpg')\n",
+    "guido_image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "dff598ad-ce04-4115-bffc-b70decd6a54e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(750, 500, 3)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "b8e6b35a-e9bb-489e-ab54-9fc2874b5708",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x11af1b8d0>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"213.808pt\" height=\"298.877561pt\" viewBox=\"0 0 213.808 298.877561\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
+       " <metadata>\n",
+       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2024-09-19T22:45:17.291166</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
+       "     <cc:Agent>\n",
+       "      <dc:title>Matplotlib v3.9.2, https://matplotlib.org/</dc:title>\n",
+       "     </cc:Agent>\n",
+       "    </dc:creator>\n",
+       "   </cc:Work>\n",
+       "  </rdf:RDF>\n",
+       " </metadata>\n",
+       " <defs>\n",
+       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
+       " </defs>\n",
+       " <g id=\"figure_1\">\n",
+       "  <g id=\"patch_1\">\n",
+       "   <path d=\"M 0 298.877561 \n",
+       "L 213.808 298.877561 \n",
+       "L 213.808 0 \n",
+       "L 0 0 \n",
+       "z\n",
+       "\" style=\"fill: #ffffff\"/>\n",
+       "  </g>\n",
+       "  <g id=\"axes_1\">\n",
+       "   <g id=\"patch_2\">\n",
+       "    <path d=\"M 29.2 276.552561 \n",
+       "L 206.608 276.552561 \n",
+       "L 206.608 10.440561 \n",
+       "L 29.2 10.440561 \n",
+       "z\n",
+       "\" style=\"fill: #ffffff\"/>\n",
+       "   </g>\n",
+       "   <g clip-path=\"url(#p924f728a34)\">\n",
+       "    <image xlink:href=\"data:image/png;base64,\n",
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+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
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+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(guido_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e6bc238-b4f7-45ed-8d3e-194576f67fa9",
+   "metadata": {},
+   "source": [
+    "### 数组对象的属性\n",
+    "\n",
+    "1. `size` - 元素的个数\n",
+    "2. `dtype` - 元素的数据类型\n",
+    "3. `ndim` - 数组的维度\n",
+    "4. `shape` - 数组的形状\n",
+    "5. `itemsize` - 每个元素占用的内存空间大小（字节）\n",
+    "6. `nbytes` - 所有元素占用的内存空间大小（字节）\n",
+    "7. `T` - 转置\n",
+    "8. `flags` - 内存信息\n",
+    "9. `base` - 根基"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "699ac4e0-a11a-4f23-9469-052371e6a140",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5], dtype=int32)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "292a05b0-351f-4e6b-8968-aebe3b859b0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 大小 - 元素个数\n",
+    "array1.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "0b382c41-8a42-4946-8d82-27c959d08cf8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 数据类型\n",
+    "array1.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "17c532d9-f1bf-4da9-9a98-4b450997d32b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 维度\n",
+    "array1.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c1d57809-19cd-4540-a23a-05d5d639b98b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5,)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 形状 - 元组\n",
+    "array1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "3376c6e2-ec8e-4743-81dd-2409fd869a52",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 每个元素占用内存空间大小（字节）\n",
+    "array1.itemsize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "29f66406-f940-434f-8d03-c0706cfa412b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 所有元素占用内存空间大小（字节）\n",
+    "array1.nbytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "68066920-3062-41cf-9cf7-012850461d70",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "43e80ef1-edcf-45b7-8143-d4a159a71c0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 4],\n",
+       "       [2, 5],\n",
+       "       [3, 6]])"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "ea0cc4f7-b504-458f-acdb-ff79d9730a19",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "b268f3dc-4652-4736-8831-b21e1f3e76d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int64')"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "b93e1712-3e62-4464-90e7-d90847e1763e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "0f17fd54-6969-4104-9d0e-cfc70678e663",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "f573369f-07ef-4dea-a46a-a4f5d837ec5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2.itemsize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "cc8887de-4600-47f4-beb3-37979ec079f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2.nbytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "9457f0c9-b433-4ec5-961d-6e8ebedad2db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  C_CONTIGUOUS : True\n",
+       "  F_CONTIGUOUS : False\n",
+       "  OWNDATA : True\n",
+       "  WRITEABLE : True\n",
+       "  ALIGNED : True\n",
+       "  WRITEBACKIFCOPY : False"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2.flags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "22adf5d7-c491-4558-af40-fa1a7fef7d6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1125000"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "adf08994-775b-4276-8392-95a9da821fb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('uint8')"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "81f03343-beac-4fe2-9b5e-9e72ca4387ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "2d01a9e3-62f1-4145-83c3-3f16ac647975",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(750, 500, 3)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "47576187-1b9b-4c08-b63a-9abe6a1352a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image.itemsize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "7304e015-1f67-4920-a045-baf66c60e9df",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1125000"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image.nbytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bca272d8-c859-4c9f-ad8f-76b8ba2926bf",
+   "metadata": {},
+   "source": [
+    "### 数组对象的运算\n",
+    "\n",
+    "#### 算术运算\n",
+    "\n",
+    "1. 与标量运算\n",
+    "2. 与数组运算 - 两个数组形状相同"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "5bec613e-df64-4a14-82a8-5ffa05d8ec48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([11, 12, 13, 14, 15], dtype=int32)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1 + 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "c2c2001a-4743-44ba-93e6-31b089196e31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 5, 10, 15],\n",
+       "       [20, 25, 30]])"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2 * 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "575bb5dc-bb20-471e-9237-e500d2f3796a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  4,  9],\n",
+       "       [16, 25, 36]])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2 ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "82bd25ad-a422-46cb-aa1c-41dba47fd54b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 1, 3],\n",
+       "       [4, 7, 2]])"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 = np.random.randint(1, 10, (2, 3))\n",
+    "temp1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "2a2a9ce0-41ef-417b-a082-b1a8b15213c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 2,  3,  6],\n",
+       "       [ 8, 12,  8]])"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 + array2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "af6ac896-2992-4e78-8fcc-9d2a55ccd188",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  2,  9],\n",
+       "       [16, 35, 12]])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 * array2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "1cf8cb80-3ba2-4b6a-8985-a4b63bdceda2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[    1,     1,    27],\n",
+       "       [  256, 16807,    64]])"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 ** array2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f6f13e4c-754a-4017-8898-52b00c97a910",
+   "metadata": {},
+   "source": [
+    "#### 比较运算\n",
+    "\n",
+    "1. 与标量运算\n",
+    "2. 与数组运算"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "bd4f7a63-341a-4a8a-9042-a2c286e606c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False,  True,  True])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1 > 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "68ebdc1a-ca87-439b-9aaf-bc59742c04f0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False],\n",
+       "       [ True,  True,  True]])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2 > 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "9d292f4f-3067-4fce-85aa-b4787bb90d24",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False],\n",
+       "       [False,  True, False]])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 > array2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "ea44e117-566f-4fad-a9ee-6ab380461a75",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True, False,  True],\n",
+       "       [ True, False, False]])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp1 == array2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e2ed3d9-72ba-42ac-a1c3-00f469d7a3bc",
+   "metadata": {},
+   "source": [
+    "#### 逻辑运算\n",
+    "\n",
+    "1. 与标量的运算\n",
+    "2. 与数组的运算"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "621c9302-b475-4151-8627-36001424a38d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp2 = np.array([True, False, True, False, True])\n",
+    "temp3 = np.array([True, False, False, False, True])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "355c4862-058d-47a0-9d36-331881f26c6e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False,  True, False,  True])"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp2 & True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "711b9eda-9fe2-49b6-903c-3921507abafa",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True,  True,  True,  True])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp2 | True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "0ec28456-84a7-4161-8bc6-765c4410ca7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False, False, False,  True])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp2 & temp3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "11c56fd5-ae20-4e55-96c5-aacf2b4e3df1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False,  True, False,  True])"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp2 | temp3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "afcc3303-0705-42b0-a503-b3efbd68590a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True, False,  True, False])"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "~temp2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df7b1f3f-4d89-45ea-9982-69ae9069dc8c",
+   "metadata": {},
+   "source": [
+    "#### 索引运算\n",
+    "\n",
+    "1. 普通索引 - 跟列表的索引运算类似\n",
+    "2. 花式索引 - 用列表或数组充当数组的索引\n",
+    "3. 布尔索引 - 用保存布尔值的数组充当索引\n",
+    "4. 切片索引 - 跟列表的切片运算类似"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "e845e1b3-d137-4832-b016-ed8d64c18a8f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([42, 49, 40, 75, 55, 99, 44, 80, 74])"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4 = np.random.randint(1, 100, 9)\n",
+    "temp4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "a6b49f68-f43b-42be-aa8b-0bbe4ca52f79",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(99)"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4[5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "35bc13a1-1ce8-4bf8-ba6a-310d145788da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(99)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4[-4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "4f53346f-a086-400f-84a7-a7ce264826bd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([42, 49, 40, 75, 55, 99, 44, 80, 74])"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4[5] = 99\n",
+    "temp4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "0f6f4528-191c-4ca8-809d-6503d4076a53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[95, 91, 74, 23, 37],\n",
+       "       [90, 74, 38, 87, 24],\n",
+       "       [ 9, 85, 23, 33, 36],\n",
+       "       [86, 76, 57, 12, 22]])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5 = np.random.randint(1, 100, (4, 5))\n",
+    "temp5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "7bcbf6e0-7e2e-4e41-bab8-cd9b99e67ad3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(38)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[1][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "a7a9a1c8-ecbc-4e7e-9f4e-3a7e8dadb249",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(38)"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[1, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "979c64b0-a600-4229-859c-252bb597185d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[95, 91, 74, 23, 37],\n",
+       "       [90, 74, 38, 87, 24],\n",
+       "       [ 9, 85, 23, 33, 36],\n",
+       "       [86, 76, 57, 12, 99]])"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[-1, -1] = 99\n",
+    "temp5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "4a5823e6-d3ff-411e-8f06-858dbdac006b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[95, 91, 74, 23, 37],\n",
+       "       [90, 74, 38, 87, 24],\n",
+       "       [ 9, 85, 23, 33, 36],\n",
+       "       [86, 55, 57, 12, 99]])"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[-1, 1] = 55\n",
+    "temp5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "6282b8c7-d23a-4079-ad98-88bc606ff93f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[36, 33, 28],\n",
+       "       [36, 33, 28],\n",
+       "       [36, 33, 28],\n",
+       "       ...,\n",
+       "       [32, 31, 29],\n",
+       "       [32, 31, 27],\n",
+       "       [31, 32, 26]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "0f7e83f3-4ab2-44a6-b537-c32645fe2abc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([36, 33, 28], dtype=uint8)"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image[0, 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "03477e7b-04f2-4de3-bd4d-f070b1983304",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.uint8(33)"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido_image[0, 0, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "0e4f6f3f-0cef-4e6a-89d9-d3281f59c5d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([49, 49, 49, 40, 40, 80, 99, 99])"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 花式索引 - fancy index - 用放整数的列表或者数组充当数组的索引\n",
+    "temp4[[1, 1, 1, 2, 2, -2, -4, -4]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "1ead8a5d-f4c0-4f5a-8709-b072ba676118",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([23, 74, 74, 33, 23, 23, 23])"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[[0, 1, 1, 2, 0, 0, 0], [3, 1, 1, -2, -2, -2, -2]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "id": "5e31efc0-8f1a-4a8d-ab85-8e1cc592d5d8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([42, 75, 99, 80])"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 布尔索引 - 用放布尔值的数组或列表充当数组的索引 - 实现数据筛选\n",
+    "temp4[[True, False, False, True, False, True, False, True, False]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "6145ef49-423f-40e0-acc7-8a8f897f4fb6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False,  True, False,  True, False,  True,  True])"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4 > 70"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "d48715c6-c743-4457-9896-211d1ad74f97",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([75, 99, 80, 74])"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4[temp4 > 70]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "efe7652f-1a75-4eed-9b33-2d52dfd25626",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True, False,  True, False, False, False,  True,  True,  True])"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4 % 2 == 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "4f2d6b1b-259d-4721-8dc6-326be4a73d57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([42, 40, 44, 80, 74])"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4[temp4 % 2 == 0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "id": "261f57fd-e06a-4aca-8c84-e70b43b42795",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False, False, False, False,  True,  True])"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(temp4 > 70) & (temp4 % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "5a52756e-e275-4750-b559-708bbe8fc045",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([80, 74])"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4[(temp4 > 70) & (temp4 % 2 == 0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "51be708e-afc5-47b2-9392-6c53738eb7d1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([42, 40, 75, 99, 44, 80, 74])"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4[(temp4 > 70) | (temp4 % 2 == 0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "93bd6975-bf01-4801-81e7-fc0bf7b21285",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ True,  True,  True, False, False],\n",
+       "       [ True,  True, False,  True, False],\n",
+       "       [False,  True, False, False, False],\n",
+       "       [ True, False, False, False,  True]])"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5 > 70"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "df324e6a-85d1-40de-acce-bfbb35e8a4cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([95, 91, 74, 90, 74, 87, 85, 86, 99])"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[temp5 > 70]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "6388f55b-40af-4cd1-abbc-29da10641580",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([74, 90, 74, 86])"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[(temp5 > 70) & (temp5 % 2 == 0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "e30c750e-5517-4314-b6d1-4fc28c5a454b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([42, 49, 40, 75, 55, 99, 44, 80, 74])"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "6e294ace-b236-4554-b34c-6f2b00bd295f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([40, 75, 55, 99, 44])"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 切片索引 - slice\n",
+    "temp4[2:7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "1519ca3a-9844-4b20-a00f-96b61780d998",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([40, 55, 44])"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 切片索引 - slice\n",
+    "temp4[2:7:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "9b96d847-5aec-4e7f-b6a7-48f4deecf454",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([44, 99, 55, 75, 40])"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp4[6:1:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "5dbcfa29-9930-471e-81d3-100f22e6293d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[95, 91, 74, 23, 37],\n",
+       "       [90, 74, 38, 87, 24],\n",
+       "       [ 9, 85, 23, 33, 36],\n",
+       "       [86, 55, 57, 12, 99]])"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "a1307d26-d1a3-4201-9ffa-314a81300712",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[74, 38, 87],\n",
+       "       [85, 23, 33]])"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[1:3, 1:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "e95ddb0d-ee31-4690-a91b-a0f0137ce07a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[33, 36],\n",
+       "       [12, 99]])"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[2:, 3:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "d69e9936-049a-4a66-af22-bb7feff1b9e7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[23, 33],\n",
+       "       [57, 12]])"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[2:, 2:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "befe1ce3-4742-4fd9-97de-3d9608ccf4c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[95, 91, 74],\n",
+       "       [90, 74, 38],\n",
+       "       [ 9, 85, 23]])"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[:3, :3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "ba3fcdcd-f999-41d4-9e96-858dc0f3d70d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[95, 91, 74],\n",
+       "       [90, 74, 38],\n",
+       "       [ 9, 85, 23],\n",
+       "       [86, 55, 57]])"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp5[:, :3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "753b5012-78fd-4a21-997e-132c5a15f636",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/html": [
+       "<div style=\"vertical-align: middle;\"><strong>gray</strong> </div><div class=\"cmap\"><img alt=\"gray colormap\" title=\"gray\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#000000ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #000000ff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#ffffffff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ffffffff;\"></div></div></div>"
+      ],
+      "text/plain": [
+       "<matplotlib.colors.LinearSegmentedColormap at 0x11d609ad0>"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.get_cmap('gray')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "2de0bf4c-61f4-48d9-a12b-9accbb883962",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       [False, False, False, ..., False, False, False],\n",
+       "       ...,\n",
+       "       [ True,  True,  True, ..., False, False, False],\n",
+       "       [ True,  True,  True, ..., False, False, False],\n",
+       "       [ True,  True,  True, ..., False, False, False]])"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(guido_image, axis=2) >= 128"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "id": "780b60d2-c634-4294-baea-00e696143942",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"848.549728pt\" height=\"531.751769pt\" viewBox=\"0 0 848.549728 531.751769\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
+       " <metadata>\n",
+       "  <rdf:RDF xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n",
+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2024-09-19T22:49:50.432942</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
+       "     <cc:Agent>\n",
+       "      <dc:title>Matplotlib v3.9.2, https://matplotlib.org/</dc:title>\n",
+       "     </cc:Agent>\n",
+       "    </dc:creator>\n",
+       "   </cc:Work>\n",
+       "  </rdf:RDF>\n",
+       " </metadata>\n",
+       " <defs>\n",
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HPvYxFYtFzc/P6/r16+rr6wveYnZ2NpARrVZLQ0NDymQymp2dVS6X09LSUgg7YsPhcjs8PKz5+XnduHFD9Xpd29vb2t7eDslZ2puPB4dOesDpHfTCPZ8UVyC3KgMDAzpx4oTy+bz29vaCxXTYxCTG94wH1b/ztvoRB7xOmTqEcEXxEilnKr2Q14XAx8oVKRYUh36MTZrldyPk96FtsfDx2y28EwYxIxZ7L+BeGpz2z3xOaGusEGkIgfpBjEgaI+gw1kmItJDC28pns7OzevLJJwM8/cIXvqCpqakQu0AO7O7uamBgQN1uV5VKRUNDQ6pWqxoeHtbS0pK2trZC/R7jUiwWVa1Wtb+/r/n5ea2srASDT72fj09/f/8B/Q1Wd5cdW3yf3FiQPdD0SnGqhrECnM9gxwMXB6wuhGnxQjzpcfvwOEC02Ju5pXSoxs9RlKyTA+6F/D4INDGEl7i4EHlsEMPLtPKlNKGnHU7+eJtdCNM8k5d8OWJwTx1fF8eXbpBcQdI8kSMEn8u0Pvq4U4FeKBRUr9f1q7/6q/rJn/xJnTp1ShMTE5qbm9Po6GjwPr1eTwMDA+p0OuG70dFR1Wo1lUoljY6Oan19XY1GI8REQ0NDKpfL4brNzU1tb2+HAtdWq6Xd3V3l83kNDQ2pUCioUqkcKBKdIG8AOxVDiXgSY6F2RfRapKmpKRWLRW1tbYXYyD2Er8hNg2tpkAoBSHs2k+Su1y1izFB5TBHHAx4nuUdzYYkF1QUUQfRCTM73mMrveRTpEBut2OB4JT9HWpwVezZ/Ztr4uzL5WPicMe4s3cDTujL5cZRhPurw8/L5vJrNpn7/939fi4uL+o//4/9YL7/8sn7lV35FW1tbmpqa0vDwsIrFoiTp3Xff1fr6uvb399VoNFQqlVQul0OCFdlvNpvKZDIaHR1VpVLR2tqa6vV6IJE8lhoZGdHU1JQGBgYOGMS4eoHB73QOFmi55zjK7bsVZrBRpEKhoGq1qkqlosXFxYSrRuFiStQn2gfyKMWOLRtHXDHtnznc4noXUr5DyV0pYssetwmY4OPq1eZxnOgeLi1uiJ/hn6FATkL4955spN8eC+BhmZc4v+PP8Xsc1S6YQcYpnoPYmPh9GH+PW9OgYK/X0+7urnZ2dvS9731PX//61/XRj35Up0+f1s7OToBumUxG3/rWtzQ/Px8Evl6vh/wmxbKDg4NBefhB7oGOo6OjmpycDLV1tVpN/f39ajQaWlpaOvRITJxn13F3zmDFk8X/Loze4Vwup1KppImJCb3zzjsJhYhxtg+aK7ZDojTlSIM7caFjDBO4b3yuW3+/P9Y1vo/33eGbpATM9eciFIx5rFgx85dGGMReKmYEeZ5DLw4XSumQBPE+uULH19K+NKXjGr/fUR70qP74Zz5mbox2d3dDu774xS8qn8/r1KlT2tnZ0fDwsHZ3d3Xjxg0tLCwkoCmU9t7enra2trS6uqrd3V2VSiVNT0/rypUrKpfL6nQ62t7e1vr6urLZrEZGRjQ5OalMJhMIB4oNms1msvrbPQKNxFp1u91Elp5GMSku9P5bOgjGRkdHE4G/C+af9nDv8UFewvvp5VDuYXyivW1HCSTPiYWAc2MaGq/mR/x/PB+xZ/O+pHkhKblZjUNUb4s/C8UAhcTjgTLFc0/73QDEnj6GgWlHWmwUpxWo3Yz7t7q6qv7+fr399tuanp7WqVOnlM/n9fnPf163bt1KyG/cL5/PjY0NtdvtUBrU6XQ0Pz+vjY0NFQoFNRoNzc3Nqds9KHvDI7LUPu/WnptjCdMsmw+MK4YLEG6Z6wuFgsbHx1MHMMbrMbx61OA7s5jm4eKJimOMWLB88tIgiN839piMh7eLz7xfsQCmLSeIf+I58r52u4fLvyl1cSPl3g10wcH9fAeluG1xP2OoH8+Py40/w+fADULaeMUeHPhPrOnjvbW1pXfeeUcf+chH9Nxzz6nX6+mNN97QyspKyOHxDJcXh7gcrVZLc3NzQakkaWhoKOSOiPG9n4xxPk4YprlsZ7H6+/sTm2DEtKYzNw5xpqenQymGW4c0ax4rdRo04Ei73r+LvVGaB0y7d/y/X5fmSdM8lQu/lwq5FffrPWD3+IJJ94LX2Pu5YeDcGN76xiWeBojv4Sxn7PViBXUFQNh9fmOvBJnliCAW7tgg5vP54I28/XjAGzduaG9vT5VKRQsLC1pYWFCz2QznpEF/N1TeTxKs3W5XY2NjmpycVDab1fb2dthzAsPkCp9fW1sLHfbsd/zjXiqfz4fq15gqj105f4+NjalYLIZluy5wdM6D7vhwmMIRD3pMU6cpXppX5YgV2D+L68vcW7jVdWPkEM89d9wP6eHNU9LaFnsAJ2viGMvb6l7JFRihpj/85n78EJu4Nfa8laRQKuOf0Weu9WRtPBYu1P457Wu1WsGAe3wEHT4+Pq5Op6Pl5eWwp11/f/9B1YHtSUH7YjSUzR5UTVBHV61WVS6X1Wq1whoklw2Xo1wup/zm5mbCevR6vcTeZsRFTBpr4TudjgYHBwOciF0lD+DziYkJDQ8Pa2FhIUxGjInToIKf8ygCwc+PrXUML2Ilc6WKPQuHGxMfTLfKnOfsnhsAp6Z9fLx9bgwQdKyyK5kTOq7QaRQ230sPEx+0ud1uq1AoJKoSYu8en+/eBqF35URuHFLhVX1sfHw9d8f4xCyo9z+Xy2lgYEDHjx9Xo9HQ/fv3tbq6+lDhgBsG94DIPW1xQzQ2Nqa9vT3Nz8+H9pBigJkMJV5bW1upgsdEOxWZy+U0NDQUFGh3d1fFYlH9/f3h+7gQlXsNDQ1pdnZWCwsLYV09guk5irTBcmWK3bRbLZ8UHzw/0iCRn+/tR3A9dkBJEA6CcycNmMTYkvtY+iTSVybTGbvYah5F7KAs3g73sGn9l5LeJaas3XC5QTgKhsVwFEXyQtm0/2k/uw05UZHLHSxhgAjxflEhc+rUKU1OTmp+fl5zc3Nh+TheBMravbKjB+4HpGu1Wrp3755WV1c1OTmZIKeYR4+5JB0oEg12wXWogdfJZDJqNpuheG9oaEjNZlP9/f0aGhoKCuVFiExaf3+/jh07pm984xvhXrESuYL4ZKcJgg8ox6M8zVHXu7WM3bYrohsTqGv3Sv6cmFCIBc6FNI6ZvKA0TjnAQPmGHN4PrxVMG7e4797eXu+gUj222FjeeGyA+DFU5HrfF86fmaZwjIMbNO7PuQ5jfXx7vZ6uXr2qUqmk3d1dra6uPkTgxN6PIy0Rns1mw4JANpT0UjHG1b1X8EheL8aBoHODOIZig4iBgQH19/erWCyGte2UTiB0DOyJEyeCqyYZ5hPtihATBXwWHw4p498cjvl9IOK4ymGSe5k4SGUcEAA3Oj5+Dt1cGH1tkvchhmXuLXzjyrhNsVdAONICa1cUnh3DyViwjhr/OJZzdhF58XO97Z5s9bl0Y866I+6by+UCzON+nU5H58+fD0iBOjjvj7fBYyNgoW9DNzw8rFKplNgUxXNpMXIC5gVF8p0zsUJsmOdWxAePZbYDAwOh5KJYLKpWq2lkZETlcllDQ0OhMcPDw8rlDjYpZyBi8iCesDQPE1t4IEd8fnydD6rnj3xw0wTFrwci+FJzZ4diT8ZAxzGWx1duYZk0h3kOtTwPh4eKDU4a/HO47MrhcxAbLzessWd2K0+f+HxgYCAB4dKUnzFgfHxsPEHs4+Xy1+v1wurWxx57LJwb5yfpj8+lzxP3aLVaYdP8SqUSKh+OKo3iedw3/+DBA+3v76vZbCbeGAHdCIygI3TCB5kiPsiIwcFBlctlzczMaHJyMtQ+sTSYTSdii5SmDGneJk1ZYkFwxfDz00gNV7C0IlQGDcPBADMZYTB/MAYMcBz3YJi4n18rHe5FEMctCGh/f/9DsM9hjAfU8bjEMPSosT4KGfgR4oIf9DdWAjcicRtop4+zw2v6EsdtnlZxFFIqlULpDhuSxEpDW9OIJMaL+L9cLic2QXEj6QbHY8VsNqv89evXE1ru2F1S2MfblchrqRgIzt/Z2dHKyory+XwoCaKzZ86cUa1W09zc3JEZ/bQJO+pIU6oY/qTFCbG1igcpbg/X+Bh44MmEkGfgf9ryqJoxh1hunFiMF9PMsRFj19sYjnkbY+FDobgmhvRujPxwyBaPLQLnRAn98L7G5ArP9D4Sl1P8yjmxYmYymbCMfHt7W5ubm4kyoviIjSuKwjhns9ngSOLxe1Tbs9nsQUI2DiKdmubm/B03jMM70Ov1ErtnNptNrays6M6dOyoUChoaGgr3TDtc0NIsbPz8NGWJJ+eDFMUHzK91BYif48EmRwzb/G8fw7QYx2MKBNKTp76U3Y2AG7IY6sV9w7r7NbTLKXTvC4qDx3To59DT2+/j5PDI1xbF88AYecyeRkbRl3a7rZmZGeVyOa2uriYW2/kYpwm+IwoUaWtrS++9914gzcrl8kMoJ3Yk2Wz2cBchD/54YGwRmQwP/BgkNJvOpQW9vd7B/s31el0DAwPq9XoBPrq2uxDFFiQedBcO/y5N6F0wPLiOg+kYBqXh7diLeYzGfXxZPdQt2D+TOXynVOxVndHk8E32/Xva6BRuDJni5LD31+fW4VOsHB6Mc26aQXBSxyEW93QZSpsbn1vux/8sDI09/enTp0MRqq8xAn579X4cHnBf9l5Anvf29gLE84PKnP7+/kTd3+7urvJugdLih3hiuNgTdk69ppWO+EEH2NrYBS4eyHiweZYrBM+I3flRcVYcA/gRM0mx0qQJftp9XFAYE1Zh0t44fpKSVDjnAatj4XPs7obDk8EuSN4Hz/S7dfc+OYxjDl3xfLMW93SxwfO2uXdyIxuPIQYnhsNuADEgx48fD0xepVLR5OSk7t+/H9YWSQpr6+inG8pMJhMUaWBgIFSOV6vVwD5nMpnw1grmhP6i4Hn3QDzMA1LHhZ6MpMNuseNsbyxQbq16vd5DNWUuKHEs45/7hH9QHMV1fngcEMMy/x2fGysZAucYOoZW8fcxdIyf74YIA+VjyTZU3Jvxc/jnfY6F0Gsk4/nxvqNAXlkRs6N4IK/YcFjPc2iHwzRX8JiwyWYPdzhCBtNirb6+Pp0+fTrQ2MTiw8PD2tjYSPSL/hLHFovFkMLJ5/OanZ1VpVLR6OioRkZGQmonl8uFrbkYC/pBArnX+8FOq868xAxWbE388FhKSu6yg5DEMCumvd0yMbCPgmJuzeJz47jDDUE8qD6JseLGipcGP+hbjN/TlNGf4cnS+Fwfbxd4SYGxc8zvQT/jFEpWfmDVqVHz8YhjIYfoMfzzvvo88DuNcYuVIx5z2hGPW2xkXEaQNe9jrVbTyZMnA4U9MDCgSqWikZERzc3NaXd3V7lcLkECkSjmNS+spK1UKhoYGAjpnGKxGBLKvV4vKBzz1Ov1Qg1eNps9WI/Eg2IhxTs5n+6WJh4o/z8NbqUJq7NAcYziDFh8pAlsPPn8ToMPPvHxkeZN4r75c9LiuNiTuQFBmTzwRvAZD4dgeCaei1WM2STaHnsP30/P8zKer/H+eIxKfz1uS0MGnOeV2T5WPk5+TWzw6LuX5SCD3I+xGB8f1/DwsPL5fNjYvlAohPcYuYHm3sRRIyMjGhkZCekaVjVQuIoHcmPJWKNQfX194SV6+VgwYoGgzskxvTM+PITv45gqpl35HRdhxoMcY3gXlNjipVn3oyYs9kJHwblHxVxxzOZC6zGVX+cK6bEa4+8kTZoHcOjNJDrxE0PVbrcb9iv0/ruCSgp5K28Lgh2TEX64oDrt7fDHCaw0b++GgL6xfDueC5cz2vjYY4+pVCpJOkBHVHqjFNJhLFWpVMIuq9VqNdSLUu4GVOzr6wtxEW3kgEChGoJ7t1ot5R3PuqXhIi+BYcKZhDjD7T9OQMTxTqzhsULFg+/W3SfjqBgpTSnic/2eDit80uhzLERHCZcrRxwXeds9juRwyOdjQXLWDRX39Mn08fZEKZQ189nr9RKbLEqHpAZVBhBBfEepTtyHuP8eR7uXcXnwMffv4zngcIX1lEK329VTTz0V+k69pxdVc+/h4WHNzs5qbGwsvKEPZej1emGHoNhAexLeDU0MT/v7+5WncoHBY4Kga7FsPNS3cu31eiGBRQM8EPZXZLqwIRA+YC7AsUDHQhr/Toth4udhuWOlju+Rdr/Yi8UKnea9MCgOc1zYHAU4W+jXxtd4FQP4n4kOQa8lcH3CEaw4PmJ+eC4Fyt6XeIxoj8M9h3KkTtKIF4eX9JO28hYTh62+K5Hfp1Ao6Ny5c+F/SIHBwcHA0jHOeKDYayITjAfj6IqbFjfSfzdG+ZGREXW73YTV4oGsR4e6xWKhBP4OzaMOF24XLrScRnsM4QJ8lLIchbcf1Qb3vPH3Rwk2B+1yRvIoL5l2bxc6/9yvd8XzOCZmtRh/5iY2WuzJ5tXZPrYeA7s3QGBdCegPn8UFt7Sd5xPLxERGTM6k5QpRGn829DLjxHXlclnHjh17yNhlMpmwbKKvr0/ValWjo6MJI824eFUDaQZXLPek9JFx8rbncrkDj9RqtcKO+3GZilsDGujeJ3bX/uMDzeGTgQV1OBjHTGmCedSRpkix4Bx1XjypRz0rTcH8iGM6Z5u4J+Oc5lndYjLRbsC8ba48PnfUPsZCGyc4HQryXOYHROLUc/w/AoURYl5dOblfDKWdocSw5vP5sIl9HKP5M/L5vCYnJ8Pe227Qut2udnZ21G4f7Ds/NjYWoJ97a/e+udzB2//cIKH0PmaMtbeJMck73+4TR2eZXCaLh6OpXqgZY+g4XnDhcFwfC4gL1lFsmZ/PEVvctONRcMwnJM0Q+DWPUjifVIcPDs0cejnc8/IfPou9ZfxMt6icE0OSTCYTPBXXoEDZ7EGdIKjEv2dFgBMdKCaEh687QtB57u7u7kPj4W0nxnOD4V4QZUUeefbx48dD+/DibAC5vr4edkBl/CEQ3ONgMNwDYdwJb9wjOdx0A/KD9ieLImONS/vbvYknad2de7CdFqRiCbCOnimPD2/0UYc/IxbyWDn93LTYy+8XQ9NYmVz5/HDr689yVs1ryugf44NC+fMRhridWHN+Yq/HHKcpvBMYCBPt85oyJx84YuqcGjX64b/jNIF0mCujhMoJJoS/0WgEJfLnX7hwIWGMCEVWV1e1tramgYGBUPgKE+ceJy4ZciPE2GEU+B+KHV4BB5LL5Q43iORDp7ndRTMYrqE0wJUjprv539kc7oG1Ce7xB+zQUULp/6cJsFO3DG4cg8TKEDOLaR6Oa2K6nUmNn+UH17mHiM/38WWCYrgS07mO4d3iulCmxUjeJxfceFl8XH4U54Wc4evv73/Is2YyGe3v7yfuw/UuI3i0QqGglZWVICPs8+3jS5sLhYIef/zxgIjcIy0vL4dV3GygT5sYO8gIPLnPtRt7jAnVJBAMXlfKs8Mb+xD2mJLFWqJkJGcRfhTJB8utI1ibyfAqZRYTwgS6C+eaWKjjOMKVyj0XbYvhTSzkXkx5lHeJvaRDMlesoxTJ2+/Q0wtP4/HmXLeUsEr0KYZyCAbGiTHxOXZhccrcBZbxc0WPi5fb7XbIvxzl9eN4Nx5D7jkwMJAK63zMfAzZ7MShry8nqVQqD+0S3N/fHxQeAxAbClcQh6dx/SL5JvrXbrcPX8aMt+HhfpKkxMAzaR4neeAFT+/WK86BSArnoaA7OzthAwrH0z6QHC6EbuFdUJ0lis+J4V88+S4Q/r1PaNr/afDQ2+PjzVj4uLu38XlxgXQ4jqA44+cZeYQiJjd8LL30JQ2CuUK5J4uFzefGxwtL7l4oTq2Q+PeQAVnw+/R6B28rn56eThgW+kq8tbW1Fcp6nKrHsFMORP/8PbIYcUdf7mR6vcPq/qB40uGGhEwgXscFFs+DBYAO53+0nlqlVqsVXvL0qDou6dDds9bFmR4/jvJQR8U//I7La+L7uXLz/DSrGt/bhetRR6zE/mzuT0KUtiAYLngu8LFHZq48BuM5zCWK4nMB3PG2cm7saREu6ZAO93IkDBsoAyOLILpx4BmDg4Pq9Xph+wHuAXnic0MbLl26pGq1mhgnaHOUMpM5fBE284h8sp8I9/Ox9JjO4bKHJCAfIOLe3t4B2eCwy+k+F2ifcGIZVyAeyvtjms1mIhil4XTcBdYhCpjbcw8xfKM93MOFyQXoKNo7Fo5YoRz6xMrrFjIWvviIBcCVmfsgzDzfz6EdnrvBwvp9nS5nvjg3bqNn6flfSi6LcE8Z9wVhAppxPoaA3JazfxTO+nMcufjeIHwuJQkLN5JPPPFEGKdWqxWu5/k7OzuheltSgHXEYg6lPayBnHBZ4FwvfCVHhez09/crz+B7YEjQGA+kM0kEtDA1KJ03xOMcrJYn7GJB73Q62t3dDQuo4gRm7KHcwqfh8TSYFQf/KDYu2tmctPukeZ80i33U9a5QcZvjgD6OTRAo4Bo4nZ1HPVnb6/XCjjrxs4AvThr5tc7Q8T9K47GskyB4O+TIGVhfIEc7PeZD6fCOxEyc77C0r69Pjz32WPic/UY6nY7W1tZ069atoIx4H/7mmfw9NDSUqGTA+HjuFGOXzR5WbMTeKZ/PH6yQdZfqbBAPQCudRPAAzDf1APfGEAZBSIt/3H1DQMRwKxZct+ox5nfIF1/LM+NYyBXLnxnHSTHciT+LFS3t+S40biAc/jAHjCse34uCse5Ac39LucdMjvHpt8NA94zMh7OZcT88VvLDvaffG6SCwmKEURgUkhhmaGhIGxsbqdByYGBAZ8+eDXO4t7enZrOpra0tvfLKK2o2m0GBvAgVWIfs+n51GHyMCQeeZ29vL1RXMKa+w/De3t4Ba0cj+/v7E8WNDAR1TBAD7XY7QDdXCBdwD1yPimHcyyHI7uZjOORKEgtrmqdIY+LSlMH/juGcGxW8pLcphpyxwMV/p8V2rtxu0Ig/0mJX9xR+fyCWwyFXHlcW7h2PbcxG+hw6xInjHt4RHI+PkwB4J4+HQUB9fX2B9vayII/7SqVSgIOMx+7urv6//+//09zcXChcRdB5Jr/dI8HAMTYcjGG32w2xm8uukzsYgDzuyicPr4AS+WpB3uiM5XH45hOBoMTejQns7+9PsH9gXLecNBzLEQsgE+YKkfa//+1tjANZPzcuq+GI6d40z+TKxcD74X2I//d4CeEBvjE+fE4bY4PGgaB7gXBan5wo8O9iJWW+gXyxknKuK2jMzvl7YJkD+orXoG9ANMiDXO5gj4ahoaFAdnU6HX33u9/Vt771rfAqSp6P7FINTpkQRsvHEqRFMTZ1f1RmpLGgjHE+n1eeDRy9lsuF2IMzXrDEIDuPj7WmIfzv1tytktPnHpy6FWPy/HNXntjT+N9HeSmONAXic4csTgZ4oO2K4+3zdjk75obA+8DYePrAKVvpMAURK2QcixBvuHFziMXznKyg7R5ceyzmyMEJDIQNYd7f39fu7u5DSsA1IJ64sqDRaARB7uvr08jISIBeg4ODqlQq6nYPXpKcz+f14osvJgzI9va27t69qxMnToR3vGaz2YQCAX9dflyh4jQNY+K5qZj4YuwD03ru3LmeW984ZqIsfXd3N7FS9ig8j7DFRAHLewnWWKbr+SmHODzPy17iiaeTRylVHCz69f47PuifC1gcs8UxhEMzZ73cAMQwius5n92VPHZyBfDnxjEK4+SC721m3ni2J2wxau4Z+dvjsE7n4FUp1LchaDBlbDhC+500QKCLxaJKpVJijsvlsqampjQxMRFWvaI4ICFfGhELMvff29vT3NxceItfqVQKaIqSId7IF8N3n0uMgedT8/nDuj6PSxPQzm/M4IJXgXoxNe6D7QoYQzuUcWhoKARu9Xo98P4x5csPE+fMklsTF6A4uI0JDQ/MPbD2PsSehcM9kNPMMQR1hfBJie/vz/AYDkiDUYlJHYyd38PHA6jjHsM9lV8DhOE7P89jspiVYxz9nh6c8/6reI5QBDzM6OioxsbGVCqVVKlUNDY2FgpMGUdfXOiK4wbBx49Ki3PnzoUNI1EiJx7idEMcjnBOp9MJisNPqVRSX1+ftre3wxgzR3ncr+dtfAWhpMSuMG7Z3Sq7ANMgBrBYLKrX64VXtHvs4+6Vv2MrzyDyjBifxnGLT7zjW/+J+xMH/374s32y06CmGwX3GDFcjJUh9gjxGLv1j2lyxpsxQenjKn7a5PAbRXVInRbTMSeONIDkKFyslHzHpiSXLl3SpUuXNDo6qlKppF7vcM2bx8c8L5PJhAWMLgduEHw+9/b2wltPqJKRDjc8cYjJDkCOFLLZrIaGhoK39HyTG9RSqZTIuw0MDBxsxxVDI94uwQ2dKo1zIAiEew1n+SjN39raCrurovUuWLHn4bd3wIWcwWNA/NnZbDYsGUC5XTCBSHG7Y9cewzEPoGMPzoC7MNF+vG4c5HMPt/4oghsmp2U9jmIyfRy5b8zU8TdKEm91HJ/L82kb3zEGvqgwmz14l6sbNVAObX3sscf0Mz/zM6GSgXs0Go2HhNlDBjcW9Jc+xG3PZDKB/h4fH9fu7q7W1tYSiVee5fGdx420BwcgSfV6XZKCZ+NeOzs7wdPlfUN4gjzc6u7urur1eoJxQwh9Ml2ZvFSIrYlRIgbGBcvhDELKIMZJYQbUhdAF3P+H0ODw8hgGnnPdG8Qwz5UjJgr8x2Gdb37ppEEaPHMllA53VeU6x+kuYP7MWAGcoqevbgCJLYCPjF+MMJyAoPQGz9HrHaxSLRaLmpqa0oMHD3T79u2AaBzWj46O6tSpU2GDRWro6AtG2/c5ZB78LeuMKWPHM1wu8HKNRkPValXZ7OGW26y5cqjuMTqfMUbNZjMYN/SEew0ODobxbLfbBwlZlMKLTev1eoAACL677RiCENeQUMtmDxaLsX0s1yEEDjcYBJ4Tl/S7EHG4Qrkl9oAcz8h94/iJvrl3cqX0DQAdW7urZ1z8PI7YEMRkBIGqpyC8Ns0tNHGqw1hPlPs8eIkVP/GcufeVkjvq8jnXuPLzGcLa6XS0tbWlbDarS5cuhfs89thjmpycVKFQ0NTUlM6ePauxsbFE/ob7ArVohxs3N7S8VyubPaz19C0Q3AAiB6VSKcBMPBBzCSTGO7rcIaskZdlPgvn1MiFJB1sWuyJxMR1yBslxqnSYHeceUI7SgTtsNBrB/XvA6BPjeNgtrguG/+ZwOBjDTJ7lCoBwxrAAi+LQIfY+TBJCjvAiFHHM43Rp3DYmyL0T8ai/JMthn1PaCIi3xQWcMXWD4bEoQuVjGueLaLuXe7khwQPu7u5qYmJCExMTWl1d1ezsrD7zmc9oZmZGExMTGhoaSgTk3NvnyNFI7Am51mXPjRb36na7Wl9f1+bmZpgvoCe7CzUajeBNMV7eHsbQUYukRN2fe2eMYCKP5NYPwXKM7Ydv6A6ZACQkUNvY2AixleeavObKM+90wp/n8UUMh1zIXABcKRkkhz1OZQ4ODiYGttc73CEpDnh9YKFkXYBpgwsC9z0KXtIPzkWJ3ds5Y+QexquaHdI4++SejLFyCJOmqG6UPD7zseMYGBhQJpNRrVbT6uqqzp8/r3/r3/q3dPz48cRanVgJvL3uMZl7N2a0zw0I3sPlIJ/Pa2xsTO12O+yJx7Ic5JIUDuPh4+mIxY0+v/GehCu9Xi/A9wDBuQEBIi6UB3BDDg8MgXPw9CgRy4Odc3dIE1vO+Dl+Thz/cHinfdL8nvztg4Gh8BiEOMG3rIonmGuxht6muN20yzE/Xj/2tq58rugoaazIfp+02ND/dq/kFtgNptPYnofyANz76/RzuVwORnR/fz8kT4FfQKlYebmn39uNl7/e0o2owz03BPy9t7cXCnhzuVyowyuVShoaGlKn0wmlbY60fC2SeyU+dyq+2+2GcAHiIbB2sRDScfc+8UQSf5CwRYl2dnYS+RYGn8MtdGz9GNSYvYo9kAsvn6exb9zbg1cGmWQvr7J0b+leEoGLBdTzNT5+MVHQbrcfCkw9sPe+cG1MR5OioE3e76OOWHiPUgx/rnsvPk+DqVNTU9rb29PJkye1trYWDG+9XletVku8LDqOf13e4v67QfFYLZ5/L1NymEjpmoceGxsbGhwc1MjISHgWRajcE+SAIcXr7e/vq1qtSlKgz/keOQrxrq8PAVtyuEByYMGz2Wx4k/ne3p42NjZCgZ9fizfioZISFQ1uNf08rw/zAXfligPh+IgFLWa5/LlskuHlOSQ6PdfmECqOQRwauYWL46k49nCP3G63ExQrwsThChgrrN/Tl1DgbdrtduL1mVhV95ruiWmDK0G73db09LRmZ2f1N//m39RXvvIVXbt2TbVaLawNcm/sHsdhlc+neyPGgvPiOkL3+JKCd4AhBNqxzRz3BeIBzVB+5pDyJr4vFotqtVphHF0umU/m6Qdyc2j9vUOx1XLYgXYWCoVQwo4SeZ6D354dp3Ex/UwsQHDplcQOqTwuiL2l9DA9ngYpvU/eVwbY2TMXYPrDdbHVR6g9HnHP5XurUXLCmNFO33daUlhtzDUeQ9AmZ5v4OyYGaJd7Np7jEBLhdcjn8BJS6WMf+5guX76sEydO6MaNG1pcXFS1Wg3K6kaAseJvN5LujYhR8cLxfNPWGHYzbuVyWffv3w+Qkn5tbGxocXFRp06dCm2LVyc41IOhK5VKYXyJi5EB2hLmkxv48gmnWZ1fz+Vy4ZUXWNitra0w2L4tlO91xoBSVc6A+ZoQBhVsu7+/H+KuWLkdhsRKFFsvBtutmZMQDqVoB5PO7jEekHppjQuCw9k4JxYTNyxX8eudiHDrx9h6cMvh92ecnDyhXfSHNsUw2AkZH7cYGvZ6Pc3OziqXy+nKlSvKZDIql8t6/PHHderUKW1ubobxJXUSGx3G3ufMIT6KDDoiyGd+nIF0OI7yXblyRZ3OwYb6m5ubajQaajabWlxc1NDQkMbHx0Md3/z8fEKRHFkMDAxof39fxWIx6ALe3IkInEp49WVcBuQCjvviZU5Qn7z8loArLb+EwOCNXMhidgahpfjRGa44FoljIT9iRZIOy5w8DmOCnQTwazyn4QGnj5d7XGfd+D+Glw5vYsbP40rGw5Wbc2MqnPP9DfSMXzwmfnjbvDQojanj3iRWJyYmEu3x+YuVx9sZK5I/hzH2/bsZ62azqd3d3bC8x/sfe3EUvFwuB8Xc2NhQvV7X0NCQRkZGVCqVNDIywn4LYT5iI9hqtcLuQ6AKznXiI4/1iGMbnzDebEaZOvmhOHD0tRtxvRbBW6/38Pt7nJt3vj8WAhrt1/qgpgmMK55bvrRzsX5AqJi0IEB1xXEh9GDYFcNp/JiJQ5E9N0R7HREwTrTFvZcrjU8yfUvLnbkh8bFKGyeH6KVSSc8//3xifnhunNJwCOZj7IeTN5VKJcHyevwKI1ev1xNognYxBiiOb6ecz+c1PDwcvBxVD0NDQ4kx4n4QFz4Hvs7J5Ttc53AB4WGCyOiiSJK0ubmper0eIIcHqnHC0wNUgjbu6VCSII/197GQu2XnWc7auFI5VHAh8nNcSOg3hiOGjZzvsUf8cmQmE3jhyuN98IOxQQgoqyIe8r7QBycEHGoSN3gsGj+De8VjiQFwpcWgMLck6C9cuKBWq6WnnnoqAfmkw2LaGKo5TExLgrqR4C15HmPF162trQWFQaF8S684FHACjb7gBIB4Pjbedy90xShCrj0Es3mAB7RcRMYd9oqK2vgNCP6weCKxDpISCS02rMDjZbPZxAumpCTM8viHvFBctMlguyX1c1x5YijX7XYT+bPY4vikutFwiEps4x7Q28JYu5dEiDiwtJICxI2TtQ73fAWpxxFxgI43dYXzMXDoGMN6rnvsscc0NjamycnJh7w+htFlyQ1JHBfFLCHtxmD7vT1G4u0pvuweg+ZGAuXY29sLyeN6vR5QU71eT9SD8n4kJ5tQeKe+Yas5j7nKu7fwgBnCgE765o24UddK12wqvnd3d4M1o1SD7DMNweNtbW0lavuYgHjCstlscLsxUREfbs3iuIl+xIlg91j8z98e3MYBqg+0B6QeN8aC5edyEBvExZkcDsmcuaOdzAmC4LDR+8SzvP/uSTAozGOpVFImk9Fzzz2XiNHceCLgeACHntzXvYVDbDxF2uHzQDyE12KOgV94Z54xODgYjA8sM+PPOLAAkcWAcYrB4zXmg7iJNuQZSAaOAXbLsLm5mdjshAQssYzTqu5O3YXCy7OoD+tBqQ4ZaWeXnByIA3I6jiVzKtMniwHwHyY5Vgq34u5V+I5CRYiXuFwIwSYojb2RCyf/03YOV8JSqZSIRZ2ooH1ey+hK5zDNYzKUi/lzb+KeyfvTbrd19uxZ9Xo9PfHEEw8ZOeaP8+O4yL10LGsuMxsbG6pWq8Hzeh88NeKxst8/jgm5t5MZ/sIxKh2cMOF6lxOXG5dDH4d8DGXwEOQ5ms1m2LElvjgOOHG3XmUL09dqtbS1tRWEJJvNhrzK2trakZ4h/gwFZMLc0tJxVyYGMz4cg3vMJSU3W/FAnup44hmE0GsTfSxjeIcHcGYvFjqHoowfr1aJ++Lw0JXG2+xW2xWOfvLMWLhpG89+/PHHdezYMdVqtYfGEkXyefN70A7Pu8XCmcvltL6+rnw+r5GRkRAvxqQBihDPZRoi4eB85s5TCyAmCq5JeXi86vFv/Jxg+N29c4NcLqdms5lInPpSaEnBGzjmp3HEML6FF0pEx3GjUOienSf28PyHTxKCmNY5j2n47StCffIQuDghiZfwGMLfUcp9YxjD+EmHuShP9LklBf66BXcFok++Ww5tIWblcE9Ov519Q4j9vu7xfaz8YNyGh4fV7Xb1/PPPP3SOzztJ1Dj+dCXn/7Sj1+vpwYMHWl5eTuz64/OVzx9uiE+c7nP+qCNGIdlsNlDkOAFPP0CKuRGjH8hHaJd0WCpO8SkWxosOOTzIzuVygQp36MTqQjj37e3tBBOGZWFr4zhuiWGKD6RDFaCEb2iBQDvzUy6XEzkh91ruCT03ECs1VOne3l4iVuJNb876IFhcDwTGYLhBiiltzomVxYtKHeJKRwfyMX3rwkC7ODc+fP7PnDmjwcFBnT179qHznHn1+XJIFStr7FXjuJSwYHt7O3F/zgeSkm8qlUqq1WrBqxyVF/TD839OlKCYnl+FxUM+iO8T9/PAk4mCJPCBwhr4y3qBC+B4LC2Wu91uq16vB8aIDpTLZQ0MDGhlZSXheRhsDxiPUijasL+/HwbPJ1dSIn6BecEwMNgOy6TD1zm6x/U9o0kIAu0QSO8Hg47X5PCcD4lAFIZneQkLfel2u8Eocb4TEtLDWwB4bOdz5fGWJ3N5jrNotOHUqVO6cOFCYFz9oB2Mg3vXOK5gXKRDRaPfHs+6B0AW4rwiskH4sbq6GuSLkh42VxkcHAwbl6QdjCcy4QxrbPD4O4bUeVgJX7PhuQynev0hwBqHB8A51iXt7OwEjI3QkdilShwY6bEaEMuhF8/wmIhO7u7uBsWFFMArxBbKd5ORDiEqAuQQk99A3L6+PtVqNR0/flxTU1PKZg+WTS8tLWlpaUmNRiMBExlwYINvUIL3gSxgHtyb+eTFJVFOH8eC7WkHN0Ien3owHeedaG+n09HMzIyq1aqeeeaZhCL4wY493Mfhj5RUJP9BUB1deH/8eg7a4KkDh7L7+/taWVlRvV4PBnBoaEgTExM6fvx42DfP7+f5KIeguVwuGI84buZaxi2PF4FW9ow+8IHBZ3BoMAdMHtBQOqh+IHELDGQ3GQYJIXMr7gyPB8kuJD74eESSa7QfFxzneeI2Y+XpG0EmAp3NZlWr1TQyMqKTJ0+qVCppYmJCY2Njwcv1ej01Gg29//77evfdd3Xv3r1wrUNHF3z38kwaHswtonRgPOLFbH5PFDHe0pjPneqNhZT7o9TxK33Onz+vsbExzc7OJjyB3yfeczwmiNzY+v8+rzEycCH3+zisT7uWWlBP+K+trenBgwe6deuWLl68qFOnToWNTZgDILAb6rgfaTEecprHE3W73UBhokBpGXOHBM5GoUi53MG2xp5zAjayqxDfxWVGnOtW3Bvs57p3xMWTA3DFYKVuf39/qAJwWCcdunYEutfraWhoSOfPn9fJkyc1MTERWMz19XVtbW2FnZZ4xWI+n9elS5d05coVzc3N6U/+5E+0uLioXC6XKH71DVEYN2rJYiF3GMb4+Jj54ecEJukH8+bLQFx4/f4kKuOc1vHjx/XEE0+EHXbZj4PrETCv1YzZu9gj+XXSIeWP0vv84lU9BibG4xz6yRbDXjAL67y5uanl5WXNz8/r5MmT+tCHPqSZmZkEAkC5aDO/Pf6NQ41gEMH61LjFF7nAuWVx+DIwMJCow3Mlcm6elZS7u7uJbb6YAIdgjtVjT0QHvJ0Ovzx28LIVf0s1E0cbobRnZmZ08uRJXbhwQWNjY9rf3w8ZcQwLlcUwm/v7+yqXyxocHFStVtPp06c1OTmpt956S6+//nqi3b6JBp7MPY/DMfoMWYHld+F0xo7/Y/LGvbErigsxCsXRarV04sSJsB/dd77zHd2+fVsf+tCHdO7cuQQk9uSzK1Oah3E45PEHMUync1iShKHh/jwD8scRBugDI4gcNpvNUEywu7ur7e3tEPfu7++HpRKSEiVBriT8nfZZMFp4GB9EtzYxro7ZM2hKrD/WPe2tBcRDlAfFg+k1ZTzf2RX3lm498Z4INPDOLQdtQlnj4Lfb7eqTn/ykrl69qv39/RDEetkKwnPnzh195zvfCTGRY+yxsTGdPXtWly9f1kc/+lFdvXpV3/72t/Wd73znIY9P/OhxjSsRB8KKcMEAcl0MNRgTN4AIdxzjxOjCyZDLly9renpapVJJKysrymQyev311/Xee+/p2Wef1fT0dCJO9nF11jD2Rn7A8JKjA5LjXfD2xJe+R4jHuoytpFAAQF86nY4ajUZYGYvhvXHjhqampjQ2NvaQp3bj53Ph0NuJrzzBuVt8YIgPtmN7hJTttzKZTKBL+XHXiML1er3grdzzuKWhsT7xvsuoQ0AnIIAvmUwmFBa6IEJjuqcD8nW7XZ07d04XLlxIQLCNjY2wD3Umk9H8/Ly+9rWv6ebNmwEmAqFQhOXlZS0vL+vdd9/V5cuX9aEPfUif/vSndf78eX3uc5/T0tJSAnoy7u6xaTNCwIESsJWuU8FuHZmzmPqNPQLXx2MiHaQMZmdn9aEPfUjLy8sqFosaHx/X6uqqtre39ZWvfEXHjx/XuXPnHiIK3Nj6/37Q92KxqNHR0RBHcx1kFl7fYyKMsq958xjTFcHjVOrpCDEILxqNRmCTff+GuA8+Bz5/kpR3zwGMILkW06DQsigbkG5nZ+ehYNUFHmvDdz6JnEOCjcpap53J7rsQSIcwCco4kznYadOTbBAIaZYewRkYGNCFCxe0urqqvb29QN/v7+9rcXFRzWZTt2/f1o0bN7S9vZ1Qer+fpPD6xM3NTb366qv6xje+oStXrujjH/+4fuEXfkH//J//c7311lvBc9MnvJp7UoSAAwFypXBY6MSKK40rrN+L7+LP2+22zp8/r3K5rFOnTunatWuSpJ2dHZ06dUrb29vBYPR6PY2Pjz8Uw/pvRzJuxRHqSqUSUIvDUuYQhAAhQBzs9Z3xXn4Oxai6IEXRarUCMUbsvL+/r7W1tfAGSryWcwWMj/+NI8rH1gjNdi/lWJtBGBoaSrzWnc7HgsC5+XxeGxsbiRjMFY5BAWo6rEOgGGiPmVwpvIasVCppa2srtJs4wQNUgtLp6Wl1Oh0tLCyEjd4zmYy++tWv6s6dO8ErQqjgQQiCmUxnirCwnU5Hr7/+uubn5/WZz3xGn/nMZ3T69Gl96Utf0sbGRsKzxvEL0JPncA59j2F3jOM5Ny3H5nGpx0j07erVq7p48aIymYxWVlYCGzY/P6++vj4dO3ZMzWYzwQzGSMaVCXnwdqHMXt3tsTlt500myExM1yMvXkXi9LqHB9wfmcTQAiO3trbCuigPA9zbcxCDPXjw4KBoNbZSLqRYda9X8goIilBdARySudfyCggEMIZuXlpDZzifgWCCfCAZcGK1SqUSiAcmDyqeeIljamoq7BZar9dVr9f1+uuvh+XJnvdh4snToPxuTFw4Gc/5+Xn9/u//vv7qX/2rOn78uH78x39cX/jCF0J9GX0itomJHTydexmgYBwnMeEeWyKYfi73dQjZ6/U0OjqqyclJXb16VYuLi+rr69POzo62t7d1/PhxraysaHNzU5VKJaw4ZX5RbqA+Sxi4txMk7lHdynMgT71eLxEjpcVk/hn3w3v1er0Q52IAnelDDh31MD4uz9wXcu769et6//33KSzIBwvh+xHEMRM/KIeXEaXhUgaPAlhYOu8kAwec8BwWDEp/f3/iWocF7m24rwtauVwOis5EovD0p1qtamJiIuxPnsvlNDc3p8XFxZBf8eXFeEcnaVCitLfQQWv39R28RAujMzAwoOeff15f/OIXE7WGTLQjBCYcAXCqmcMtucezLpzc0+MyZwoRrhdeeCHsoPrqq68GJe7v79e9e/eUy+U0OTmpbDYbFn1S3Y/SAZNjqpiDMWLVK6kLj01oo3uqWNni2MWfhfI5qvK5cXSEMnmsHXsj2rW+vq5bt27p/v372tjY0OrqqvJuBb3RWGAG29+21m63Az/vnsgnNJfLqVKpBIjVbDYT1gjr4MlgLxyEnQE2OLxzwYjZLciPZrOp4eFhScm1JygR8cjMzEyoxKCw9t69e0HZaRtxkVtuD0YdghK7eelQf3+/PvzhDwdyg3Ngm9ybcriQQ+/G4+EC5DR0TDa4sXFo7MqWzx8sJT9//ryuXLmiZrOpubk5jY2NqVqtKp8/WLK9v7+vO3fuaHJyUtVqNcSzIA/e3uDxnAumK0C329Xi4mKAWx5nxTvz+vVurGOG1u9PO9Lq/LLZbPBw5MfcS/p9mOvl5eUQL7/55ptaXV09IEviso7YTdIBt5Zw8o5NY2qTlzx1u93A1DmEkw4TuZ7LYABRYKyJMySurEfRlASVvB0Oxs9jwmz2YON38He3e7jdsk+aFzUi4B4T8dyJiQmdPXtWb7zxRqI0qt1u64knnlCtVgtj1G639e6774brsYZ4QGjhvr4+jY6OamtrS/Pz86rX66GtroAOv13ZaS//uyHzqgfuceXKFVUqFZ05c0bNZlOTk5Pa2NjQ1tZWqOZoNBqamJgIFDNzAWMKsRTXTLpgejva7bbm5+dD0SnFqK4wHuc96nBFjf9m/uMKl1hpY+/G9aurq/rWt76lr33ta1peXtaNGzdCHjVPh1yJHBo465bLJau9nVr0YBFF6OvrC/QlAwIOJQ+AoDHgzogRBLrCoswejHqlNb/BvAijZ/ZRgkqlonK5rL6+Pg0PD6tSqWhzczPBnnndGwyfs5gc+XxeDx480MWLF/XZz35W3/zmN/XGG2+o2WxqdnZWP/IjPxLancvltLGxobt37wYj1d/fr3Pnzunq1at64oknNDo6qlqtFhajtdttvf322/r85z+vN954Q5ubm4ndQmFTe71eaKOzd8wVqIP2M44Ymlarpenp6bDZ4sWLF7W9va25uTmtr6+rWCxqeHg4QS74s/r6Dt5NhFHycXKvEIcAuVwuBPsoJWiF5RR4FuTAmT5nBtMUy6GdfxeTEzzbq0B6vZ62t7f1J3/yJ7pz5462t7e1sLCgnZ2dAGnzbpHimwO96BBbIrmyoeUcJGipuWNZOEwgCgIUImbwYNOtlrNVwCIvPCVJ6ZDGlbBUKqlYLCbgHQPLXhEodqvV0v3794PSuvXy2MxhEd8TK335y1/W8ePH9alPfUrnz5/XV77yFX3oQx8KuSh2xHnnnXcC6zU2NqYXX3xRv/ALvxCoZPrf6x3k3jqdji5fvqyrV6/qlVde0f/xf/wfun//fkhuMwZ4X66nryhUDI95BoaHsqZOpxM2yM/lDt4mvrOzo+XlZS0uLqpSqYSE9ebmprrdrmq1WogbSWf4eHts67LG3OAVfLU0YQcBvt8LIwOcHxwcTOy86s+KvYwruMtbs9nU0tKSTp48Gcas0Wjo3Xff1fz8vCRpbGxMKysrQfEGBwcPqr8dPmEtgBpusXyXlqOgFjkh1giRF3LL7h1MY/tyuVwoOXJ3jGLjKWJmz/EzCWCUCZjptD7fc269Xg/l+Cg2gwW0dUPikImYrtfr6Z/+03+qZ599Vk888YT+2l/7a0FIiYtWV1d17dq1YKxeeukl/eIv/mKI6Xh+p9PR/Py8vvCFL+jb3/62yuWyfvRHf1Q/9mM/pkqlov/2v/1vQzzC4cIbH44+mAv6goLNzc3pn/yTf6L19XVdvXpV09PTGhkZ0dramrLZrCqVih48eKClpaUgvJJC1TU5m3gjGQ8BpOTCQv4G6dBW+lIoFMJGjdwfdhcljgkJVjQUi8UAFb0kLPZcHK+++qq+/vWv6+d//uc1OzurlZUVLS0t6fr16wm6n8JYypXyThHSaOCTl6NQDEgHweZeWsSm+uz8H9fv+YRy3xjW+SszfR8AlCbOZsceASaJ/3d3d8MkeJ6DfjebzbCEZH19PSi+TwrKjjI6vkcoGJdWq6VGo6Evf/nL+sY3vqEXX3xRly5dUn9/f3it4uuvvx7ixhdffFGf/exnwxLuXu/gjXM3btzQH//xH+vVV1/VgwcPQgL3zTff1N/7e39Pn/jEJ/TzP//z+uVf/uVgCJwM8jIq9wAOm5xs8Hn5xje+odu3b+unf/qnderUKT3zzDOamppSo9HQ2tqaTpw4obGxMW1ubgahZlceF+j45Vz87efE8bWjoTgNUKvVEhtF0l7ms9c7SJOsrq5qdXU11NixGHNoaEilUilUsWCsOR48eKBXXnlFKysr+tznPqfZ2Vm99dZboW2MM0Sbo6i8DyQeCOvv5fXO6qXFUwTI0N08jAc5zOC3r8nPZDIqFouhohqhByqQhXaL5hQpz3Ahd2qd9fjOylCCwkrM9fX1YOliihVr6LkZT6Y6dIEN2tvb0+c+9zl9/etf1+OPP67HHntMpVJJy8vLymazunz5sv7u3/27qtVqgZrf3t7WP/gH/0Bf/OIX1el0wsaI9Hl/f1+/9Vu/pRdeeEEvv/yyfu/3fk/vvPNOaBeC6bEkLyWO4z7GyPuLZ3rw4IH+/t//+/roRz+qnZ0dnT9/XqdOnQoK5XPvpT2xkYmPmML2+zj0Qz64rxNAKIxv2E+7d3Z2tLKyooWFBXW73fCKoU7n4M0UQ0ND2t7eVqvV0tmzZ0OdX6/X0927d7W5ualWq6Xbt2/r9u3bevDgQViBS7tI3qIT3W73cBchBt334/aJ9LghhoFALhQgXhbggsbfKAj3cE9I3gas78SEC0ScN3EK2ZULi4Q7RmA2Nze1tbUV/l9fX08UhLrlpO1ezu/BqXTI7rmXlKT79+/r7t27evXVV3Xp0qXwXtVf+qVf0pkzZxIC9Vu/9Vv6zd/8zdB3xtTzVdeuXdPq6qqOHTumn/zJn9R3v/vdsL8A5V1gd16zQp4MxSfV4EbSDwxKX1+frl+/rvv37+vEiRN6/PHHNTMzo3K5rLm5ucQYxXOSFg/5/xjtNNYVBe10OoliX08RkFqAoSWsmJqaUrlcDst5JIWC5fHxcU1MTKi/vz/EOSMjI9rf39fNmzdDVUyxWFSvd5DIhXmGKvfCAeY55JFcGGgkHoMOoTwezHo8xWTA1MWH5w4c8pGn4j6we36Ou3ACSrd87o28+tsz0v39/QEaEsTz1mv65S+NdlaKzxBEz+VICvunuUIhGMSO+/v7+uY3v6lyuaxf+IVf0JNPPhnOz2azWltb0x/+4R+GFb/+hjn62O12tb29raWlJU1NTelDH/pQeDZWu1Qq6amnntJP/uRP6vLly4E9vXPnjq5du6bXXntN9+/f187OTijYdO/N/OdyOU1PTwfo++Uvf1lf+MIX9Jf/8l/WRz7yEZVKJTUajQQziFfxuMXnx+PqmBZ3r+ay5Tk09mjAWPt8AVnL5XKCifPEOciHt6vj2VZXVzU3N6f+/v7wTiSUkKQ+Bt7nhBAmz5cIDo1xj+ECGyerKF6N33bntDSd9IoALz1CCcGxwEmvuvAiRbeePjl+ONVLrMSaFwQ3l8vp1q1bYTmxQwy8ZozXETLa4IykM2c8V0rWknW7Xc3Ozuqv/JW/Esab9v7O7/yO3nvvvaAsXtaC8GWz2bCcP5PJaHJyUrVaTRsbG6FdL774ov7D//A/1PDwcBDWWq2m6elpPf/88/rZn/1Zfe1rX9P//r//72E1LxY9FnhSBENDQ5qfn9f6+npo82OPPRaY1NhzO0vmdHvMlPG9/++eDSWHDfa9RVhrhJdizkulUmJ3KRAVf4O0eAb7LUoKSsiGPYuLi5IUUI0TVcxxvV5XkEhwPZ1wj+Cwzl00FoIJhJ2K6Wz+9gJPXDXX4ZWIRbhHDN9QSC9A9fs7fOl2D3d9RaBHRka0vb0dFustLS3p+PHjQWCdXXRmkO+h6zkHi+dCgqGQDvcM9/F57rnnwtscgFXvvPOO/uAP/iAIA+txwO944k6no9nZWR07dizMS7lc1sbGhkqlkl544QX9R//RfxTeUNduHyztuH//vlZWVkJl98svv6yzZ8/qV37lV/Tee++Fe7mhJBdUq9XUbrd17NgxffSjHw1FnawfYoxihYhZwqPiJ681dIoeJWNHKt/ui/F20gtY69Q753BfnATkGW0HRbGCu9Pp6J133tHW1lYw8tlsNuQlnflrt9sHMZJDKkmBbfNqA3dpPJxkrStZHEM5uYCVj1k4Em7xrqWukHg8t3gMvCswE+Nrdvx6J1KYwDt37uj8+fMJNtIVCA+GYrnC0CaPi5wJ5TpXtMcffzx4rkzm4EVY/9P/9D9paWkpbNbiQu05sna7rY985CMBw7vX/8QnPqG/9/f+nkZGRkKA/lu/9Vv6Z//sn2lrayvAzVKppJ/7uZ/T3/ybf1P/+X/+n+u/+q/+K337298Oz6IfkvTWW29pampKhUJBzz33XBCikZGRBFERk0kcntzmfz+cvHI47vcjVxQfyGCv10u8CCw++C6N6EJ+gdOM8fXr17W/vx+2+eJwFOW5uXwsBK7dNAz35y7XBYrPgTg02BlBVwqEH02nYZISgSTXxcwS/3MOQssPSuQMFVCSHYvwVO12W1tbW8Hyjo2N6f79+2FMUC5fKBhPJvfHKjNJHmt5UeqpU6cSFvyLX/yi3nzzTTUajbAHBP3KZDLBUu7vH7zT9Md//MfDhNbrdW1sbOiTn/ykfumXfkkjIyNBMH/rt35L/8P/8D9IOiRipAPs/z//z/+zjh07phdffFF/5+/8Hf0n/8l/kqiRYyyXl5c1ODiosbGxYKDS5sQZvDguitMJyAzXIVMOAV0h0pTDP48NlR/exhgyokStVktra2vKZDJaXFzUrVu3VCwWQw6tUqlod3c3FOI6cwvKCns2QK16cSAPjpXAlck7A7ceu3SHOk4w+FJhNNytk5TM0fjfkhLLnJ0M4cCiu/eiv9ns4dLzbrerO3fuqFwua3R0VAsLC0GA3YXTXw82UWj3xngi7y/XeAIabP75z38+3MeTy+6hYeF+5md+Jigi7Z6dndUv/dIvaXR0NMzR4uKifud3fidcH7c5m83qD/7gD/Sxj31MzzzzjJ577jm98sorCZiayWTCBjClUinQ87zhIS0eiqG4Q/z4HFc4PneB93jd42W/DzLiNLorYtr5PqbISafT0f3797W8vKzJyUlNTk6GMq1isahGo6GbN2/q7t27CZofEiKf5o2AXi6YMW3psQ1azbkkBqFYvZN0zilzfrDkWBhXMtoYB+goiw8ifYLtod0eX8HeEMM0Gg0tLi7q9OnTOn78uObm5hKK4waDwyGfK5xb6zhP0mq1dO/ePT3xxBPK5/O6ceOGvv/97welpj1+H/r6zDPP6G/8jb8RdnftdDq6du2a/u7f/buampoK/e90Ovrt3/7tECi7oNN/2sGK4JdfflmvvvpqwkgODAzo4sWLOnfuXCBs+vr6tL29ndg0JLb4aUoRs3Rpgu5GkP+B7STfMUC9Xu+hZeFHeaUPOhYXF/X9739f/f39unLlSjCow8PDoRZzY2NDm5ubunPnTiAvSB2USqXDt5qjSI77pYdfxYGVQBFw9zEZwLXu/vB8VIYzGGyDhFV3RSHoh4JNG6iYRpUO3T2KWSqVEpNFfEEA39/fr/v372tyclLT09Pa2NjQ3t5e6GOcZY+hCfkFt7QOK9yafu9739NP/MRPSDrIMRHQ0i68gucpyuWy/s1/89/U6OhoePatW7ckSU8//XRC6d566y194QtfSOwr4W1y2MzzHn/8cVWr1bCqWDqsiRweHlYmc7A0xd/CgSfw4J8+OCrh+fE5Po5+ONGA9242myF+9FdR+vNcHvx4lHLt7e3p2rVrGhkZ0eXLl8OrXanjo1g7mz0oj8LoOlPYarWUd9YD3O1C4o1xvO8CQoCW1mC+Z7BJmkkKE0Jc43Q7ntGZGae0iXk8lkuDhhgK1jsBEQhSsS7QuNeuXdPjjz+uCxcu6ObNm9rc3AwskJMuMHFA2snJSeVyOS0sLCQgJWPKeOZyOX31q1/VgwcPNDMzEwTSBYdrnS18+umnwytV6Pvv//7v66WXXkoI0/z8vH7t134trK3ysiiHXdlsVqdPnw40OvEhjBX9vXnzpra3tzUyMhJews38e8wRxzj89r9jrxQffo6Pt8cyxI94Zeae8fVY7YO8E9A4n8/rQx/6UPA+oChY6Ww2G5Z3SEokedvt9sFLqHFR8QNito0J9cptvA2H18pJCsLLdyz+4h4ImpMFnjFOc/8+URzODjpr5rFdu90OdVrO7rEUnv7s7+/rnXfeUavV0hNPPKHZ2dkEg+m0vPej2Wzq8ccf18DAQMKj+LWQHLdu3dLv/M7vqNVq6eLFizpx4kSgvBkr5qXT6WhoaEif+cxnwkRSD/fee+/p1KlTiQqAf/SP/pGuX7+uTqeTSBJ3u92QvOQeH/7whxMKCyR3uv7mzZv66le/GozH4OBgWOQHBPRUBIoVEwuucLEH87+d/ALacY5/x/u6YnnB0H+QEnU6Hd28eVP37t3T9PS0RkdHQ56RH68DdK/HZ3ijnZ0dZZ2hc9rXlQd4c1SwzcTQSSrFPekKpHMa01k3kmTcE2FF2dwL4v3cY8awFM8DhGu32+FV8SQgHW7RZ5ZKv/XWW1pZWdEzzzyjv/SX/pLOnj2byHfwTDaVfOKJJzQ0NKTZ2dnE4DMZHqfs7u7qd3/3d/WNb3xDx44d08/93M+pXC6r2z1YqYnC0O6zZ8/qqaeeCm198OCB/uE//IdhXwqOV199Vf/8n//zBPHhrBkxRV9fn86cOaOPf/zjwYA1Gg1tbGwkIHi1WtX4+Li+9rWv6U/+5E8SLxDAS8RxiSuHK5THMGnK5tf6/3gl9qTDQzm8c6Hmb5RsdXVVy8vLYUPPnZ0dLSws6Otf/7q+/e1vq1QqBe/GjkXIJjLPXBOiuKGmGiaPRnts4YezZCiVW3rO4be/mIz7Qir4+26glf2IE7qe8/Hn0E6vtcJiuJtHiP06BiDepyCbzR4u0sofvF39zTff1P7+vp566il98pOfVKt18LK0zc1NSQq7q1LnVq/XdeLECX3/+98PMAXIw3hRB7i8vKz/5X/5XzQ+Pq6f+Zmf0d7env6v/+v/CuUojNmZM2f0d/7O31G1WlWvd7BfwK/+6q/qnXfe0czMTOgzBbKbm5sPVZkwdzCwtVpN/86/8++EBXi5XE43btzQ0tKSJIUSoyeeeELlcllra2v63ve+p729PR0/flyVSiXQ7LGySOnvQ8IoxtDOYXisRIxb7ME8kepw3r0asP3/+X/+H333u99VrVZTtVpN5C1HRkYCW01ahMoIYiPCCO6LTBK/sxde3rE+v51uplOe8XYe3ZUIK4Cgcu84ASod7piJJSHv4lDDhZHryUhjeVx5XNliSjTOj3W73RCfocQoMO3c39/XG2+8obm5OT311FO6cuWKzpw5E4oXsXAocqFQUK1W09DQUAjaXWjoHxb27t27+uVf/mX94i/+on7u535OTz/9tF577TXt7OyoVCppcnJSzzzzjI4dO6ZM5mBh2z/9p/9UX/nKV5TNZhN7sM3Pz4cqcObCIRJHf3+/XnrpJX3iE59IxExf/OIX1Wq1dPr0aX3iE58IQff6+rq+9KUvBUhcr9d17ty5sNlimldy0shJhhiipx1pMVRaztLv57GZowW8L8loXgHEEgqq7nnZA+uXKDGCGc1kMmGJUKlUUrlcDqxm2LrLH46wxhuWx6yP/+9BJoE32B7txRu5BXMP6PEHyse9Pe/ji8VcSdJYuzhg5XygItDQg3uupw3Awo2NDX3lK1/Ra6+9plOnTun8+fOanZ1NQE0KQ+/evRuMgmNr/vfnbWxs6N1339Uv//Iv62d/9mf10z/90/rsZz8biBCYMjzO//v//r/63Oc+FzY4XF9fD1Bjbm5Oq6urYVk6CksfpMNN8f+Nf+PfSDzj/fff15e+9CWdOHFCP/VTP6Vs9mA5wu3bt/XGG2/owYMH6na7unfvnn76p39aFy9eDOOVVqkQy4wrVyzwaezdowgJlzue4XPnyidJ1Wo1GJZMJqPZ2VlNTk4GeQQh1Go1lcvlAJeRE2Qlnz/YiXVmZibsmlStVsOeIGE9EpjXFUA6pKBpOJY+xsXkGXzTD3eFHsgC93yfu7Rkmueq3NVzpF0TewBXTp7tL1WO808OBWPmp91u68aNG7p582ZiYRzMDrGhQ2C3kIw13+/u7oaFZ7/+67+uV199VZ/4xCd09uzZsIF9sVjUysqKvvCFLwToB2V/9+5dbW1tqVwuJ7YcY+5gujy+/Wt/7a+FnBNe9R//43+sQqGgH//xHw+V1UtLS/qjP/qj0Ac83eLiYihShv30gD+mpWOlcdYwVp74cGPEHMX3O0p2ULCZmRlVKhW1222Nj4+HZSzSQZ6RotxKpRLq+VzWfM7YeZa3k+C5JB0UrXqGlw7gAfgsZuu4LpfLBQFiDRITByMG3vS31HmiMfY0bsE5z9nBGIJxXpx85YdB5X8gntfOuReMDYXT0j7YeBinmX05CQaFNnN/AmXioK2tLa2trWljYyPEV+Pj4zp16pRKpZJWV1f13nvvBU+3s7OjfD6v+/fv6/r163ruuedULpdVLpfDd2mw+Nlnn9XLL7+cENY//MM/1Je+9CW99NJLAe83Gg1961vfeshbsPrZYU8s9A7fPI/kh0PO+Ls4vnJGkCMNSrrwO6SsVqsaGxvT3t6ezp07p1qtFrxxNpsN5A7G0MkpRzLk086fPx+cBbLT6XQOoR3xhrNA8f7UvkIVQel0DpYZsy4F3J7P58NqV2KCuIoipqjjFxv7Yj8UwPMM8WQQ57kncWFwI0BdIM/iPq7ADseYTPpCe5hs8hvEWvHhgtZsNoNRYg0RNXZsZtJsNrW8vJyAaU4fM16/8zu/o6tXr+rMmTM6duyY3nvvvTBnWP1yuawXXnhB/+6/+++qXC6H79555x39g3/wDzQ+Pq7z589LOkhZvPbaa9re3g6QkvIkSRofH09YbR9f2sn4uay48KdBsTQl4rPYKzmFHsNFJxwymUyIaVhGks8f7LGAQfSlLzBzrkS0x7dHA3klUABC541zC4y1dhrQO+k1drh8kowIHoIVu3g233eB98FyOt1xrisO7XE20asR0jYZjJ/nguCD6JQ7hxuDtFjAYY7Xh8XEjHsrxh7WEMPG2HgMCduJ0Xn99df1la98RT/xEz+hv/23/7Z+5Vd+JTCnuVxOZ86c0c/+7M/qL/2lvxQqxqUDCv2//+//ey0tLenjH/94aOd3vvMdvfXWW2EsXLD7+vr0+OOPhx2XMEauVE7qxNDYmbY0r++/3YDHcJ1nxfEYMuMyUi6Xde7cOZVKJY2Ojob3FyPTTmfHS4fcuHJvZx593vM+mXHg6C7SGTcEgyQndC1vcfAOoWxxgtc7L+mhpDC0o8M8rudwwUJwPb4DbnlxLPCSyY6pYu7Jq2HSsL+3w61iPNnx50AK94ocOzs7ibgBhtKNGOPqtX9Uco+OjupHfuRH9N/8N/+NXnnlFUnS8ePH9fGPfzysTaL9a2tr+h//x/9Rb7/9tnK5XFgSsbGxoW9+85th3Dz+a7fbunr1qoaHhxNwxwWfNvmSBu/TUSRDbNTSIJ+Pd3xujEpidHHu3DkNDQ0F+juGn6xj89pNnpUWyzmMDPG8B+kOHzz+QCk8eCbBSSWwZ7gREs/W++AjsO62PY6JrQrPppN+viu4Ky9EggesuGiudavng4eSEbgD7xzy0UcEJp5cx8+MYVzAm1bG4v/7GjEXQK6nnWtra/ov/8v/Up/97Gf16U9/Wk8++WSCrXNofOPGDf3qr/6qvva1r4XvWdZ+7949bW9va2hoKBgRvN/IyIiefvppTU1NhRxS3PZHHTHiSGPw/Pt4XtKo8w96PgabvQJh6txzAuW8YiXNOMSxFwYc1JJ3iIH1c1iBUnmy1Bk6igjZQQgNJuvre5U5dnYX6m2IGT/pcJNJH0C/BsUirmKi3JPhVYGgTkPHUJYxcK8ZF+ISu8UBtcdHfIYi+Zj6xDib5ZPlCuj9d7JGOvDm6+vr+l//1/9Vr732mj71qU/p0qVLoRiYNye8+eabevPNN7W+vp5oZ6PR0NjYWHjNDOMrHSSQ+/v79bGPfUyjo6PBe3HE8MuNY5z/8SNNMfz4YZQ0Jjt87Pgsm82GnBdjDYzDA3mtqBM13oa4f7C+bKAaVshSmOmWoN0+fC0HD8MTEX8g8CiRK4d0GHzTSPdSDpcQaB8AhCWmoz2ucgshPbxC06/ze/tEuqI4oUFsRj7Gc0ux8nNPZwwdFtA3jx1i4iT2xB5rxbQ/3sLbvLOzozfeeEPf+MY3QnJYkra2tgJMJKYlv7e/v69bt26FRLN0WOLV6x28ROxHf/RHNTk5qfHx8cRq0TQIhiH68zj8OTH0TjsP+SXP6XEe8+kMnnsvziWcITdqlRL5IDSexPMjm82GSld2uiRxFVct5PP5UDPnFgBFdUXEAnsMwD1iQfcBc8iC1eYcmDvp0Lt5YBnHek7xx5NBHgZIyP1cCWgHz44nOPZkTu7EyhV7SbyqezI3Nu69pcNq5G63G15GzNgjOG5QaPv777+vq1ev6rHHHtPNmze1vr6ugYEBHT9+XB/96Ec1OzsbILxb6w/yGu4xjjp+GHjm58bEww97H+TGa0CZ++3t7bB5J4QEMugGkdQC23WRe/1BiJFPCJ97BseLNEI62PEym82GnSqdfKBxCBpCQsOYbElBOOkoA+eTHZMLR01G7Im4j+eW4nMQCGf8fLdSznG2LZ7YtDFzS+YxlQfoDn9i5Y6F3WO6eLxhBqWHX6KF8rhi+itnXDD/6I/+SC+//HKo+8tkMonNaKhHiw9n22IoFAfqf5rjURAwjrsedQ474JJeIO7l8wcPHgQZyGazDyVoBwYGDhbwWTomgT6kQ/jl2JzPCdopS5mYmFC5XFapVEqci9AAFaVDVm1gYCAE+Xg+DrfQ0uEOqj44LpSxMjhTh/IgJLjtmG3r9XoJ6+pQMg2uxdfGk8j5zvB5bIAncS/AeW5IaENsPGKox+GLEmGjmBOPG3mmV65wLmO3ubmp3/u939P3vve9YDD5fnNzU9euXQtsXDzvLrQ+V25UjzKCf14H2xdT4U28n8sdbmTC+4KJq3xM4QJ87NwQ5VEiBtyLOguFgprNphqNhvr7+1WpVML+19KhkFBE6nkPBMPLg7wi3K2wKw6EgVt/V07+dmHBYvO9W2naFcMMPGOv10vszOqCjHVyz+x9kw4hln/G57TRYz0nJ2Jokna+ezM3UIwbyhjHUa58KCPjxsH98Fqbm5v6oz/6IxUKBV24cEHPPvusRkdHdfr0af3sz/5sIBoYO2TBYz7ir2z2cIeff1mv5Efs+f1wQxifWy6XJUnr6+uSlGDcstmsxsfHlckcJHBZM/cvQojk3e07TYyC8T5W3qXqAp4Gl1wY0XaEzZc+x+yWF0HiyWKI454jniDPrdA2P8fv6ayk0/Gcx6TE/XHlpd1+bZxKiGOhtGA4LXcX516AhNyDc4CiDvvIP8VEjhu/+HlxH/b393Xt2jVdu3ZNx44d03PPPaczZ848tPQ69tgYNGQK48Lmk/+qj1i53ABmMpnwpnoMJD/5fF5TU1OamprS8PCwqtXqD02YBFl85plneo4ZEepGo6FcLheqXCUlstj8djaPGxMg++sy2REzptc5YtLChQ6I4JAIK+uTGQs0jKILoxsBH/C08ha36t1uN7FjJ1g5XtckHW4cmQYNXSm8r3gVJhbh9ns4DPZcEpALQXY460bK587Hnfv4vMYlWuVyWWNjY5qamtLjjz+us2fPBrZ2fn4+0MD9/f2anZ3V4OCg3nvvvbD2Z3BwMLQN5ffDDUf8GX3DcCNvnu9jXCcmJsISnjj3CGkUJ7mBoSwn/2GUyMdK+kHRKg3BFTebTRUKBU1OToYqX+nwbd5+szg3giJRx4SFc2/iBaIEeHQO3MpAOjHhCgt8cyjGtTE0c5jHebRfSsaIWFGE2yGdH3EJFX3jeTF9n8boxYrv4+iHkxQxsZIGD70ttNUJEQimtJjMvSpz1ul0wgvGrl+/rl6vp+vXryufz4cdeIghBgcH9fLLL+vTn/60XnnllfAGiD/8wz9MPJ9x4OVhtI9NatrttorForrdbvCCHtMxRxAi1WpVzzzzTHipHJ7Rdw92Q+JGiTn8ICVy5Ynj+LyzdWBfVkCSuEQZXIhcaVzIgHKO7bPZbIKCdRgGlua+ac9w7+GMFe12T8WgcC0MlAsS93F2zZXQ4xegQNwOj3Xi+zLJTIp/HzOiafGSY/04qHWP4obIlSf2Qj7WNvEPxRROwXNdnGqoVqsaGRnRxsaG9vf39f3vfz+kCSjo/M53vqNPfvKT+lt/62+pXq/rf/vf/jetrq4m9u3GeG1tbandbqtarYbXpbDcH2+PYUdZQSP+XqhKpaKnnnoqUWXjb1Zh7OJqj3is4jH2I/aaCc9OgMiWrVNTUw8lZ9NioTgwZnKYEKdXiRliogEIEXeESfeYxDPQ0uHOobFFdm8nHSqkx0woTAwz6Q9C4aUjMSPmB9e58fDJYkzcuwGZ3AP0er0Q+Ps1XBdDRYyc95HzYuvpxoqxjeEN7UYQIaHcs169ejVUQm9ubqZC9IWFBf39v//39dJLL6lSqeiVV14Jb7VnfVQMzzB4KI/PA2GHwzPmmrE/c+ZM2GuPOXajxjzB1vJ/msKkGbIPOvJbW1saGBjQzMxMwLiSwvLvTudw6yoGlA44m8ZDOcetTxzA++EFp3gG7u2xC5+7QEvpWxozSHGQjrWlMhq2jvs6ScIEO8aOyQZXHH+WCyRC4MaCv+Px8MoGn2wObx/GyZUNAfB4kHHhe/c6jgAQbix9JpMJW5RRd1etVnXx4sXQT4peiYd5zt7enq5fvx62+OVNGbQrjq/z+bx2dnbCu5x8s3w3Kowf90JZKpWKnnnmmaCkXlPnUD2NjEg7jjr/UUf+7NmzwR1KCsk3vIXDMekwDsGTxOwWD/fyFRpDvOXwwj0Of/N/TOd651zA4uoBPxIB4Q/Kk4CTMFxx3EG7vT2xV3NLlyas9NHZOycx4riItnp/Yg/H/34PvJtf7+egaMSpfjht7c9AWKlQQckee+wxDQ4OhjTG6upqMBS0073z7u6u7ty58xAcpa20ibAi9h5OsdNeZyd55hNPPKFarZZ4v+xRscwPc8QK5PPu8oDHy2QyylOlIB0qiUMzt/ZYFGjEZrOZcLHEQkywDyDnYOWccZEOc0wuOGmT7AMTW2gU0OMfoFJMQMDA+QRjQPzebul9QOMA3Z9PDBDHPfyODZBPdBpE9M9jOJ1mPd0TOdR1MgIB8fswdv76HYSSbccopblx44a63YMNRXw/DunAEAwMDGhvby9spuhwC1lDnig/ajab4YVs3i8XaP/bvZGnWuKfoyCcHz638bkxFORckrq7u7sHyyj6+/tDNpvGehbcBxrhpGjVIZQLiwtbjO2ZXOf0nUCID9y/tymuM6PDkBr0xYNL+uXPoV9+XhwbpsVE7hljT+VjgABnMsliW57l58fPceVhPF1ZQA38H8c9IADGLGY6MZqxMkNPUw2Qy+V07ty5sDfE/v6+bty4oc3NzYSieuyVzWbDCl/3IvH7idiGeGhoSM1mMyyXR4mJ7fiNQaZI+sqVK2EPCiBpDN/icU070giE+HuXIYzIwsKC7t69qzyuWjrcdJ4J9MJSJsETazH0khQWxKFskALx9lseyLsQumJxYGni+jcXVm9DzNAhSLh9h07c44MKLGOIEFsw4BPCyblY+TSBdUXncDbNvZxXbnCe3x+L7/f2pfsOqZnPOBjnXJ5z7NgxvfDCC3r99df19NNPh7lfWFhIvA7SjYFXX2xsbCT6RV+QgTjmBsa5TDCf8TMo17ly5Uqi32lHGuT9oPPSDLXfgznN/6CKPg9bwo28Fo6YguJSpxOZQDrKPYAF2Ww2lNzDwGBlWGLulQ6xRWVSEZYY8njxoCtDLDQxi+dCQ1+cKPD2xiyYB6+xpUuDczEM495OR8ceCXjt+bSYYDlqcn1c+CxNGGII6ERANnuw5H1jY0P379/XwsKC/sbf+Buh2FOSvv/97yeuo9/u5VAinu/EgZTMw5H7AU2wL6Ajg5jpzOVyOnHihE6dOvVIJYoVJ+1cj219XI6ChcgdnnRkZOQgIYv7RdNjz0TjPfHoQuv5EgbYV8biifz9SdlsNlggvA3KgJZ72xAgBDYeKARcUsJr+kCgcA7/6Jt733jQ4gGMD1dSD4xdobl/2gR6Hzy29OsdOnGtx1pxjOXsHGOCQXrUWz16vYMNE/v6+tRqtfQHf/AHWl5e1uOPP67h4eFQf9nrHS5DZ1y8ooMXd7nc0H5gPFUPtId7wxwzV7zPlcoXNq+8evVqeP/vo/oTz6N/5zGwz+dR8VI87wMDA+yFfrhJCYPH8uJsNptY6kCDmPQYjjG4eC+Uwy0+lg1P5LR2nLNAQOM923zgPC/ighZDAfcu7kldMDnfy2VcOH0SGIu04N89WOxFneVyOIaAgfOd7JGSCwI9cOdZzt5xOMyKvStCz0Ymfk9X4EqlonfeeUdvv/22pIMtuaCpiVM8Vut0Otrc3Hyo79xXOmCGqTpAkbxsKJ4fV8RMJhOE94knngjjGB/eX//My838J81rfxBcRGb7+/sPXusCTgfGeQzCQ9074da8s25Z3QX74HkVMgPsAuVMC9d5YpA2Ea8hEK4Q8eD5314W4pMcB/G0iXFwyxZPlFs0Hyfu5RMSEx98lva/w22UxCGsGzQmlDbFcawbKNqIp6fGDHbNx4174zlarZaazaa2t7cT7+TFAxITLy8vJ8Yd2WCseEO4jyVvznAFdOMG2YRXevrppxPviornxeEtcry/v5+ojUSmYkhO/4+6Z3zewMCA8pRkAKXcbdOJeMAIMvv6+gJcY4I9fnK2iUoBBsStMM9zaOBQ76jKYY/T6JRT4ZyT5vrTmLnY1cfKlHZ9/B3994lMmwSPgZxQcMGPoSzjghDHpIkrsN/PLS+Hx1Pu6TxecIKDdTp8znW0jzFuNBrB43A9c4+nbbfbGhwcDPtBINh4DGCex1a0fWBgQOPj43r22WdDe49CDygtG+Q7cUb70pTISRHOiWXIkUs2m1WenT5Z/VcqlcJmIwyS38Anjw6TuHPBQIA8CVosFoM3cQHwc0mq4SlhAZ3mxZr4i5898PW2pgXmXBMLfKwQHrv43z4ejqVjCMgYeV/9+a5kMdmR1mY8j48DExrDOp7r8xdDLQRkZ2cnlOLQlrgaw9vqfSGepQ0bGxshxuH5XscItO/v739oCza8ZBx7ofDU1OGNvE/+PMaEZeX+NgmgGLAy7djf3w8emperxXOOsQLW5p955pmwNZO/+JZB9Zt4HIL1IHCPmTMGhCQb1zh0I57ywHxwcDBYD4eZUpLpyWazibVOHqAzKU4tH8V44WEZGBdiVxr3oDEBETN57g1d8NLIDO7hzzsqJosr49MUzpXXY1P3lD4HzLu3G2HEA0oKm/N7zOx95t2yXp/n84tw8zpRYh2XHRQrht/SQVqlXC5rZGRETz755EPK7Uen0wlbxcWxcRyPpR0sP8//oCI8niPfjZWf/AsvvKC9vT1961vfSsQgHicxqF465NDDB186pJx9s3IfGJ88vBDwjQF2jY+JDC8kdVrUy5hok0O1mEbPZrNhWyb6mcasxTDQJ4EJde/ncCquqvBrnQhxb+hEDOenTbzDPveMfq+Y1UsjAZwZ9fYTMwPfPQ7mXv4Kynq9noBNKJErbbt98PoUngHMc/LK5ZDzEP7nnntOlUolMQY+ZyhRvV5PeN6+vr7EkvxHHR5DxiiFtA3yiULle72ePvaxj2lzc1PvvPNOgHgOP3iwFzYyWJTgoJlOMtTr9URHHadj1eKaPIJODzi9+NVjKSbXXbQnbbkP7WaCuA/nd7uHeaMYqsVxk8dg7oG4twsy1suVKU78+vg4yeIK6kRL7A3jwy0w4xN7MbyGtzNOegK/+B8jyrWsN6Nt0N253MH+h2yPDKGRz+dVr9fDnLsx40iwYJYnzGaz4X1GJGAZd2AY3g5CAcOL9/P47lHeiPvyknDfpDOWZUmH+zu+/fbb6na7Onv2bFhfMj4+HjZ1d4LBsW0sJAyia3Ka8KHNTIBDgV6vF+ItF1YPpv2ePjhsRsnEkJugPZ7bQoGcFYqFMyYX/HAF8h8OjzV4ThrOdkGWDqGgw+NcLheqQ2JPmJbRd3KI5zMvCKa/ANsTuNKhArKporN5zsbC5iFM1F1mMpnwueerqGaI47z9/f3w2hX/nD4SDw8MDOj5558P3gijsL+/r3q9HsqZGDOnzF3hH6VEkgJsI6caIwjmgXkixsy/8sorarfbOn78uJ566int7OxobW1NtVpNIyMj4QXGxDTgYs+1uBAR1zhDB1bNZrMJmtPLW+JO+mbyR+FhLDjQg3PdA7h7duXE8rtAH0U88J1Dp7jvMQQAGnksk0YJp7FqfO6xIcyX38NTBPG40Da8twtpWnKYOfDrGdeY1OF88khsSxB7QIdnGDoUAyV3NJHJHFQ5gCoQflbeXr16NTGfzAMQy5WHZ4BgkAs/YuIIWLi1taVsNhs2TPH5ckNSKBRC7JhFEJeWltTpdPTX//pf15kzZ7S6uhqshb/niBuwQMo333Ah9gFCsxEs3gbgG0y622QyGJA09gjvhqJ3u93wHGCmQ6E4CepBeMyi+d/+GYMYKx8CmAYHPbbw4NQVyUmOeByw5FSre59iatjbGbfdn+FKmpZ8lpSohI8FG+HO5w8qt4lHmDOu8wqFOL6KPW232w3btrEmibkfHBzUpUuXNDIykthOi9ykk02+jTCySYwUK5KPG8q+tbWl1dVVbW5uBgdC2xkD7smrL2dnZ5XnHZizs7M6duyYzp49q8nJSf36r/+63n33XR07dix4IjwAGwVCe7qgM0g+mR6fUGXOeTFL6BAurjfDkqIUdBClIBB0gXSP58E19/eYxSfavZcfHkPFn7kSxR6Ge8bXxXS9W7w0BUmDJn5uDDXT2u8xrx8+Xj5G8bNzuVzYW2FzczMhHx7PYTiBP/7MOEaizblcLhhGBLZSqegjH/lISHm4h4z7ASvn3ugogxPPHbvTbm1theoe95qOHPghhsr39/dreHhYn/zkJ3Xu3DkVi0XVajX9rb/1t/Trv/7runv3rqanp4MS+Z5zdIaEl3RIGjj17LgfpUEhnH52SBbHLy6EsYdySOGK4oLkVtiDbq8siKFbPODu2tMEND4XQeFv7x+Ht5PJBh4jWM5KEkTTBoeOjt09nuQcJ5H8O+5HkO6eH3hHbgkyYHBwUNvb24lxj71lnB5BuDnwaLQH4glygkKBq1evanZ2NngxniUpkFoIPOwc48Y1R8VGtLfVaoXXkOLRMAaMq495fH02n8/r7NmzOnv2rCqVSpi048eP6xd/8Rd15cqVsFTYGTEfMKCebypOQ3DBvkWS42hnAT1u8TiKgY4ZL4drtC8mI1yoUTg8F9fzLDcOcdzCOY+y9hx+nhsNnpl2HzcKEDtusPC8MevkHtsF1K0wXshjtTgm9QQsbfAXwaHE+Xw+xDE7OzsJI+TkDwoqKSAYDDFw3gUcI+tjUigUNDo6qhdeeCHhDZEx4GW3201U5NAG95CPOjqdTiBM8vm8xsbGVC6XQ58dYcSIgmuzk5OTOn36dOItbAzI5OSk/u1/+9/WE088oYWFhSDs/t5Nn8RYawuFQthw3DedAAtjORgAOu0khLOCHqhivdzjIDRpiVC8TyzMDi24j+Ni9yYu7LGyxDDQPUGszG4AfHLigD/2im7Rnf3iANbQXk8mIljZbDaRB/H8Hb/xPigR3mRwcFCVSkW1Wk3NZjMRxOM9PbZk7mBLXXFJxsZQllgQ2Xnqqac0OTmZGqsyVo1GIyzbcGPg/Uo7nKxoNBrqdrsqFouqVCoqlUrB8KfBeb9Hp9NRdmJiIpSqLy8vB66fyarVavq5n/s5vfDCC1pZWQlQDErSvY+v4UFIoR+xIAy2Kx9C4gLChALJYkF1qjtN8H2gHHa224cvl3JlSPvxIBOlcA8T/5/GZMbK4vf1OC1WMLei8bluCPxc+saYEC/yOWPpguv5Mw5yMjwzn8+HbXyHhoZCBQMC73OAMPs21V6P6Zvd+LhiSCE5+vv7NTo6qqeffvpIReD+CwsLWlxcTI1Pf5hjZ2cnVOCwrz1vnvQ5edSR73Q6Wl1dVaPR0Pr6+kMN7/V6qlQq+it/5a9Ikl599VVVKhU1Go3wIiu8xObmZgJ+ucXxuqo4PmHSEUoGl8YXCoVEAOqCyPN943fwucNFYN9R8MyF39vJGMTfu2K5UfB2xfc76l7ugRkTzsP4uFLH95OSHswJG6A310MAcL57EbyLK5GkYDB/sO5G6+vriTbyDJ7pqIA96ZhPqlNgI1Eo6t84p1wu6/Lly5qcnEw1pIzb9va23n///eBR0mBYbNh8Dkno4o3YmptxjGPao4784uKitra2tLm5qUwmo2eeeSYx4QhJtVrVyy+/rGKxqC9/+cuhWJXgkyQfr87whCuDipeRlEjmOhUd530YMI9jXICYeKe9Y4HyujuHdmmDFXslj1/8uT4ZP6zixJ7GSQufeAQCBgsLzzh6uiDeCdeV2/uGF/ICUmc0/Rw3asQj2exBdcHGxkY4H4HzejogJIv0fIxIezD/IA8qwPFyg4ODmpqa0vPPP6/+/v6HlIi5aTabWltb0/379zUyMhKo75gMcjTgc9rtdoMiwUQ68YKRT5tXb4v0A0XqdDra2trSY489FpZTxEcmk9Hw8LB+7Md+TOVyWZ///OcDnEMZ+CmVSokKWurqXED8zdw+2OwvjlIw+UwcVsThGhDQyRBn7/zctJjFnx97HY44EegD6fFZbBH9x5UfgaJPLvDePpTHK+A9XqOdPk70C+Pi8UQcfPt6NN/MhpQF+cORkZFQl+jPYAMT7wexBfklj2kgTICF5KQcbg4PD+ujH/2ojh07lpgHh4+SQv5Tko4dO6ZarRZWzPp4xnG03w/GuVwuP6SEKPdRXsnvlW82m0EYeQufT4QfKMnHP/5xZbNZfe5zn1O32w0DgfXY2toKA+uTTzW4C5db2hi3e/KSjscUMpPm7J50GA/Fgpl2xFAv9kCufB80qLFniZ+BonmM5nS+xzA+8b72JYY4JAd9/0GPL12RGHMgJN6D8Ws2myF/wpvqq9Wq+vr6tLa2lngu8Y6kxF4fXlxMW1HKVqsV4iwYPI52+2Afu5MnT+r5559/aPEfcoFHhlksFAoaHx9XuVxOVNYcZfCYX69GLxaLiX3CYf4cwcTGy+Um75uOV6vVh0gAGsFg9HoHu7188pOfVKVS0f/5f/6fCUqZwW80GuGNfuzpTLDpAsnmKx58ck6nc7BRigubW10XVgQsdsUE8bE1877FChD3+6i4yv+O4yOeHUM32uhw0IUaSOGGBujsDCSGyu/p/4MSeI5DOcbTk6cIDnPlmftisaj19fXgRWDWQAZ9fX0aHBwMJUAQDCgjBBWKhLBDXkgKVRCjo6P68Ic/rJmZmQSki8uh2u12orIBphkZhXn0eUGmGA8qF4aGhgK85MDw+8vS4tjW5SM/OjoaPNLw8HBqPZJbUZY8FAoFPf3008pkMvpn/+yfBZwpKUx+o9EImJNGecfcY3gHmRAExxk94iEOH0jPQ/mAOOSLhd+Fy4NVFNAHK80b8J2f6zDFlSz2en64EiDcLggsCYdN8nOw0BhEV1aUhHgEL+L7xeG99/b2Em+HGBkZ0cTEhBqNRlA+j4MQWIfRVNavrq6GxCbxMM/2uQdaIvinT5/WRz7ykYQSORvo3oS+7+7uqtFoJF5n42kNn0vCEcrLcrlcImns+bcYdseHy0+eV2fk83nVarWHqgZiqLO3t6e1tbWwY2oud7C32GuvvZbYSINB9viJDhIHeYfjtz5AR4KnESopuXl8PHDx3wxeGuvlAu0xkyuVK5rnyHxyHDrFODyNtOD+MRvFvVAIJ2EYn729PY2OjgbF8P55OsCVmfv78gWEoNPphG2IeUk2Af/MzEyicoDcD8onJV9+TXwCAvDiVPKIPJfELoILxf7iiy9qYmIiAckZUxSZ+7thWF9f187OTniRcmyQHAUQv+/t7QVP5Lkuv545dY/O+Pn5ebZempiYCK+TPwrCELw+ePBA9XpdmUwmvHr+6tWrunPnTmL1IAMOw+f1TnTOc0Aei2DlXGF8SYAPpmfHnfLG8h2lSC7o8QQBN33gYlaQz2Mo6QqT5sFcAY5SZCYtVpJer6fNzU2Njo4mVqvSPleguNSIpRGdTifEVKQNyNADuaamptTX1xeWjnNfaGsp6dUYG0nBg3lpk5NRjG+xWNTW1lZYzn358mU999xzD4UVMWpAZgqFgiqViiqVilZXV7W9vX2wx5zFXYwf17Xb7cTCP2QTyr9cLifeSsgcpOmEz2f2/fff1507dxLsVww9vAP+AHA0iwEvXbqkWq2WeJO2lKwkRii8qBBFcMHjOdCkTCTn+NoiF2QG0QcwZupiYXYFCRYmf7hJC/Gf3ycNqmHF4pgyfi7nOe3sk5SmaP47LW/G3PiYcq7DKunwZQYYHukgKQmLOjo6qunp6ZBfceuczR6+XBuB9lrJbDZ7sD7HqvpRNicc8FDZbDYo0o/8yI8EZU8IqSmSw2hirFKpFHZ+9cNDCJSo2Wxqa2tLW1tbAeJRHkTM5PMXw/KjjHH+a1/7moaHhzU4OKgzZ86EZBgeodFoaHNzMyiN42U6SnFhoVDQk08+qVu3bun+/fthP+dsNrlpvcdFgfXI5xPW2CeQwkZfj+MxVGzVY0Xinnzu33ubXKiZgDSvEk/0UUdaYIqQI8h850bCBcbjkVj5m81mSFfEREIaDCHPxPhyLs/CmE5NTQW2yokBxtoF2Rfv9XqHG8sTF1OBUiwWwzjX63UNDw+HzfMl6fz58zp37lxivNPGnb4zltR58sKymO7GgwKLNzY2tLa2Fl7B6bv9xslpNzRHeaQgv3Nzc1paWlKj0VCr1dKJEydULpe1v7+vu3fv6vbt2+p2uzp+/LhOnTql4eHhkITFqrKuHwE4ffq0BgYG9N3vfldTU1OhKoH4yAsgXaEQMOCbwyQPvH0ysYR8zr1jmBYLNee7QXD45pP3KJgWf+5KmzYBsTdCOLxezQ2EB9gu8JA3eHZnLVFWzkMgGC+EjLayurTVamlsbEyVSiWsjo6huFcpuJABF1dWVhKekHlH4fCa5XJZnU4nVBP81E/9VEjqx8rjY+lFzxg7PImPO23m/Farpa2tLS0vL2t9fT1A2/39fQ0NDYV+oXy+NOSDjkwmc7CJPgHa97//fd2/fz+UhDDpa2trCfxMqUitVgs4utlshhxEJpPR+Pi4PvGJT+irX/2qRkZGggUlbmKlrG9tzAA5NGSAoFURBDyhK47T8M7UubJ6zOEUMoPtVtrX4sSe0OOwGNohOLCbQ0NDGhoaCktUyHd48IwSQM7U6/WQuW82m4GVQkgxROB87uFedmhoKMSVjB/Gxr0I4zU4OKhjx44l4i4fK5TTrTfKCP0dJ2ghLlgUuru7q4GBgfAal0KhoJdeeumh5KsfzB8H997d3dXGxoYePHiQiL+YFxSJNMrKyooWFxe1t7cXVra6cvOGDH9VzQ9z5HI55U+cOKHJyUmNjY1pZGQkMeHZ7MEy4vn5+cTumaVSSTMzM5qamlIul9PKyoqWl5eDQu3u7mp7e1vPP/+8arWavvSlLyVyH7B7CGan00nkIBiAOL/iDJ/jbwSQjS98jQuKhxJ5MB5T70AeruF7Z8PifA33g0at1Wrh7d/Dw8MqlUoqlUqhaHdoaCi03YXRWSEKOHnH0NLSkr7zne/oxo0bWl9fD8aDMW02m4nNRuiT793GWLriMmawdbOzs0Gg/E0TtIv9DklvSAc7B9EPiCu8JF6J4B0Dwa5B5XJZFy5c0Ec+8pGHGEwOVwjmG/nZ2dnRrVu39P777+vChQsPUd5uWLe3t7WysqLNzU2Vy+VEqgYoDPwjL4ZxdfY0DW5ms1nljx8/rtHRUY2NjYVYqVKpaHh4OODi8fFxPXjwIFi//v5+VSqVIBSSdOLECQ0NDSmTyYRq3Lm5Ob344osaHR3Vb//2b4cg2bfARYgZHCwtmo4AwxD1er1EESYK57CF83wy8KYOsTzRFpMSWGD3aCgsFrWvr0+lUkmTk5OamprS9PS0jh07lnhPj7+GEevnHsRxOBNKrFIsFjUzM6Nz587p+eef1507d/Stb31L3/jGN7S8vBwEwaEe/fNNGp3dc2/aarW0vb2t/f39sHwANADMZlyJg52cYB4Zp0ajEfpLlQvxEbKE5ysUCpqcnAwvCTvK+vd6vVDGg9cBji4uLuqNN97QwsJC8KQO5VEiCrKppqAwFYOAAgL1fIUAeVP3iLEiSVK+XC4/9BY94AoQjvVEW1tbocrbS32KxaIuXboUoNDa2ppu3LihpaUl3b17V48//rgmJyf1h3/4h3r33XeDQgHlUBa8jpfUM4C+RMBxPrDP19gg8HTSvQYDBmRMY+I8fkABMplMgLzj4+OanZ3V9PS0pqamQkVIf39/qIinT0Apx9sIaEys0C4XzEzmoPasWCzq8ccf1+nTp3X69Gl97nOf0/z8fPAWVJV4jg6jhGJgXX0XKLzMyMiI2u2DPed8/Lx8yIUHFpD+othuPGDneJnYzs5OYHknJyc1PT2dMKZpSsT+DKxBg6Gs1+t666239M4770g6KG3iXp1OJ2xVXK/Xtb6+rrW1tQQ54RDY54Jr4Qw2NzdFrvVRR95ZMTpO7qdcLgcvg1A0Gg1tb2/rvffeU6/X0+TkZFjoxwQicP39/VpaWtLQ0JDOnTunn//5n9dv/MZv6J133kmsspSSm0bGTF4Mt1w4XRi9yhuhdWvnwWQcKDtcc3YMLzI2NqazZ8/qxIkTmpmZCVZWUiIw9Y3aEaZ2u53YmIVruDdGyRlDtj2r1+vBarJrzYc//GEVCgX9w3/4D4OhQVmc/ncWyo0MSkXuhH07PMnucIbfVCr4/aSDJP3W1laASVR5k/BkI5Jms6nh4WFNT09rdnY2EcO4UPt8MVYss5AOSs4WFhb05ptvBiiGMQGmNhoNra6uhrio2WyGNVXIGPLidY/Au3q9rnq9ro2NjcQmmGnKnslklAe/03AIgXq9ru3t7TAQ/t5YNsVrNBqamprS8ePHNT4+HixGoVDQ2NiYdnZ2tLS0pPfff1/ZbFYTExP6kR/5EWUyGb3//vtB+XwXGoQfxSaGAld7xyWFPAAuGUvjkMOhn7NzTlz4QAEfBgYGxAric+fOaWJiItFG6FP3OBAuBLO0h8ppL2+ibs2Df+7tnhZvu729HSz8s88+q9dee03Xr18PpIUvpvP0gbNbHP7aUdpEm+Nqe4ypdOhlGWNKi7rdbvA8jGkud7A0wT37yZMnde7cuQSTCOMbwzvGGA/Ic5vNpubm5rS6uho2YWFxKozcysqK7t27p/n5ee3u7gbPiRGhj5ubm4miZ08jsNQ+Tif4Ecbi2LFjibVEW1tbYTJ85aAHytQ2LSws6N1339Xo6KhOnDihEydOaHR0VLVaLVSTs6FfoVDQ2tqaOp2OPvShD6ler2tpaSkBE/b29lStVkOBK0JA/ooYCuGkwJKch+eCcNOxB3LCgMODVFjJ06dP6/Llyzp+/HiwSM6yoeC0Z3t7OxG4YvmwlC54mUwm0M3Eex6cO8TO5/Pa3t4OuB4iYGhoSE8//bTee++98Lxisah6vR7GwuM/hAKLS96F+09MTCRYVIpNnWzBIPkrXTqdTug7Xtdf5AaD1+v1NDExofPnz6tSqYT57XQOlnmPjIykCqsnxpkjytScVu92D96yXqlUNDg4qOXlZS0uLoZ8Jsx0zABSmcO8wBMgvyipG8C0Iz8+Ph6SZWythJUluQblisep1+uam5vT+vq6Go2GSqWSbt26pQsXLujixYuamZlRqVQKCcORkRENDw9rcXExLCC8cOGCxsbGwn18sPxtfZ7IxVOA72Nv4O4Z5XJ2jslHEaXD3Wuy2azGxsb02GOP6fHHH9fMzExiM0usOdYdUgBrhrDs7u6GWIUJItPf19enzc3NxJ5twD/iUQxGpVIJpA6kABMLfDx58qSKxWLC23mlCNaSd/qiKLSn3W5rfX1dJ06cUKVSCV6MJKWX4ni6wCEj+Sb61Ol0gndg+QWe8sqVKxoeHk6wo53OwVo4mNi0w5WLNEq9Xk8YN5wARoSXoaFovlEKcsAY4DB4UwZbFZPv4i0qHLH3zGQyB2SDD6KksMLVYRTlGJ1OR2tra5qfnw/bMQE/aNTi4qJGR0fV7XZDXR64E9Yum81qampKQ0NDunbtmjY2NsLm6gigwygmySdbknwZCEEoAbd3FgWLXXhfX58mJyd1+fJlXb58WVNTU4GR85IT4jIOX97BfVB+r++DrqYPGxsbwWrncrmwy9Li4mJo1/7+vqrVqsrlsvb29jQ+Pq5er6e1tbXEGh72Fmg2m2GjE/cg7kmYH97SQJ4qn89rdnY2eC0gZa/XCzGTv9SgWCwGUqjVagVjSQWDU+bVajUgDtIlTk0jkJubm2o0GiHXdNQBrNvY2AjICWPBvA4MDKhWq4VFfqOjowFBSYcezne5gpCg/4QpGGMvED4qz5XHhWcymRCHgDs9N0Mj9vf3tbS0FLAlAr6zs6N79+5pY2NDt2/fVrlcDoPUbrd15cqVsAafZ3U6nUC1v/baa8GieEBLUpEKZRjDbrcbavxyuYNX2W9ubobiWGf28FRON/OakMuXL+vZZ5/V6dOnEzknj62czcHCeYAM80XMAVxhCT/Wu9lsBu/hFQfkV9h4BrJheHhYx44d087OjhqNRkiASwpFq7VaLSy4c0Gmfc1mM9TR1ev10CbIpImJiUD84HlY4cxnKA6xNNQ20JxKF1bT7u/vq1wuh52mxsbGdOLEiQRx4ULYaDS0uLioYrH4UK1dLLx7e3sB2XBgyAYHB0PyFwTAIkJkF8IEMsTJKVI6e3t72t7eDqkAEA3zGx+tVkt5X4XoDyFJioVFO6m98yQZjdnd3VW9XtfKykpQBvZRPn36tIaHhzU2NhYejhDlcjmdOnVKb775pt5++21tb2+HLDiWAMFkktzqLi8va2trKxAWbvGwnNJh8q1Sqejy5ct6/vnnNTs7G9gcqFavaPAcErDGg3Ggr2P4drut7e1tra6uhjHxzQ9hPwn4C4WCNjc3Q0yE111ZWdHOzo7Gx8fVbDZ18+ZNjYyMaHR0VLlcTjMzM6pWq4l4BkXHKxJHrq6uJsbv1KlTQZFqtZp2d3dDbojUBFQxi/EkJaDu1taWhoeHAyFBrqnVaqlUKgXS6dSpU4nVroytG63FxUVVKhVNT08/tDI2jo/YTpuxQgmq1aqKxWKg3zF41PR5iOIxKfFlLpcLOVVnKX3lLPGYM4uNRkN5YBA3hqVDUTY3N7WzsxMqZO/evRtWtdI5oBWQy181CCPz9ttv6+mnn9bs7GwIRhFsSSqXy5qdndXJkyf1u7/7u+H18Aglrnl4eFjlclndblf1el2rq6uJzeqdPqeNCNrAwIAuXbqkH/uxH9Px48cDyYLA0VZgLX1wiOfxCB4OT+IeAMElpqIvrpy8hkQ6pP9RJm/H2tpa2KRzaWlJ/f39OnHihDKZg1IsBALjhOfgusXFxRCYVyoVXblyRSdPntSDBw+CYYMswCu51+Ceznw2Go0EbT4wMBA8W61WC4LtcM7v6/dHTm7duqVMJhOWcGAoXakwqL4bajZ7sOX25OSkPC+KDOD5nQySFPpL23u9nqrVamApW61WUExvi5NTIKE8WfFcLhdqwlAkLPTy8rJWV1dDgpXg0rWShkKFtlqtQDO2221997vf1ZtvvqlLly4FSOY5Gyb6iSee0IMHD/TKK68kdiSi3MYFxL2HMzFAGyxWuVzWiRMn9PLLL+vJJ58MGB5l4TkIMYOIN4s9mzM4Dtcojdrc3NTa2logR0gO7u7uJmrCqOlylm1kZCTQtdVqNUFmZDIZbW9va2NjQ/v7+1pfX1etVgv5Ja8SyecPts1iPVG321WtVtPVq1clSTdv3tSVK1dCPEkFAhAVtOACSbvX1ta0ubmpWq0WhBBF7HQ6Gh4e1vj4uEZGRhLMIR6COYoD9p2dHb333nva3d3V7OxsAub1egd7gdy/f1+dTkelUinR39nZ2QBTvXyM5xD7QX64gnpdoK+cJQZOY+yQm+Xl5YOEMbtYUmMHXQkzRXHh+++/r9u3bwfr6w+nUV40iCIBh1qtlt5//32tr68HqtMtjf/+1Kc+pXw+ry996Uuh086uwBRhjcibYOkZtEKhoFqtppdeekmf+MQnND09LUlhxScQhTyVZ8bJxHNfoK0zYk60bG1thQDeFzcSY1ASBUzyvIzXp7ExIkG6rzr1/S36+vo0Pz+vubk5lUoljY6OamNjI5ACS0tLgehoNpuanp7WpUuXDqxnPq8TJ04kBMmNi3tEoBzeEchdLpc1OjqqbDYbqGXipImJCZVKpYcIn/jAU/E3ynTz5k1tbW2FlQh4wBs3buj27dthHvAU4+Pjmp6eDixhbJxdcZy9I2QBRYDCYPk4n9IoZ4TJs2Lo82NjY2HCYas8SeflKkykxwYcMGppBX78vn79uq5du6apqangXbAanMe5n/zkJ1WtVvXFL35R8/PzodSfzmM1fcsv9lGj5OXjH/+4fvzHf/zgtRv5fAJioOw+kTBmMFhYPH47/Q6Vvb6+rt3dXa2trSVeYu0EBXEbUKRUKiXitomJiUCbz8zMJMYTNrLdboeKEQSavBHKOjU1pa2tLTWbTd27dy9URZ84cUIXLlzQ0tKSZmZm1N/fr7GxsUD3eiLX+8hc4kWLxWKI5UZGRsKuUwT6uVwukAwO42JmzRWI/x1RtFqtwPaWy2X1ej2tr69rZWUlsIM8q1ar6fTp0zp27FiAZ9wTOA8UjHNisHkbGxthnV21Wk3UGfI8ltlvbGwEGUQ/JClfrVYTCoJlwg1jLbHANNKZMM7FmmG9fbCwNl/96ld18eJFnT9/PpE09HvxzA9/+MO6ePGivvOd7+hrX/ua3n///bBtMlSub10MPn766af1qU99SpcuXQrwgPbjsTyD7wfKyUAyHl6u1Ov1Asu2srISPBwxFue0222Njo4GcoY3tBNfMNGSNDExEbxkJpMJa8Ioe/HdTrvdrubn5zUyMqJarabV1VXl8webv+dyOc3Pz2tjY0MDAwM6ffq0Tp48qYWFBZ06dUr7+/s6fvx48L4Ou6C53ZhiBJwCHxkZ0djYWPBCjDsoJU4Iu2H1cMAVyQ/kYX19PSguY+OkQafTUa1W09mzZwNlHRtvZBmBx8CR56vX68FJ4Ei8zGp7ezsYOVCQIy/ani8UCiHLDqRxZYKJIdj0PefcA9FZr51ypUNo5ufnde/ePc3Ozia8UhoEgI35+Mc/rnPnzunXfu3XtLW1FWKSfD4f4q3+/n5dvHhRn/rUp/Tkk08myjpQALd4tB9lcoaLe9OfuPBzfX09vIyKXJoX1aJE0ME7OzsaHh4O3oOdgIiB2CeAgk4gpW+EWSwWQ3JwdXVVGEA2rAGmUmt26dIlZbNZHT9+XLdu3dKZM2e0s7OjkydPhmJYqGpJicCdecUyE2OR4GQPOWh6xgzmC2WKhc3/jpXnqAMF9LIp2trpdDQ9Pa2ZmZlEGZMfXk7mcZkTEOVyOez5zRj7tf7D+HAP2pjHujYajYApY5g3MDAQcLC/4xUB5LdbHZQReEK8MjAwoPv372tra+uhglgnHxzn9no9zczM6LOf/ax+7dd+Td/73vcCOVIoFPTUU0/p5Zdf1smTJ4M1ZKDdY2EA4toq8mDupVB+FwgWks3Pz4ckM3vAcT9IisHBQY2NjQUPwL0HBwd1/vx5rayshKpmavrYvgwFIyk4MTERhIElCD7mzM3Y2Jja7baefPJJbW9vq1gs6ubNm7p48aLW19dVLBbDtmnQ/XhQnkexMZuCUCIGDCwUChoZGVG1WlWv19OdO3dUr9c1PT0d5gwPlwbZEUinkF2eHH65IvG9V0rgHX33q1iJPGb1dEvMxDEPvq0yn5N/4p4u89wjD9O0tLQUqpShLpmgoaGh8KoLhwQx1vWOc2AFeCv1yMiI1tfXtbCwEDYf9IHkHnzmAzQ7O6t//9//9/WP//E/1ve+9z2dOXNGn/70p3X+/HkNDg6GjS3itwK6FYFMwGjg3dyL0jdJgYni3bpra2uB1nbSIKZGqaJngRzLuC9duqR6va4TJ05oZWVFQ0NDOnbsmEZGRjQ3Nxfq0PxdPZI0MjISPj927JiWl5dD7AJ+b7fb2tjY0OzsrMrlsur1usbHxxOkydraWkhU4j2AbcAdyCeHdywlmJ2d1enTp9Xr9cKrfjKZg9Wqg4ODiQJolCKGdO6V3HByOILw5DoH98PgO6TjvpRVUTVB3gkEAzxjDvF0To9nMhmVSqWE7GQymeBM/MiD9dfX10MnCGg9AATiMWkE6mk/cS6HyaYcaW9vTw8ePNDExESwMLHy+WD6ZIyOjupv/+2/rT/+4z/W9va2Tp8+HTybu25gCvdyS8hgOxzlfy834j77+/taW1vTwsJCqJ6AcCCX4IrLG+3I6+zt7alcLuvcuXMJNovkH3HcxMREKAsql8va3t4OHmhjY0PT09Nh5XKpVNLi4qJqtVrIs2F9aRNjABHCeiCoerwPQpbJZLS+vh6YRbz9zMyMMpmMzpw5E7z+0tKSVlZWwvwgE54bjL1TDJPcaMXeyWl3VxA+8zg+Zv5ILUAGwagSjzoERXFQWI/5HdJzz6OKV/NUIFNywvZGVHqTSyI/xEARBzgGJX/kCigp5Kdwma1WS7dv3w5V4hRsIohOdzM5Hsz29/frpZdeCgNEqT7x3eDgYGDQ3NN4cjWTSb4biEGFAatUKoFuZw0WuSu8VLvdDkWjkkI8IR2+TG1wcFAnT55UuVwO1d5TU1NaX18PmBwGLpPJhJWqtAlhxxCVSiUtLS2pWCxqcnIysWp1Y2MjVM8zX81mU+VyWW+88YaazWZQ8k6nE2AMhhODSnw3Njaml156SU8++aQmJycTK5upkmD+KSUDQrm3YezjuJnPY4WJleeoWIui2/X19UC0+DP39va0srKipaUlra6uhriOCm9XIuTEjbqHK85GpyrS6OhomGQEGYGEBoYzRzhzuVwIUjmwODTIG0bSzpcRUJPH3s8IBC7bSQCvQyNuo0CVHMDp06eDS/ZkLFYGb+OUMpYHKIPwZbNZra6uamNjQ3Nzc4E+5YUDFIq22+2wJ4PDOFhLqjWq1WrYzwDyYnR0NHgirq9Wq5qcnAxKMTIyEmrHeB/VyspKYKt4TmykULxGo6HR0VFtbW3p8uXLIQ7MZDK6d++eFhcXE1UhsLQoP7T57Oxs4i0lxHG+r4ELXjzuDu3iGDgmH1wRYkXzGBp4vrq6qnfffVfNZlMTExPBSPBS5a2tLd2+fVuLi4uBoBkfHw+lVUA6xg/5itlG0Ad98ziu2+0eVH/H2kdDCTBZsoCiUdkQVzq7J2KQiLmwvihMp3OwjuXevXvqdDrBI5JsxKrxfHInDCzxEAzU0NCQZmZmlMsdrjh11iWGAG7pqGLGGzWbTS0sLGhpaUkLCwtBeVjcODQ0lNhIvlQqhSQxUJVqA+IjNtxg4oaHh8PCPrL0mcxB4hHlQHi5J+0gKQqso7aMnXLZdx2lzmQyocqAwtuXXnpJt27dCmU5xWJR4+PjevLJJ/XEE0+oXq9rcXEx8dJlTyVgJN55550AGRuNRhDKeIxRqNirxJ4Jo+xKEyta7DEWFha0srKi4eFhTU1NhU1K9/f3tby8rDt37mh7e1vb29va2dnR8vJykEUvRGBuXKF7vV6IqSQl4lLgYDabVZ5GxdiP5CQY0eEVnoMYA5jDQWdzuZwmJiZ0+vTpxC5FlM10u92AyVm8VigcvDuUknpyKZRtOAOH9+p2u1peXg5WBjjomWpgHNc6wdDr9VQsFkOt2MLCgh48eBDiDJKqBLcs95AOinJ5fymehbwKQbzXdFWr1VABMDg4GNgvjAsK5caEGKZQKIS3JfJdNpsN3g5DdOrUKS0tLWl5eVkDAwO6ePGifuqnfipUdQOJJendd9/VmTNndOzYMZVKJVUqFe3v7+u1114LhoBSLChuUMn4+LgmJiY0Pz8fqjYetQeDV6OAYFyB3NBxIJdOPsTwqtfrBaMvHW4kWq/XtbCwEEqqSJAvLy8HiFssFjU8PBwqv0ncunzAZgLBvcQt5BlTe6yHX7LllbJOQWL1oUbjo1ar6eTJk2HrLi8/YZEZTBuBdaVSCS9+ItHpb66DqWEQW62W1tbW9J3vfEdnz54NAT6DQWKNWMoTn8QKeFZPJpKE85dH45UWFhY0MDAQqqZpT7yakqXhFJcODAwkNtn0tVe8XsTzcUA/mDMq4qGku92uJiYm1Gq1wv6B9+/fVyaT0djYWFhgVywWA5x0Wvj8+fNhmQE/m5ubYRkHxg6kgFfq9Q72qzt+/Lhu3LgRYDb9dcImlicnkmJFciVB8RwWItwOvbgH8Jq+sY2c34vtw8jpAdmbzaZKpVICrmKEeQsIyuMb3NCGVEXiRvz4hh7Q4U5NE7OgoVgdCAPWzUgKxIUnPtmIw7eRJZvsTCDCSpAcszbsFDMzM5MgFWgjyuK1eQ4B79+/r5WVFd2+fTswc9SSoVD9/f3a3NxUNnu4xzUrV6GrmRAIBBLP/nZ3hIxENZ4POMx5jEkulwtLE9hkEWKDfI/vEgRc6XQ6WlxcVH9/f9hgHmNGRQurpPFW/f39euyxx7S6uqrV1VXV6/VQyUBFAeVYo6OjGh8f1+bmplqtVlj6TwLfhc1DB/dIMdPr18QQjzl16EXcyxL6Xq8Xcn7EjCg2skp1jKSQW+OeKCSJacbLYaU/O+GR+AIhJgZZWlrS2tpaog7L2R7n8im5AI7l8/mwN8PJkyfDTjAOs/r6+oKnwINIB280IO5gwRZekM7QSSaVJc7EO3SUtjiDh8HgwJJhWX0RGMu+8ZB4Rq86YL8JlKNarYb/ia/oI21nooDIKAN9BG44nexsIB5scXFRuVwuEELj4+PqdDphER+vuveUBR4PA8Y+CNVqNUGPr6ysqFKpaGpqKlhiFIY2joyMSDpciUuVO22OhZ/PQQDuVdwLHRVPpdHp0gHJRIwJc9nX15d44wZKgXFibH2lgh9OtXv+yT2s9AP6G4uGILEKlTL8lZWVEKNwM7wSezeTH9rY2AgbcKDdBO2zs7MBLsHgUQyJVaZMh435CAYRZKwZHfF8F0rN6z0Smed8PhATlPrAxCwvL2t3dzcsN6ZAlIAexgwjwve0BW9bqVRUq9WCNyDGwOuwZIU4j+XqeGkmC9IBiEX8xvN5NoQGC/NyuZwWFxc1NDSUqNInMXns2LEgLI7v2UAReMOY7+zsqFgsamRkJBSEonRAecYcZEC+yhfEMQex8DuUc+HEO8TW3wUbQ8h57tU8bvfkKs8n3mX5B23NZg9XEuC9uHfMHsY5y/xbb70VLBeKwyTQIKAdQSbWAgGhErfdbof8Sxz4bWxshEoGgjcEGchF7IXlw+s5lAP+SIcrcqnIIDPNntls1O6QE6sJbFpZWQlejPX7TDKlOk4wAJvoU7d7UNA5OjoqSSHpTKzAnhJMNPka4BET5RTy8vJyIEjw9qyMRdiJWcHwXk3O0hf22FhYWNDy8nJgXhmLvr6+kKtbX18PsaTDWNY9+dsXnX524glF86p6DmfiYsGMIR3nxgrkP/FncWlYPOZe+uVlb+7xfS6Ai2ltw5M5O5n/vd/7PW1ubmpzczPg20KhEPIEnqxySOCVDHgDhBk6FyjDLjYueFgemDhwK9aBsg9nbXxPagbTtwlz6LS+vp6YeAJzJxu2t7fV6/XCEg0qjVFiBg3o0u12EwQE0Kzdbod6L4dhKDaTJB2usI3peYc8eCQmtdlsanV1VYuLixoZGdH09HTINwF3UQD2KIAUQPGy2WwicQmFjuCxxoa2EBNRZe8GrdVqBWKIZQh4OVcAt9oOhdyiu3dKU5BYkBmjGPa5cccLoeSePkGmWIJB/OohDaEBMo1Rce+FIUNZ89/97nfDgyhpgd1hLQiKwA1ipULIHEM6K0Mc0Gw2gzVD6bDeHPHqRbLnkhLPwgKz3BnBpySeHXeoDwSzQ4Pu7++HTUVIrsJeSYcJ4lKpFHbDAT5h+fBCTCJ4nMw51tA3kHHvyFhg6bLZbGDtoPH5jgpsqPZer6dKpRLyNowvEJF5o3IEkqTZbIYx80ptlnfgLYvFoqampoLCeLI7m82GuLFer2t+fj7Q/A6HvBgZxaLt7rE8kHfj7N/FSVAnL5AJfnsc6spJng+kAIx10glI6AbdlcrHw71lHi/gwknMw+pEp589oRpTj15RwKA7pgQabW9vhyw6lgO2CoFh8J0goAMeSKK8sDA7Ozva3t5WLnf4qke8gFs/GMmtra1wDV7RV3xCw+PuUXoofPrOvnPEX5TTuDC4IGDpIC+AI9DsWFJXasa03W4HhpKx8XsAz4ndtra2wlisra2pVqsF6y0dMqmsANja2tLCwoJGR0dF5QtjjtdDia5duxYUyb1EXK5zFDPHd5ybFjfF93BFcsX1c7gncWRg134Q3+NhPFcJzHX45vk5vyZW5Dz5Da9S8OQUB8Lq1hSLS0ccq8IScQ3/cxCHMRDsy4a3QFC9pJ17EcCTpMSaUFUuSdPT04GWpcaMtuOxms1myHjDNGGtiE1g3iSFcXF46F6QKoNut5vYL5pnuzdHiMnP0DYobU8mkxjHkEBKYEyYaAwEitLr9cLe4ZlMJjCqnich2U2SlnaxyciTTz4Zlo7D/qGcr732mr75zW8Gj+/5M49RUApXpDiGckXw+Mmrv51kcEjs6QS/F7k5EuU4AnY8orqG/kM0+eI+T6/4Itf4yI+Pj4fyEyYPa0oncHFMNjklBsUDSSm5gpbvGSCobwSavIXTwAgf2NRjH3A6loS1VBAm2WxWw8PDiTUqe3t74d2lXEO2O5M5XJ/P5GWz2QDpIERgqTiX/rH/mZ/HUghwOfESY+H1XHgKrCATx7IFZ7DwQIyr72eB4JB0hGiYmpoKsSlxKvsHss+dvzYSz95ut3Xjxg3duHFDlUpFJ06cCG+smJ+f140bNzQ3Nxe8KPOD53ca2xGKW/FYkdIqIvx8p6ZjssLzgtKhgQLaA3lhgkFhQHviZOBbvBwE7xRXWgRFovLaYx6sPa7Rg2MmkE4ymQiGD0bsAuPAk+LCpaWlsHMmyVuey2QwyVQHOEMD7GQ7XA6v7qYtbOvFG+a8PcCq4eFhVatVDQ8PS1Iok8lms2GnUcYCcobKbjwHyu6UdgxRnbjxQNbHFSND1XkMXZgXYlEgaKFQCMsrSCfk83mtrKxofX1dvd7BrjwwtXgVFA6ISt3he++9FyA+92IefHxd6T0Yd+VxYoH/XTDTYiT3RrF88bcfDuXY9pl8Il6KuY+NGorGHIJ8fGMVR16ZTOZgqblbD+CTB4YuyC4QMR3pQsm1/huh8ngJq8C9c7lcoLWJmfghMMxms6Fj7EPmz6CsSDos0eEzPAiGA+HxrcjYOw/FjeM29w7xhiQwiFS1M26Uz/C3jyveFyUit+alWCSAseqO1YEh/h6i5eXlUAzsr3nc39/XxsZGSNjCwGWz2bBkRDost4HJRTgxTM5Q0ndvr8cvXkMYK4jH0GmeypUmlq+j/qatzAOIi30ZKFhFearVaoC4GGWMIsv/GdvYANC2PLGL15/FeDa2rO4l4gOF8Af54PmgeJyF6+Vaym+4J256eHhYExMTGhkZCYrkAuZ0d7vdDtQsVh1I6QQISc/R0dHA9BFXENfAMkICeJzU19cX1hAxqXzO84CkeCAMGIwQWXnpcCkEr6JEmakLlBQqC1xIgamDg4O6d+9eIt/ECmIgI8YSYwA8Y6MWDJ0vZsODwnwRezDuXpNG/s3RTsx0eSjgShUrnF+TJnNph1PdzAPxr79BEBnF8DI2JL7pE4qFo3GaPZPJHFZ/e5FkvCbDL3Dr4d7JO8AAuOuLvRPxABDClY5OxVCGKou1tbUQAJMcddgJ88R6omw2m6gZ5HwEGpob4eCeWGJcOt4O4XDSwIXAJx54ybjFOSQ8RMK6/aBe7s6dO6pWqxofHw9vx/BiXenA4MRCubGxERg7vGSvd1B/xsI/4CjCQf7E96CgrUAeWFkstW9/5amCGNJ5rVoaI+c1m3znRxwa+Gfx4ePgMolhwxuxvIVqdWdM4/yROweH2x6q5LHKHI79PMfxQfDNr/eBSjtiqpTP8HRe2uED1ukcvpbkwYMHkg6YNPB6X19fyGvcvn1b6+vrOnv2rC5cuBBgjaRQLQ72xfqwTxsEh0MVz5Z7UhbaHJKGHJk/D8WgFAsviXCvr6+HWsJCoRDe8N1qtUJ5FknR6enp4I29SNXTEJLCHt94hXa7renp6VBHSJK5WCyGFxWzLfLAwEBYOkEJGMgBpcBaM06SgjfEOGK5PZ6LZcPhMrLmsbVDxDSD7DLp8bh7Lr83HgokAO3t4QKy5BDe43WXC9qbRwj8gVwYW9HY9fJdrP0+WG6JPAnpcCF+ZoypY6XyxCyU7fLysu7fv6/NzU299dZbIaN/7ty5AEdpK9YE8gC6mwHm3vxPnoVJ3dvbCwrIOawJYqkDbQZSrq+va35+Xvfv3w/V0dSo5fP5oFztdlvf/va3Q40buYtcLhc2MuHFYBgcN0KSwmpf3iBOCVM2m9Xt27dVrVbD+h3ecsfbHYBmzBUMFmPYbDaVzWZDnhFICGzGSzMuXnUdy0OMaFy5mO/Y86TBPr/O4WPavfBAQDg8JoqDQtFfDJ4bBFCDe9I8Au70o2Nax5FxuQsP8yO2FGmd4ceZQk+YQq/zTM8x0FHyAcvLy3rw4IEePHighYWFUHSbyWQCq0RCFuXAspZKpeCBgHMoqtfusbQDoZcOV612u91QKQ2lyvuK8vm8FhcX9f3vf1/Xr18P9WokBJ1K5bnkMdgYknf9NJtN3blzRzs7O/rmN7+pU6dO6emnn1a1Wg0eZH//4JWbGxsbAcpgOSuVSljVSxt5swMxnBNAnt9iHjjHZSEmPnq9XpgfjyM91nE58e+Ogmpp3/u9kAv/m++dJCJhLx2+L9nlH6WIHYqvS3La3dnqPBg+hm+e94kxuCtHfMS5gBg6+nneUEmBtXLv5IPp1RS5XE5ra2v6/ve/H156RrGql4gAqSQFRjCfz2tkZCSQCrh5LA3nepEnbp5lG0AXSA2SyLlcLlRA3759W9/61rd069Yttdvt8HZylmP0ej1NT0+HoL7b7erKlSuSDmrnlpeXtba2pna7HV5Gtrq6qrW1NXW7Xf0H/8F/oA9/+MOJUi0KYSkFYgxgPSnEpfKcKg7qGDEi5FFYeBinMkg7UDlAHOU7ELkRdMiPHMWwzuXEFciP+HqOWLH43hUEj99utwOTSfxMGRyG0+UQJYXBJBHujiUokl/gBIQTBgxIPABxEBl33AfNmTtXDhQutj5xToE2NhoN3bp1S7dv3w5MkyufL/VgEmEnsbjEGl7Jy4CzvJz/YdwoIaIanKoGknmdTkf37t3Tu+++q7fffjv0Z3h4WM8//7xu3bqln/iJn9Do6KiuX7+uv/pX/2rwmnfv3tXTTz8dnvnmm29qeXlZ58+fV6vV0q1bt/Qbv/EbAd79o3/0jzQwMBD2/CYpTXDs9WJY5JmZGd2/fz/EBxxQ7l5F4fGgz20ulwuKiVdmAaLXozEnXuWAUXY5illcf1acl/RzY8XyI4aOfh++w4DjMDCOTiQ4KoOQYZ79mfk4GeaKwIOwyAwGSoBn4O/Yq6XhYA4mO362K5R7Kw8U8/m8bt26FWIiBI/qA19TwhokBIv7kh/wBDTPckaPpcjOXoGhgU0kZYkHyPxL0qlTp1Sr1fTYY4/pySefDK/YHB4eDu+KKhQOd6GFRctkDt4Afvr0ac3MzKivr0/PPvusZmZmlM1m9Ru/8Rv67ne/q9/8zd/U888/r6mpqWD4Go1GiJMgHaSDZf+dTics1HSlYDUtOS4MB94pm82GglZfZ4UiYaQcDvMbofVYO80jpTFtsaeJPVKaV/L7xhUQzC/zTngRJ3o9n4pcNhqN4H195bKkg/cjxV4gbmQawcBnXJ+GMf2Iz4kDQldovIMTERACY2NjWl1dDXvOoXhUHrD5PLEUg4/Sk7FGOGCdGFD65aVJfE4SjxpAGB+HqdlsVmfPng0xCLvazM7OhgWCfX19YSUn/cOrkRdz6+jV6x/+8IfDDj6//uu/rtdffz3ESWB+CkzZ044kZLd7uHeF5w5ZGNjtdhMbgHobPDEMs4VQ8jnlRXt7e0HgIGmIMZ0JixUAuXMSK5YTl604L+XKkwYJXRHdG6eFLbTT/8a4Qv1jOCQdLDXnZM/ZuDYihAh67FZRAnfTnJcWRBJLOMxz1+uL3GgHz3///fc1NzeXoGrL5bKWlpbCBLNBCTtqetsRCpY8xFsm43koniVvQBuYMIQdi86ksp/CCy+8ENrOOhd2Spqbm0sUUfb19YXdUImhwOLAJp8fyl5+9Ed/VF/60pd0/fp1lUolnTx5Mig0gktM6P1jp6C+vr5ArVPihLWlgNOhPYWrGCNiu16vF7waCsl4SQr3cpmIDbd7jqPib58nV5747zgccMfgiIcjJhj4jQKBeJjvUqkU1iyFAuqLFy8mOuIN4aEIhBerutK4V4pjoVj5vGNebOkxWrd7+KJlvNPi4qLu3r2rer0eckA8c2trS319fbpw4UJ46e/GxkZQCmAbpTzkVnyJMRtgxJAT2IfiA3kYfLLlbmwIwhEu1i1BE29sbIQxfffdd8M9C4WC3n///WDlKCtqtVqqVqtaW1sLOwVtbm6GNWN37twJW2mx5x7j6IvaMAqDg4PhhWMoGnEMFRO8lQESBAhKzIWnpp++l7h0+NbAXq8XkuHucdLY3g9CNX44ne6KlVb46s+gTZwXM9G0I24L14GOiJMZg/ylS5cecqFHxTZOM7oliJXF/3bqOr6nJ3y9MygYdXlvv/12qNYeGxvT2NiYstls2Ip2f39fly5d0l/+y385bHG1vLys9fX1IEAIAcwUVC7u2ivdgXK+aA4c7RPe6/USWzsDbdwoUCjJYrps9iCR6+/I7Xa7ieoBr0IuFA72/ab6AU8Gxf3444/r3r17gQlEYIklWWODNd3Z2Ql7izcajbAHH9R4o9EI7c1kMuE1Mcwf5TKej2Ezy06nE/brw7gAg7zCw+Ui/vuDSASXT782DheOus7hdJoH8zYxH06KEQ9jtDDEeZYspx1pjYmPowK+tOsfdb+072DFYMaeeuopnT59OiybgI7c2dnRpz/9ac3OzobJZ+dPFyxJQVFQICAekwOcwxsiMDFERWmglLHwlNtwDgrLPmq1Wi3Qr9LhehfiIIJ7sLikAPOAvF498ZnPfCYIRavVCmuiCoWDN6WPjo6GPBp7mI+MjIR9HRB2vAxweXBwMCgVfc1ms4kyK8gR4rdMJpNIcHc6HbFMhxSEC+9RcVKaEfbz/FxYQfeGaQeK4dA8JjIcAsasXNyOeCV3nj3Z0o7YbR51+A1/mHv5/255XFidyBgbG1NfX58ee+wxFQoF3bt3L5S6dDodnTt3TufOnUssT2g2myGRSVy0v78foBKCSt0UAujJYScgfAJROISO7YC3trYC3JMO2TyPuVg6D6uIkLZarUBGYPkdWjurCOPo484ycbwMcRkeiOc5WeIVCIw3MR95JOJXF1a/D0QJ3ltSQiFLpVLipcwf5Ali4Y4hVyxHtC2Wx0exeWkIjHultSNWfqAuech8Pn+wjIIjjRTg8x82APyXOWJICet24cIF9Xo9nThxQl/96lfVbDb14MEDbW1taXl5WT/6oz8aXoeCtd/Z2QmwBfIBq+uCQvuhaRFYBMsnxbPmHnMQY7AHBALOUgoYLZK8CB/CiWfI5XJhAxImlV2SWPaPoLM+iWdLhwWV/f39YVve5eXl8AKyoaGhxDt3WfyHlyawhgVF0YjfvGIBL4o3d1KI7bx8M8yjAvpYfo5StA9SvjjfdJQixf+7AXcvFJMh/oN8QFrlcrmjtyz2B3+QIv1ZHf48flMvt729rUwmo9XV1bDIbHV1VZVKRVevXlVfX18oIOW61dXVENyiYJ69RpB7vV5iTRGVwVDjXqPngkuiNp/Pq1wua3x8XJKCB3CoBhTw3WRRHu4JDKRagjeBUMLES6+Ag5ISyullPVTOcy5GACFA2MjiM95403i/CPrjMI6DfrDHha/fkRR+/2mOR6Gi2Hv5eUfJ66PCi6NY6Tgujr/PZDJJRYof8uehPB90wCARkJ89ezaQDzs7O7p48aKmpqYChYu1r1arqtVqWllZCRARqysdlHs404SFhXhwgXEqlyA+ZpkGBwc1PDysXq+nlZWVhPCwvqi//2AbX/cqKDFwyKl/6u6ofKB6WzrcZRavVCgUgrdhe7Vutxv2GcfzUMlN0po3aWBUoPLZngovDazxtVzSYRyYyWQSUJnx+rOY/x/mux/2ef8yIcqjiI+HTEWau/0XPf4s7sGBdWHhHMu/mfinn3467CzKhONFhoeHAzuGCx4eHg4vM2bgEPA4X8B9ICbwRL7kmAOBJ+9D7odnAJX6+/tD9QBJU7wHwu0wE4VEuD0O4cd3OmIxI1txUUFOXEeuh01d8FC0Ac/jlRoYKH828wzcJPHLor9HzX1M3PyrOH4YGXyU44g9T9p9nJzIf9AD03DlBx1/1oNEgxcXF/VHf/RH2t3d1cTEhAqFgs6dOxeCamIqrwwg/0XykYVwWOi0A9gD8yQpFKsiOJ7zwjuxjKJarYY33+EFUHI2ql9fX08sby4UCmGrY8aQeK3T6SQ215yYmAhwEa9E+Q4LH3u9g4LY4eHhRBWCbxNGvAc8RGGkwzcOUsxKVQixI+OHQUCBvGzmqOMooTzquz/NcRST/MM+N0154nv6OflHPeRRmPSoc/5VHFj/1dVV3b9/P+xR/uSTT6pYLIY1PrOzs4FM8Noo4g5eREx+ymMHMvpePeBJRS8l8gEkPnE2Z2RkJLBwxEReO0a1cb1eD3AMOOSQiTaxxwJ750nS1NRUKBLNZA72q+M1JplMJry7iFwH98dA0L/BwUGtr68H7xnHAsw5beMa3/AS70c64U87x39RjrQ46U8fBf45Hd5gBIEVjWfPng1v1JucnAwBOTQxbFg+nw+1bdTkuWdxOIWl8V1jvDocMgOG0K/DSjuZ4Out3MOMjIxoeHhYGxsbwYNIh0WkMGS8vJn9t2/evKnt7e2w9XAul9ODBw+0urqqRqOhXq+n8fFxjY6OPrRJvD8Dj7eyshL6D5QD3lKuhCHBAFHXB5VPhQWMHvP2F0k5/mWPfylF+vP0Rvfv39crr7yiubk57e3t6fLlyxobG1Ov19Px48cTu8XcvXtXCwsLoaas1Wppbm4u5GgQCKyzpES85Fsx8cP/KAy5IPc2kgJhwXlUWrOkgw1IhoeHlclkND09rRs3bujmzZva2NhQuVzWxMREEPapqSmNjIyEVbgYg+3tbY2Ojur+/fths01J4XUyLtgot5dckR9ijwZnoDASKA3Gic1ZoMOlwz3fWRjJ8RdFkeJY90975OPcDTf/1+nodrv69re/rTfffDPg8SeffFLSwY6qvV5Pt2/fVrFYDJXMksIycVbOEn/gRYilEHJXMK+x44cSI+ALpAEC5MrmK0ipVEdIx8bGVKvVguIXCgc7ut68eVNLS0thEaC/b4m1U2+88Yba7YMti+k7/SBGK5fLobwoXtJAv2izU+mdTidUYAArqeTO5Q72YSDpiwKNjY2FrZrj419nmfqzPv5CQLvl5WV9/etfD/HG+fPnNT09nVhfw2aIc3NziQToxsaGlpaWND09HWriPHhGAMi9OJRB4fy1MlDhDgv9IC8FvJSk1dXVUGqUzWY1Njam8fHxcD05qLNnz4Y35OHtqGbP5Q7ex1ur1bS8vKxjx45pfHw8EBdAMXZBipeL4I3wMhhQoC9C7/VjwFAod1hF9qTglZppSvTDVLs8as6lv1jK96+9IrVaLb3++ushF1Iul8Mq0r6+Pm1uboYNJmGf9vf3A839/vvva2FhIRABvDPJF6GRmESZJIVYCuGLq4RRLur9XLFgBVl1CuvGc3ghGffE0u/v74ekK4WllKEAz2ZmZnT69OlQKMpWu2wl5jS0K490WGGB14Uyx8PAHKJEkAuQI8RDQ0NDGhkZCV7yz/r4s1agfxVUfHxN/l9H9+usyNzcnO7cuRNw+fHjx3X8+PFQ1cw6H6+fI98yNzenGzduqFAohO2siAtg4FBIr1jw0g/IBQ6HwShfLne4FwTnuPWXDl+tSaWA08QYAJYt9PX1BSVCKXwzFvrK8g/iGHaL5RpvH1ULeFtobApUqVv0Il1JYaEeREqpVArQ9M8q6fr/z+NPK/MPKdKfRWP+VR3dblff+973wmsZp6am9NRTTwXhwQvx4l2wPXu13bt3L5S3sCKSGjUSpCRbYyYPiOYLA52l44Cm5t7cz98qQcXD8vKyer1eojxISm7i6Lke38qYPeSAYxS7Us2NR0GRyOlwTxTXIR20P+/N7XQ64c19lBthwGq1WmAaqWT4i378y5AMcS3ev5aKRJ5md3dXa2trofJgcnJSU1NTWl5eVrVaTeR7yLNg9dmFp1qthrVMCB/CzMIsL4kneCcmcjYLwfRcEt8TW1CFTQwGPBoaGlK5XA5JYZbJu8Ky2te3CyPJjFdBCSTp3r17IeHrDJu/uFo6fOFWGoyBencoC5SUDndQfRSp8BfxiJXoX5a5+1Mr0p/GIv2L4NFe7/BtCevr67p//75eeOGF8HKu/v7+RLGmdLiiFMuPQKJoe3t7qlQqiRIXVm+Sc5HSV18SdPsGKb4+yN9g0G63tbCwoF7vYD+ESqUSXppMqQ5v3mNBHhUYwNNYGWgvUPP27dth3zu8DXEb5zJP9CUO4tlAXzrYgJ9XgRIrFYvF0HZfJfAXhdqW0tt6lBIdpUw/TF/zRz3sUcefxyACM3iR8N7eXnjDOKybdCj0FHbyN5s08mZuCjO9Pox+sHeDf8Z5LIjjZVTxHnIkO9fW1pTNHrzhfHh4WOPj4+FFxvfv31er1Qpvudjc3AxJZa9royTJV+bG7BdMZKvVSmxJHG+zyxh68awrFePGauOdnZ2Q/GWZuW9i+ed9/FnWfP55HH+u0O5fZFA6nY7u3r0bXkSWy+XCSlT2jvCNCff29rS4uBig0tLSkiqVSvAisHXAKRKtksJ+4sQ1tJMge3V1VSsrKyEHU6/Xw3cwdXioQqGgiYkJjY+Pq1qthhcnLyws6P333w8vQev1Dt6YMTw8nNhE4//X3Zf9RnZc53+32dzZZO/chpxFkseWEFhSICCIYAROHD8ESRzYcOzEQfIg+O8JkrwmLwkQKE4MBJCBxJYRG4YTCYYgKdJIkUYznOEMm+x9ZXNn398Df9/h14fVnLFkOUsBBMnue+vWrTqnzjnfWUrVTM9ElEiNRsNsIdpGBEzUlvNM5Puin218fByFQsFUutA6KVP/Mh3x/1vaYzGS6tejdO1fZKMTc2dnx6IBWGyiUqmYn4W2yvz8PG7cuGHp2OVy2Wq7UWUhdMuobDoZAVgsHo1+pjPwNPFWq4VcLmfETzCAZa8URj84OMDGxgbK5TKWlpbsYOa5uTncuHEDt2/fNoerEi0Z0ufu+LkmjM5zfr0tR3AEOI+QZ2McodplURRZEp4+E7hYseeX3T7pMx91/6NUOn+d73vIGX/ZDbqIv+hJHSW6uaO2Wi1UKhXzYfCsW/ptWMaX5YNPTs5OTqhUKpaK3m637agRNqpCmhLA7Fn2pVEPOzs7SKfTporRRiGwEcexBadq8Ga/37cA23w+b6rX6uoqbt26hc997nMmmfQsWE2vDs3X4eEhcrmcwfVUPwm5EwzRTUJDmHwYj0oaH9Gs7X+LTfTzNB+Y+7gtJEwuRH97kfppeZlHEQuN+jfffNNSEWiMM96Lx53Qy07QgWfDUg3TWs884pNIGmFq7sr0PZEp9/b20G630Wq1DGpnCA3TqRlZTl8LYWTWH2etBkoQRmrncjk8ePDA0se1WKXuktzxSPCsEqRlsOgHY9M0Er47S5vpnGtxS6KR/xeZRVuIrh9XKj1qboZi7Tyn/TInl5BuFEWo1+tGSNxpoygyJjo5OcH29rbVaWDdgE6nY0lts7OzBumOjY2ZQ5YHM/P9NGqBqg/Rvnq9joWFBWxvb9sxMTTCGQFAW4lIG1XCZrNpMPfe3h6KxaJV5zw4OEC1WsXk5CQ+85nPYH5+fki1AoYPH6CUePDggaVp8DNKbDaFrolwkhEJPlBdprr7v80e+TgtxDgqjS57f52fUfwQlEj+4aHvftFNdXwaz4uLi0aMdLwSGKhUKgDOiyBWq1XcuXNnqGwVjyvh/0TwaGiTiLiLc2JJ/K1Wy8J7ms0mABgqx7RqtdV0flhGmAVReKYrA0739/fx0UcfIZVKIZFImNSgWuYXmPUoWHLs8PDQkvhOT08ttCiVSg2FCSkSeXp6dm4s0czj42MUi8WhTev/cntc6RO6z/OFuhSiKLoIf/93hQypNCTSdefOHaRSKTQaDTQaDWxtbZlKAsAAg36/j0qlgk6nY5Hfe3t7VhCFkDDVOg0sJROQkbjDl8tlY9ooisw/Va/X7eCudDptad7A+bGVRN74XKJ7zI9qNpu4f/8+MpkMtre3sba2Zmcs0ebRutKDwQD37t3DlStXcHJygkajYRHtHHej0QAAC1gtFosoFotD+VJ03pZKJTQajSHA49NY60+TlkYR96PG4SWS//5Rz/PvxP+T3hj97xTzcXwWcdDr9dBoNHBwcIBer2fM8tZbb2FhYWFIwnDH393dtSgC1rOmekdVTieaqo5GOTMOj/czKY6RCVEUYXt724CGer2OXC6HdrtteUZMF19fX7dyVyy0SAkGwBh8Z2fHwpV0DTQaYWdnx2Lgbt++jc3NTVNneb0GnPIcJvqkvIp4fHyMbreLtbW1Tx2F/WVuzKOcr5epdR9HSimPGCMBF0sRczDcrdXT/zj64uM2LwkHg7PTEu7evYt6vY6DgwN0Oh2zZW7fvo3r16+bykTYulwuo91uG9PNz89jd3cXu7u7mJqasprZ3O3JRACs6DtVOn5OotNSwoVCARMTE3YAGFXKBw8emA2Xz+etDnehUEC/30ez2TRYemFhAdPT01heXsbBwQEODg7w8OFDrKysmD2jUqTf7+P73/8+VlZWsLu7i263a/YN/U6pVMpSGlgzAoDVWNBGdXVlZWUoyPbTYKZPS9Kx78v+1+YZZhRjPep5ITxhiJFGPdwP5BctrTykPjY2hnQ6jUKhgHfeeQeHh4dYXFw0KcXQG6JoRMmIZBE963a7JkWYg1StVjE3N4dms2lEn0gkUKvVLKiVGbSse83EPob9sB43k+dqtRo6nQ663a5B87u7u5iZmbG6EqVSCd1u16BxAgE8+DmVSqHX66HZbNrZRwqBv/nmm9ja2kImkzG0EDjb5Bi/x0iEdDpt4VDqJ2Nfx8fH2N7exszMDJaWlmwz4kb5P6190g17lOQZhdg9LjOFnmOM5MNRfpkTq/6LwWCAvb09zM/PmwGdzWZN/ex0OiiXy1Z9h05UInL0EU1MTFjaNnNoNB9nYmIC6+vryGQyWFxcHDrx4erVq5iensbDhw9RLpeHnJp0wBLBo/O31+thYWHBIrHHx8dRqVRQrVaH6jzwXQkyEIJut9uWIk5m6vV6+PDDDy1Gr9PpYG5uzsqKURJRgrF+uFb7Ac7DqOr1OrrdrtVcaDQaQ2ki/xOZ6eO2y+yiEONQIwo1NX2Ai9IJEPgbuMhMoUH5zz/JbuFhdia4kSGuXbuGarWKVqtlUQRURwgULC4u4saNG6jVapa/Q1SOMDmjqpkGwLyder2O2dlZAxR2dnYAAHfu3MH8/LwBFjxZgaBDHMdWYJKFKO/evWsFSGZnZ82W4pjHxsYuZJNqOA8wXLSSmb8sclmr1dDr9Wyx8/m8Sce5uTmsr69blDyZ8+joCMVi0eaZJcvIeJyjTyM57xfRPqkG9PNIo1FMxO/ITCHVLooeUdcuBEB4dO/jMpLex0EydSCbzeLtt9/G7u6u2TatVssio5n6DJzBzCzt22q1zElKe2Fvb8+QPDJEHJ9X/KF0YngOHaPqGyLRMYKcwa/MRcpms1hdXUWz2USlUsHe3h663a7lQLFQI4tc0pFMScqDlguFAhqNhsX87ezsYGNjA8fHxxbo2u/3Ua/X7f2ZNvHMM8/g6aefNhWXpcjS6bRFQWjEOh3S3JA+zeDUX5RN/bjPuoyW+bf+H2Ii348H5YBhXjD4m1+EghN/GSiePovpDnfv3sXm5qaF7rAyaTKZRD6fN3uBjMXIA2Zvaq06FrlnzQZOHo9x0UBPSgVGPvAkcZ6dqmifRhYwb4d2E2PdmBLBeWXaNoGBfD5vdg4JO5FIYH5+HlevXkW9XrcAU5YSTiQSBvfPz8+bNDw5OcGHH35oEr3dbltWba1Ws/6Z2v5p20f/HQiwt41GgQqegfxYQ8zEpuAcEKgi9MtkHjYVr4lEwup65/N5NJtNq7lA6UEUjBVt5ufn7exVRj/w2HtNKSe8rWe5qk1BG4PoWRSdBXQytZ0p7QsLC8hkMtjf38fOzo5FCJBxpqamMD8/b88liMIjaQ4ODgyiTqVSBrO3Wi2k02kr2thut22zmJmZQbFYtLOfeIwmz1uKoshcBox215JZnU7HUExKKDKkqii/6BaCionCftotxESjVLlHAQ5+brwUH4psUEmkBqifbP151IuMGsiohWNkAo1r7vZEqWgHlctlO3eVEocOV4Wxl5eXTS3UaqZxHFuJXjKA+nJ0/JRM6XTaVLsoOo/ApvpIfxTVRKpOp6enxrjj4+MoFou2KdCByjniu5PIp6enceXKFQszYmWhnZ0ds53y+Tz29/ft5L5UKoVcLofFxUVj8E6nY0e7cLM4ODgwIEdVl1EqzMdtjwtVj1IBQ/b0qO+B81Av9qf0S9XWmy2PC4GPakOMpBVwNCfGE9fjqgOPuwA0kAFY7g/rBLBGNqMKCoXC0GSwcOLx8bEVMiRCxpCgYrFohT6omgGwIFVC3XrSxMHBgRWc13TuZDJpAa1cMNpYRMn43pqzVKlUzPHK6kI8FpJ2GZkPwFC6x+zsrJ0wsbi4iEKhgHa7jW63a1HplE65XM7QPJ5EwbAkAEilUojj2Oo9bG1tWR4VmcynXzzO+n2S9b/ses8kj+Mv8j/e56NjHqX2hZjVf0d7N4oiJLlg6njVvy3fQlKYufD+2tCLP86kxXE8BASMjY2hWq2avZLNZlGr1axCEGvPAbBDxMbGxobgZZYNbjQadqoey20lEufnFFGKkZC974UR1wQxyDS0J7nLMUeJEDv9W5R0WmyScDzBEwDGVFRF6WzluxJtPD4+xtLSkhXqZ6IjmZ3luwi2UKLTnovj2Pxuy8vLWFlZwenpKWq1mh1O8PMCD79oldCjY4/qXxlFozgus5X4+1GM5Jk4NJ44js8ZCcAF5tAf/5IqsbQwx6MeGJoUvjRLa6koZobs8vIyOp0O6vU6lpaWhoy95eVlSz9PJBLo9XpDxMpgUEqV0DgZfGoTkzw/xZqpHZo0x9Mp+DelXbfbBQBjZCYMkoHpw2FGLNPL+TzOLSUoS3UxyPbg4AB7e3tIp9OWz0RUkf1S/WPsHpE8MhWl99LSkqmzdOxetk6/jOaJ+VHqlmcUtX10TfW3qn6XqXQhlZS04b9Lss6B/4IMouWoSHDeflLiVKYKLYaeBKFMy5c5OTlBt9tFo9GwhWfNBPZHwqV/KJVK2UHEVMU4PtoFWgeb9a55Dmscx0Z0fiMBYDlP9APxR0tuaeQDCZ/HbuoCkMHpOJ6cnLQCj2Quqp4cH9VREsPh4SG2t7cRReellQmQaF6T1rBYW1tDuVzG+Pg41tbWTNK1223UajWLqvjvZCLg44X+kIGUkfznIYYLXc82yp9Ke9zTfpI+Ft7Ijqnq8IfGqD7Yq4P+f//CJD6tka1pCNPT01hdXcV//dd/YXd319SkfD5vVXmYbUpn7PXr1y3g9MMPPwSAoQqihULBCFMXh3YQa7TpmDh+hawBWCYr30kjvOkzYt1tMkS/38dgMLD79IgVMg7XgPNHCcVadpqop3C7r6+gkRt6MsTx8TH+/d//HeVyGevr6wb/VyoVDAYDO6yN7/UoAg61x73v4/b/qD49wwDn0seDD6MYzAMW/hnczPT4UM53UiUNb/Aiki+udpB+Psq28tw/CqTgdTSa5+fn7aDflZUVdLtdW+hsNmtOSOC8gmkqlcJTTz2Fjz76yOBoxtnxMGJKDtZQ4P2aih7HsTGQghIqoVmRlJ8xWkB3KgCG9HFueHYTGUXVOi4e14KFXdivLiJ9arxOi0lS9dQ8pGq1iocPH9ppFtVq1YJ7n3nmGXum9434xnXUd/S2iN/JvYGvhK10ogHSqjo9SkL6sYbGrn2FVLkQXXtzxlehpTQyRho1UN6sOrtOoDZdBFX9dHDsj9mnJC6NB+POS/VnZ2fHjHMyTCJxVoR+cXERm5ubFiXAe5k+QPuIRj8T+vS8IGUY7vb6Q/SK6pXmMSnYwH7J3GrwqhrLaAqtxw1gaDy8X8ELjpPPItSuaqbWnVPiAGCF+emM7vV6uHnzpklGbhi6dl5y6C6vMDmvUXDAG/C6oXJD4LPI+JTonOeQse8b79fn6pp4BvJr5mlUhYc+Xz+nvcm5IzCUDHWqPwqFeibyD/Pone+PE6l16UIvQURtbm7ODGMSCqFxFnGvVqvIZDJIJBLY3d21qGw6LsfHx9Fut80RSVtG7TIdnybiqRNXnbq6E3EBdY40lwjA0LV8ngI8ZEDdnbW+hJ/32dnZC+ugiKe2OD6rZcH8pTiOsbS0ZL4xSjRV6XWMl9FGiFhHXcfPOU7/vwID9LE9rvrngS5tqln4771k0nnWd+cYfYiVAlUXGEkfoDupH7i/Vu2nkBqoqAkNY+4mqvYdHR1hcnISTzzxhDETzw9irBlr1jHUJY5jg31Z4pjPIZrVarWMAHd3dw1coOoEYAikoOoGwAqp+F1PbSoie6NOghg1f36++FsLs+haqArE+dXPlMB5fbvdRiKRsEL7jCDnWGlPhVR6JapRDMJxPIqZHmXHHB8fD6nMvjTZqDZqbKH3GaXehfoEYBs/pbmPlufPUKydNrWZeBP/151Yd0P9DLhY+ld3HlUH+B0jDWiA8yVYwJ1+HAaEUrpQ1XniiSfw8OFDWwiqW1EU2Sl3hIypGtHxynckw3BMFN/A+RH0amhSZ6YU5feUQjqHOjd+PlSt49zpvcrUIRVEmYeMzfHxJMBkMolisWgM5VPttc+Q6uM1FQ2T0Wv0PiXmkOHPsapPjhsQ3RWP20Yx+ii4W8fm14ZzQiY6PDwc8lMCGDJ9LNVcF0UHRdFIZMvvnN5u8rshJZUugF8wXThGKaRSKbz33ntWFGRpacnycfiCY2NjWFxcxMnJCTY3N1GpVFAsFi2KgCDEwsIC5ufn0e/3DbigtKO4HgwGBrGTCBnxrSof1S5OJK/hu9PWAoaLNdKPo/4ub1eyf0rL0Jp4e8Rfw0YJwyzcwWBglYza7Ta2t7exuLhopbhCjDOKSC/TPB5HSql6RzrRzGWmntDRHRqL0s9l0ij0zBCDhZ5BpJS0pHY0558bb9JLBb8bcdJILN7z7xlKF9szEfsmY/Jeqk4AzA/EPCCK1GKxiHq9jn6/b4GevCebzVqd7bW1NSQSCTSbTUvLjqLIAklZmlgNxf39fYOOB4OBwes6Rt25KDl1J9d358IpgsZcK55zRCZSZywZjDtfHMdB9SbEOCF1MY5j88HNzMxYdPnW1hZWV1eRy+VsDBy73xS1qR3Dd9Zn+eYZTyWw9k9UkjRGxJbqccju0/490+jGNkql1LF4jYONTL2/v2/Od248KmDiWNIodIfzDwtx7Sg7yBtpSoh+J+HCaa2Cubk5Sw0gRL29vY0XXngBy8vLaDQayOVyxhCEgum8JPozMzNj/hmqCkdHR1bvodvtDiXb9Xq9oUqliUTCEuE0Opxqm6JmfAYlNpPq+L2quLSlOM6joyOD3/lsxr5x3tgeR83hM7gp1Ot1U2kTiQQajYatBWFzf3+IUUdJF99CNKEMQ6mjfSi8z/+5oUbRcHlp/wxlFs+kISYKqXhsqi0NBoMhJuJ6ch10kxwMBkiq8aQToIPjrknCJ3KlUkelkF8UJSJyM4AhguHCzs7OYnd3F3NzcygWizg6OsL29jb29vawurqKe/fuYWFhAUdHR+h0OpbOQGZZWFhAr9dDt9u1v3kyxczMjKWK8x1oi9EByiL5nDAWqqdqx2gBLXxP5I2xcARROCa1XSip9IQ8ZTbCq8B58ZJRsW+6MXlipl1YqVSstoPPq2LtcK6hRr+PsuX8hhh6vh+jpyWF2z0jKRETbNHyZn5M/M3N+FHSyjORblTKhIyxZDYCaVc1Dc7b6enpOWo3SjyzA10kxe75WQgx0kkOvZRCkww6nZ6eNjWsUCigWq3i8PAQr732Gl588UVLurt+/Tpu3bplz9WSwKlUCtVq1U72o++EITtMIaA6FUWRJcxRD9b6D0S2mBxI6Ul0bnp6GplMxmwmOlM9xE5GoTRiwUu+P3DuTyFhkWl9QK0ih+zfrw8LvNCHxopM6XTagmSVSbkWo3yFuhnyWX4MIYBFmcgDDvq50hTLRqs7Qd0KSthedfT05hlJmSfEVLSbeR4xk0MNofv/41DU+UKlVW3+AWQYlT5UI7Qfz2RqFPodgIunvhvaKLQrAKBUKuHk5ATpdBq9Xg+zs7NYWloykUtpBpxV6FleXka5XLaFZd/c4QiNM+uUEoXpHJREZDLW/I6iyGonaDYrg0jn5+eRzWat6D8b1UJGWOgBx4zNo6RS5zRwrpryhHOVIrrAumPTZmTdiUQiYczPuDraerrGnrg8Pfg19E3VNr2HUkfPl+L1Ko10Q2fQL+edkRt+Q/bSMvRdiMlC4+RYlYm4DtqUiQaDwXmIkCf+kGjkd+xIYUCVRL4/HSwnkUTFXYzSQe0lOiUZybyxsYFCoYBarWaL3+l0sLCwgCiKzNdE6HRtbQ29Xs9ye/jcyclJizKn05c2DzNXyWStVgu9Xg+9Xg8ArMBIFEVDtb1LpZKFHqXTaaysrGB5ednCmTTPieqbnmahhfqB86M0AZg04qkXtAeZO0SdXdeL/jbW2mMNB02jV2JWxhnFKJ4w+ZluGP5eL3lU0pKxSBfeXUINQEsvqykQkkxe5dNr/Gav0kwlnULeXg3l9d5dcUG109+eEXzUa2jyVZf1u2RI3CvTcnL5IoyvA4BCoYByuYxr164hnU5bDg7HQ5splUqh2WxaAuD09DTu379vhx0zzYBVWEnAFOe9Xg+dTscmjp8zbo7JckwX39vbsxAcnpJRrVZRq9Wws7Nj597Sscz3ZBVZSjXg3AlLRmIOVBRFpobx3KeFhQU7YU/VKc5tt9u1VHXuqAsLC3YtbTxNF9Ef3xSNVAmiURGkEc9IXGutaEuQKcSYKlGYoEngRm1Gz0RK7KOkU4gG+X7UAtQ2UruNzyPAcEEisXkIVyfRMxY70O8UDmZ/fiG8yPXPBs6kHfOQ4vgs1L9YLKLRaNgp3pQyjUbDsmTT6bQFhq6urprRynJZjMEjQzEaQhEyeteZOsHEO0YCUHqx4hGDbHkEDH1VTO3u9/s4PDy0CqyUrrTVmOW6t7dnKBpznUjkPMSZdhjni3Yh55dqNqsqaXgXIwZOT8+qIy0sLAQl0eMwktozo2yUECN5O4mgihJriPlY8pk0ppVotW//499JpZN+x6ZFRqnWedpUmlYpaow0igm4A4SweX7v/Ueh/vzEUrXjBOlLDQYDK5ZfKBSwsbGBOD7LEiXqRN9IMplEq9UydY1q3v379w3qnp2dxXPPPYfNzU27hrv2YDBAPp83+4SMxIqtDJ+hHUbEiydKkDB8cUoygZbOoso6MTGBg4MDpFIpK69cr9ftPCjOm6/4yhJecXyWBEkHtaZMxHFs1ZLiOB46SJmwskYM8EgYvwahjZSE7ZmNDKHMoE2lg9rESuRKL8psnFvaysD5YXHcPEJM5FXKy6SuXksaIMDg+UG1MT7LIht0skYxQcigVcJXKeavHaXe8TkcJJEaSglC04VCwRL0Wq2WwdZzc3MWpDo5OWkQ75UrV9BqtdBsNhHHZ0Gak5OTZjMlEgm7Xo1w1mOg7p7NZu3EB9pMtVrNCkqSwWgD0cZi+gYnvtlsWnwbiZphSaenp5aHNT09bQG2tOU0hpHREZQ0tNuoAmqKB8dIxkun02i328jn85iYmLCD1UgMVFe8aqaE5Hd3ZRKusUoVDzL5Tfgyglb1kSirhl2RmShlQgwTUvn8GNi8baTrx9+kaUp5feYQI4UYii/o/UNeIvk+Rv1WRvJcrbZYIpGwBZ+fn8fh4aHl3NTrdVOB8vk82u02qtUqnnrqKXQ6HSSTSRQKBTsD9u7du1haWkI6nbZaB7SVaGuNjY3ZaX9kNk15YKgN+9cTKtrtttVMSCaTWFxcxNjYGJrNpqWG82CxVqtlC8EqQnEcW3ESoo/j4+Oo1WpIp9PWL3dm2kscP08kB86jAhhDGEVnJ5e3Wi3Mz8+bY5Z+LNptJAr1K+rGGGICzwwhaRZijpBtrIzpYXJKUkoDdYjG8XBloJCd5MEG/w5U6TlvdEuov0iRasUBzEbScJ0QeqfShDdywmn8qVrnQYsQQ3mm4suQofgd4W6+6M7ODrLZrNXVnpmZwbVr15BIJPDWW2+hXq9bbYdr165he3sbs7Oz2NzcRLvdBgA7f/Xw8BCpVAorKyvodDoolUqIosgkBf1MHDOdrmQMGqOcWCbsaZoHwYhE4qyORK1Ww8TEBBqNBpaXl4dKB2ezWfPttFotk0jMwSJAwo2GsH8cx8YURLmosjJoV9PeuUmpk5FzPUpSjNIoQkQ56hpP5J6ulAZCUowop+ZgcS4ukzpeyobUOzIqj/RhiJaqcGRe5Qm+eyKRQFID8UISRF+YHfNaNbZ4fUg6hRhLgQr2SSSHWawsGTU2NmZZrnF8lm7OHWp8fBw3b960kx8++9nPYnNzEwDM73J4eIh2u43T01M8ePAAq6urqFarluuUzWaRSqVwcHBg5xURRFhZWUG73bbaBqz9rcdNDgZnhf9TqZSpUJVKBZlMZqhQJE/VYI0JDROiJCIAwpAlRk8wSZFIHeP1GG3BkyUI1UdRhEwmM1S+jL6zwWAwFBpEZNJrDMpIXkqFJJI2z1gKHoQknSd8r5aR2KlCs9oS599LIWUq3TBCEpW+PDIT6ZrXq2tBQQ79P6kvqhPnJzAkrXiv7tqjpFBIZRyFBFJNGh8fR7/ft1JauVwOrVYLi4uLSKfTaDabRqQvvPACOp0OdnZ2MDExgTt37mB9fR17e3vIZDJmfJ+enlpFU4YfpVIpjI2NGUzNvKJUKmVHRBKta7fbiOPz+hOzs7OGwHExFhYWLDyHYAYZKY7PfEk8YW9ubm4oTrDdbttJeswjSqVSluVLJJH+K2YP87iaVqtlm0gul8Ph4SH6/T76/T7GxsZQLBaxubk55PvhOngUT5vXILy00XXV77Vfr9p5hgvZNB6Fo/NaQRIFQEKM5PtQJqIk11QJ4BzWV9rmGFUymY3kw0JGET6NTe80G8Voj+pTv9drJicnzaguFApmXBMC5o40NzdnMHMmk8HJyQmeeeYZtFotzMzM4M6dO1bzgJESjCQgEubTuYm88YzVfr+POI4taDKZTCKXy1mwLKFS9kkpqbBzp9OxfCoig+Pj46Zq6YnjBFm63a4BKQzfpzRjXtbMzIytB1GzdrttNt3q6qoRGMGObDZrkRs8b2l8fNzUGQ3z8kyk0kOZJYTS+RaSNp5m/DVK5ApIqarnIzxI6F7CeZDESyZKZPVxcRw+XIrr62Mok9phiGFI4LSllEtVV+TfPqtTGUYHxr5CC0UYOZfL4eHDh4iiCIVCAbOzs+h2uzg+PkY2m8Xx8THa7bbZJ6urq+ZzmJmZQblcxtramvVVrVaRSCSwtLSExcVFzM/PAwCazabB7Uyr4PjpGGY6BlE3HshMIuRJGDwahv4sSplr167ZXLBsV6/XM2OfxNNoNDA3N2fzzcBbAgv9fh+ZTMZUSNo7dCpTSrJccr/ft/NiZ2ZmLCCYTHNwcGBIlNovXs1TYlUG8bbOKJuJRMofJUxlAC+R1ImraJmOy4Mho+ysUe+gDMx39/0qzSqSyr+HUDvlbq+yeTXMIxejJJkyjE6CNu0LgBXqoD3Akrs8B6her5vdNDExgQcPHuDKlSsAYDF2VMsY5jE1NYW5uTkz5FnGq1QqGWI2MzNjEQGUTMB5cRKqgJ1Ox+B5ghj0CU1NTaFardopfvRlEb7nD8ehNelOTk7MkUv7jxC8BsPu7e2h0+mYvXV6emqbByUwD36uVqtIpVKIosjO2aUqSumvu7G3X3wbZUOpqqffc/1JqF4iXWbTeNXOa0JKs6PMBX7GphuGfw/9XDEAfUcN2OY1AIYZiRdwYCpO+b+XJCEVz/8dYjhdBIVcVe9kMChPxKPPZnFxEXt7e1hZWQFwlq909epVJJNJLC8vo91u4+joyM5wJRMwri6Tydj7zM7O2qkXnGhKKkYzeKYBYJEMLD7J5L3l5WWsrq5aqBHBi8PDQ1OnCDRotVbOD5/VarWQSqXMVqtWq5ienkaj0bAoCDIhUU3C8VRL1YWgsXyDwcBClhjBoaWrRwEISnijmMx/7xmJaqgS46MYSZkvBEQ8jirH71WahJhVhciotH7lhQuMBIQryCiA4KWU1x9HMZC/JvS5Or6IZk1NTRkzzM7OWkFHIisKCpBooiiyXZpESyKanZ3F6uoqlpeXzQE3OTlppXtp7xDaHh8fRzqdNnuGC1KpVBBFEX7lV37FJBDVrDiO7ZnZbNYAEkLlJH6qZIxkpzOW9iHVGJ6WTv8VkcapqSkLL8pkMpaCUqlUbL6oz9PG5IbF+dBTNLwW4ulAPxv1/SgmG6VyKS2oOncZgetvvo8HMry9pFE5ysijnqPjUmYKSUH2kVR9VSWSSh/9W6WLZ8JHMVIIkODn7EvLHR0fH6NQKFhYDw/SYuPBx3S+MpWayNvs7KwBCtPT0ygWi1afm4GpfG6/3wdwFpYzMTFhR27u7+/jySefxPz8vJ0AeHJygt3dXXQ6HdRqNSSTSZMeBDb6/T729vawsLBgEddkVOAsgHR3d9dO1aOfgotN9azZbNo5sWSOdDpt2cRM70gmk3j48OHQubBTU1PodDrIZrMAYJC5Eq/aLSod+KMbaYiZ/GchZgoxB79XgCNkz6g6SAmg/ak5EvIpeVr1Kp1KSt6rNr1KTaVbT+MjGUlvVDGsnantFBKBvvlMz5AtpQ43euevXLli56m2223T9zOZjDkbB4OB2Q902pZKJczPz2N1ddUiyRn+ztCcKIos/GRychILCwvGjJVKBdvb2yiVSub7SSTOsnCpkqVSKQwGg6HICx6dQr8H32tqasrqdhNQ4MFpas9REjKFg2oXF5kO1yiKLDV/b28PlUoFJycnBsxQEi0uLmIwGAzVCqQdyvXX9QsxwSippNdpC9keyhQqzfijkmOUXUXwYTAYmGQfxUherWPf+j7ex8R7va9U0WofxGBggw7GcyPRIP4dEv0eLFAkxqt/ykTK6cq0hHupBq2trdmzWaq32+0abE27h2kRdKiy9BYhYkqdRqNhDMQIapasSiQSyGazZsATsmYUdyaTAYAhBzFtEKpujGao1+sGwXOxUqkUrly5YgACawIwKHMwGNipgslk0piWgMHs7KzZWP1+3xC4e/fuWRoG49AIlmilVtpJlHKMPGewpq5XCBjyLcRgod9e+oVoJSSRvMpF6eFz2UIARchO8psCGZL9KT8oTfr7FIRIJFw5LuU2lUgKPSoD6H36QlquSh+ooII25Xh9AWZw7uzsoFAoIIoii5Hj6ef06QCwsB8AQ74eBpXW63XEcWyF9en3mZ2dxY0bNyw6gCriysqK2TEkqunpaZubZDJpVV0nJiZQLpftFLx8Po+VlRUcHh6i0+mY2kbG5Lz3ej2bM25WlGKsxbe/v4/19XXbOKLo/DRDPUGQUkijADgvtKeWlpZQLBbN3iJDMvfmMuZRYvK2jRKZR81CEoPXKQF7FUwllEotgiOhksshe4zfhZhC0Upu4mr/a2hQ6H1NtdMX9F/qBOouEppgnRxvUKoYD+mZei/743d0SDL/hyhaoVCw0yF4lArVFp7Qx92dIMTq6qqpccAZs+3u7logKH0+LM+VzWaxtrY2dGwkfUCpVAr37t0zsIC7PO2ye/fu4fj4GPl83hIME4mEwd70ztPwJzAyGAzM3iIgQjCC4AilHBE5xgXSgcvviOzF8VkKCp25SmiEyzX2TtcmxEShtQ/Zvo9iJKUXbzspI4VQOy3kqUzibSiVil7FY39eUvrNROfCo5pkuCS5UDv3HVDiaBSETh7FG+/RaFxdGJ3skH0UAij4UjzAa25uDnt7e1aTjRmjJNY4js1/o1G8dDwyoU0JigRKdI7SgOhXJpNBv9+38KLT01NTq4jmUTUiU7AQY6lUGjosLZvNmu+G895sNi0jltHeeiIHD3KmFKNTOJlMWgwh/VMsekkfnK4lD3/mLs5IeqqtnsB/Hkbi3/4eABcYgn154CokNZQBtQ9+7qWZ3qdj8+/g+/P2m6dVLxD0/aIoOgMb/EuxUV0LET0H4xmMi6QP1/7Yj068RpFrgCDVvYODA8vNIbHyNHPWr47j2MobMyGQ+UGDwZnf5P79+1hdXTUbanJyEpVKxaQUnbiNRsPOeGUEBXOMWJCewAQlJnNYJiYm0O/3zWHKYih873a7baeWawUhwt6UaFF0BuNPTk6a5OX8cKMgMxEpnJiYQCaTwcHBAXZ3d5HL5YaixDVmkPZmu922M5yU6EKujZA9dJk6GJIG1BS8XazM4gEE/Y59EIAJqXJeRVQG0mtobpAHvP0WQpdJr6R9PstUu1AMHaUV1QMv4oBz1EMdXfztA/+845VN7TSvP1Pa9ft9rKysmCrE+t9Uyajnk+G443InHwwGmJ+ft1g3xrtlMhmbDJ5aThvs9u3bSKVSQ6WLOU4a9lSX6AeiOspgUxr0jFZWWyabzVq2qp4iSClJ5iHczXViTB6TIKkGasxgKpWyvvVMJoZC9Xo9xHGMqakpC/4N2Tlsur7KKCEp5QlW7RtN1/CpELqBcs20fBlwng5OtTeUkBgCGjgmLz3JPIoG6hx41TUExkRRdM5IXuTR4FJG4E38X30ACjx4bg2pcyrp1Ljz16guT2KjF58VWUmw/X4fzWYTy8vLVquBTtvDw0Osrq5id3cXU1NT6Pf7ljXbarXMplhfX0epVEKhUDD0jLs6s06bzaYl3zUaDRweHmJ5eRmzs7OWFEj1jWgc1S7C73Nzc6YuxnFsxRrn5uYsp4nvzfR2AMYszM3RUwWnp6eNodgYpUGAo9vtYmtrC41Gw/xV6XQa5XJ55DqTgJUAPdHqfSGiVWkU+l7tE7VD1L/DcfBHTYwQuBD6W5mLG672yc1epaYCD55HeE2SBq5H1HRwuhvxRlXxdCJVKnmV0E+2Gov+Mw1niaLzsJfZ2VkTrycnJ2g0Gsjn80MlqwgMEIqmfUSiYzUdGvAHBwfI5XKIorPUbBZOef7551GpVDAxMYF8Pm92DO2wVqtlNRmocmUyGfMR7e7uWhLh5OQkcrmcoWdaqZUFUajSTU5OmqQjA1ArILPwhHdFrii1GGhLPxl9RrVazUKNGFdIP9X09LQdkUPiIUGHaEMr6Sht6ObqVS1V1ZRAlQnU1FCJwWsIiqgm5Z3JXsIp83vQzKuGysikM1X7NP1E+01q8Tuvt+rFOln6m0Sk13tY3Nta/n7/P3diVadOT09RqVQspTqRSCCTyZjxr4VCFMWi95/nLnEiSLhjY2PI5XJW6wGARVwXi0WTSjz0mSola9qx7DLzkDiHPAGC9zEagnA7mUslFdVopnYMBoOhkslEH/kdd1CidNy9aVtpiBBtCgIz/qSFYrFouTi6ZqphkOh1zVVaKP14yaKqGmnF96kEzcaxsh9uoN5+Cj3Lb/SkY/6vCKDaSZ6GNZDAMybrSBgjqZqlDwdwIRbL20peoniu58Mu08H1O1ULgfNkLhbByGazhpoxTo4xaHEcm2+FRJ7NZi0siOnidKwStmZynAasAsDKygo++OADc96y1jgZYX5+3nZGggpUB8kAJMzDw0MLSGXoEABT4+hABWCRE0wvZ0EUnWsijAxipY3obVXg7KTDbreLg4MDbG9vD6lBp6enlqektodeo7YBVa6Q9NHPlPBI+EpvIQ1I0TTSEbUm0hbVZn52cnIyVMNhFPCgdKVqH9U6nrnF9+B8cyPx9hOfbQ5ZT8R8gIf8vFTRDj1K520d30KSz9+nk8xSvru7u0aE3MFVDWSEOKuYkuCJ4O3t7aHZbCKXyyGO46ECK5VKxVITxsfHrRB/sVi09IlMJmNS5ujoCN1uF5VKBalUylS5qakpYwoyFYkjjs98OvR9UQXlYtFuAmBSlCqaz8yMouGa5ZRYtB95UuHOzo45o0ul0tCBWWq/nJ6eWt6VR+382nOnVmIdxUh8d5VI3kTwahiJOoqiIabhhqGaVEj6eOmkAIa3pchI7Fc3fc5FKOaQ6jaAs5oN+mL6IE4cH0RG87uJZxDPDJyQ0GKoGFWm0zFxQhqNBsbHx62WguqvKvK5O9VqNXQ6HfPJNBoNTE9Po1arIZ/PWwo7Q5FyuRxqtZo5TxkhwPCccrkM4KyACu+bnp7G1atXjRB1knu9nkkJEgODYjmfjNTgZgHA/F7sUwN1lVB7vd5QKWKqm8CZtOr1etjc3ES9Xrc66JoKompYtVq9IFn0Gl07v+npeuqmq9d6RvJxmiqJ+JsSTDdJqsCsMcFrPQPzvZSZuAGplOIz1LenGwk3MI5HT8ZQNHHIj+TVOg5Ey9KG1DP9jDaI6tZeHeT3fC4JhYug/iJViwBgZ2fHqpay3h0nmAQax7HFmyWTScsKzefzVn2oUqlgfn4eiUTCIq3jODYYnc/jzsu54H20QSh5WG9PNyIm4xEsYGo0YWi1hxhQSqLheAg2EH3kWnB+iB7SJ5TNZi0zd3Nz02pOMPJcGZNroyoXGwmT86+AgzKSMoWuu2cqb4OEmJD3KTMpI5ERleCVkZSudJw6XrXR+Cz94dyQjr1KO2r8Se10lHjUGshehfOMxGt0B1D7S/VMfhZChRRoYB03ppm/9dZbBhLQ5gHO09xp6JNBuHsxwpv5SCyFDMDyio6Pj1GtVtFsNpFKpdBqtbC8vGzQ99bWFtLptCF7NOTp32LwK2P8vJOPNgxVQJ1Lzg1hcBY44bsMBgNLxOPcTE5Ool6vI5FIIJ/Pm4r50Ucf4eHDhzg5OUEqlbJ34EFjXoJSqvO3MpGOjWurqJ4SsTKP0oVmAXuJRDtDQQTShWckql9aCVXHPGoM/IzvrKof+yODciwhKUfNxyOOF4518Z5dvnzIplE0TkW0qgxkFv1fdVb1YXnJyImkk5QpDnt7e7h79y5yuZwVD/EEwN2FcDnrg+fzeYsvY1UfACbR4ji2OhFM32Z54Pv376PdbuPDDz9EOp22/CASA5ExOmT1ZHLOlUZi0+7jeL3kZ5jS1NTUkA2TTqcBwLJ2mfE7GJyV4yqXy0awm5ubSCTOkiUZakRnbyKRsDOTSOynp+dnuHrno1f3PM2o1qEqt0YPkJ60b86fQuRE6pSZGOlPpzrpJYT2ecBFfaJ8htK3biy6BhQGXEcfTkd6SxKS9RPmdx2dPI/w6Q6gRhkJhDYLJ5Xqkg5am6/+mUgkzGaYnp62Sj6dTseS55SJNfqcPhsAli7RbDbxmc98xhghjmPrd3NzE4uLi8hms5ienjZGzOfzmJqaMrtLYeVms2kR1tls1phMC5Kozk8m4/vzfRlupJJjbGzMsnQJ86u/jOcwcX62trbw/vvvo1wuWzoHF7zZbA5pAH79OH9+bfiZOiY9YucBp5AaSGJXdwmvUZXY2zZecqiEUu0npCXp2Px3HJf/zvcVAs6U5geDAaI/+7M/izlpPt9oVCOjaWqF/vaD1wVQ/Vl3PZ18euZDOwydl9xJV1dX8bWvfc1SHpgXxGeRmGhHcJfXJD4GnpJhO52O2TK0QXSi2+02JiYmMDc3Z/2xnhyLTQLnoAFVMiJ06g6gLaZ+qdPTU0MagXMInFEORN3oHN7d3cX+/j5ef/11fPDBB2YPcf4Y2QEMR1LzHfleGnSrKintOK4Pxx/SQrxEUs1DJY7fTFVaqQRTNS6KIgvsZc0JVelCDMzPNN3CMywLbCpSqDTL/72tr/QdffnLXzZq9Rms2rxHVxmEnYYcr9qv12PpZORL8X6d4EQiMZQ3PxgMbDIpdp999ll84QtfsEzQKDp3WqpKojsiValOp2MQM5syO1UMnmlKCeUXTQ3lyclJc+SSABlcS2ImwzAglmOmxGIOExeOTlZ9LjeKer2O999/Hz/84Q/NGa3zR4exrkVonXi9zhfnX0ORlLB0jDofqqHQJiMTkUmoyiphcwPQ6AW1XRYWFqyGuzrYQyCAbuiUxKppUaVjTXUeuuxBFK+ek47VXkxWKpUhgvfiy0sVTrBez+Z9D/5+HQAAK/LByVYUS8/9YfiNqgc8qe/69eu4evWq7faKAPKZwLlTmf1QtUqn05ZvRKZmMCjtJjbu9Or30R2LxEHHMSWI6t50/BKgYKVWlb7Hx8dIpVIWKT4zM2PBt8pAjUYDtVoN77//Pu7cuWO5VbouURQNnTw3iuA5R17dI4PrGug66RyoOq6bI207tUVUK1H3in7vbSbWxKBqykMOOF7PQErXRILVDidtcF1ZkVbpV2nXmzz6rGh9fd1WUHcjj8joNbrTq8jTa0KfsS8uNBfkiSeewPPPP2+Ox7W1NayurhqQ4FVANdD5kqlUyhxkzFjVHB4SIEEIxsAxuoBq3/b2tjFBJpMxHxBPjmABEXUNKFES8WFahKoxJA5KWFWRlAgAWF06Vnml2qNIIXdvSrd2u21QPzeWEPPob/+Z9r+7u4soisxG47rpDq1jVxWO/wOw0wlLpRLu379vEr5cLmNnZ8eQOIWgVSrxh6XTFhcXkcvlLNDXAw36bI5TS5eRHrlp9no9NJtN1Go1CzkLMaJuTl6aR2trazYCr/95zmZn3N28aud1SX8/iZAvNjExgRdffBEvvfQSVldXL0hDPdqSC63j03wefp9IJMzWYBoBCZn9U53iTs3o7sFggFqtZmciJRJn9ca1WikduWTuUeqE7vSKGHm1i3NFYvBzqju7Eof2ReJT1YVzzet1fKpeewJR9Us3Kr1OJZ6icaHGcTOmj5IMAHq9Hl577TX80z/9E8rlsm0OCpUrGEXfYbFYRKFQsCRFjkPnlX8DMDpSbYX/M3es2WyiXC5boiTf06+v19D4fzJkPHnDTaVJiDC82sekM3X86b1c6GvXruHb3/42bty4cYHDQ81foyCA7iK0d0K7FKUIDVfq391u1+wg+o9opB8cHFhR/GazaWW98vm81YPwz/MbkurUNOiVqNVG5H1URdQ5GFpU/ywS/2U276imNg/nie8WsoVCmkeoTzqc9b7Z2Vl85StfQTqdxl/8xV+gWq0aAxOg4cadSCSsYAzpj4izMnLI9uO4PMPTPmKxz0qlYo5wP8f6t2dUAGenUXix/7gT7h/Epv0oE5IQuCs9++yzWFlZeSwmelTTBYzjeCjIk9EPKkE4qazUw9g3Xk8GYUTBzMyM+S/42f7+vt3nbQU2EgT/1kKTKm1JMKoW6QnmIYiX76oSKUTw+reqQaPmXWFnr6ZfUGkCas6jmvaVSCTw67/+62i32/jrv/5rixBR1ZhjOj4+tlJkjKH0RH0Z4KCgkEpf2paKDoZUYP/+ypjJx+W40GTooImEKTGFdGoidTMzM3jqqaeG0MBP0lTV4Kl7HMPc3JyVn9L3VQCC9hR9P7TXWGOh2+2i2+0OBciyD6afFwqFIJqlUQkaF6Zefv6mbXJ0dIT9/X3zF1H1VPUOgDmVear76empZceS0PRHmTfU1EaiI5yIIR3U+p4ftymBj4+P40tf+hI2NzfxyiuvWAyb0h+Jlu4ERnwoAMB7PP3q/zqHSge6Een4dK75uW5GBpr4yfC6X2jSRz1AbRl+r4vHH8LIy8vLwf51x/AvxevVz0Fdt1Qq4fXXX8cPfvAD1Go1AxVSqRS++MUv4vd+7/ewv7+Pq1evmn+Ixirh8EQiYTshDdRkMon5+XlkMhmzy6j2sJQws1g9cqXxgjTYB4MBPvjgA2xsbOCzn/0sDg4OUKvVUKlUDJl655138MYbb9hpgwpw8BnZbNaCUPU7zpPaMrquo1Qfb8NRYhNFHR8fx5UrV/BHf/RHeP755+0Qa67VYDCw5MSQZAjREP+emZnB17/+dVQqFbzxxhsX5l8r8OqJ41x/BXM8HWnTAGC1r/wGpe8Q0gL8ewyJgxDaFmqhSVFIXB+gOwSlEcN9LmMkFkhU/8VgcBa8WavV8NZbb+HOnTt2baVSQblcRrPZtHONuIN1u138wz/8A3Z2dvDcc8+h1+tZqBDzmdQnoe+hNh2AC+PJ5XK26PQB+Q2FC0Wd/NatW/jnf/5nbGxsYGVlBclkEm+88QZ6vZ7VpXjuuefMRxRC2NgfpZoy0ShbN7R2XhVUhqK6STh/amoKGxsb+Ku/+issLy9jfX3dgBc6fH/zN38TN27cGHJkE+Dp9XrY3d3F6urqkH3L5xaLRbz00ktotVp4//33AZynKlCFDTV1toZoSZlBQZtR6tuoPvR//nCtP5ZepTqnTj5fSh/u1QoawSsrKxYz5tvx8TF+8IMfYHt7G9euXbMSWuVyGZubm9ja2jK8nxO8t7eHdrs95NVXAl5ZWUGj0cB//Md/4Mknn8Tbb7+NXC6Hd999F5lMBjdu3EA2m7XDkfl+1MUJRPidiu9FKUT9nvf2+31LZecZssfHZ6dW3Lp1C6+++upQFi+PXzw6OsK1a9fQ7/fx4MGDIZeDzvllhn/IFvKf6T26CepnfFfaeu12G8fHx6jX60PIKAC8++67yOfzWF1dxeLiop2DW6vVUCqVEMcx/vRP/xSf//znL2g/4+PjuHbtGv7kT/4Ef/7nf26n0qv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+       "<Figure size 1500x900 with 8 Axes>"
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+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 创建画布\n",
+    "plt.figure(figsize=(15, 9))\n",
+    "\n",
+    "# 原图\n",
+    "# 创建坐标系\n",
+    "plt.subplot(2, 4, 1)\n",
+    "plt.imshow(guido_image)\n",
+    "# 垂直翻转\n",
+    "plt.subplot(2, 4, 2)\n",
+    "plt.imshow(guido_image[::-1])\n",
+    "# 水平翻转\n",
+    "plt.subplot(2, 4, 3)\n",
+    "plt.imshow(guido_image[:, ::-1])\n",
+    "# 抠图\n",
+    "plt.subplot(2, 4, 4)\n",
+    "plt.imshow(guido_image[30:350, 80:310])\n",
+    "# 降采样\n",
+    "plt.subplot(2, 4, 5)\n",
+    "plt.imshow(guido_image[::10, ::10])\n",
+    "# 反色\n",
+    "plt.subplot(2, 4, 6)\n",
+    "plt.imshow(guido_image[:, :, ::-1])\n",
+    "# 灰度图\n",
+    "plt.subplot(2, 4, 7)\n",
+    "plt.imshow(guido_image[:, :, 0], cmap=plt.cm.gray)\n",
+    "# 二值化\n",
+    "plt.subplot(2, 4, 8)\n",
+    "plt.imshow(np.mean(guido_image, axis=2) >= 128, cmap='gray')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "abe85cca-abf4-4805-9e69-b2971021e741",
+   "metadata": {},
+   "outputs": [
+    {
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+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x161275890>"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
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+       "    <path d=\"M 29.2 276.552561 \n",
+       "L 206.608 276.552561 \n",
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+       "   </g>\n",
+       "   <g id=\"patch_6\">\n",
+       "    <path d=\"M 29.2 10.440561 \n",
+       "L 206.608 10.440561 \n",
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+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"pa4d833b64e\">\n",
+       "   <rect x=\"29.2\" y=\"10.440561\" width=\"177.408\" height=\"266.112\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 局部马赛克效果\n",
+    "guido_image_copy = guido_image.copy()\n",
+    "\n",
+    "n = 12\n",
+    "\n",
+    "for i in range(120, 350, n):\n",
+    "    for j in range(120, 310, n):\n",
+    "        color = guido_image_copy[i, j]\n",
+    "        guido_image_copy[i: i + n, j: j + n] = color\n",
+    "\n",
+    "plt.imshow(guido_image_copy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "id": "0e1f177e-db9d-4585-8c15-2cb5a928ccd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install pillow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "0143bdbe-afde-4741-8d11-2bcbe34de477",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from PIL import Image\n",
+    "\n",
+    "# 灰度图\n",
+    "# Image.fromarray(guido_image[:, :, 0]).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "487ec2f5-97bc-413f-9a06-c8f6875ebab8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# from PIL import ImageFilter\n",
+    "\n",
+    "# 滤镜效果\n",
+    "# Image.fromarray(guido_image).filter(ImageFilter.CONTOUR).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "15aa7ec2-8bff-45fb-89b8-19790838aff3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(750, 500, 3)"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obama_image = plt.imread('res/obama.jpg')\n",
+    "obama_image.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "id": "e1df160a-3e97-4594-a0ba-43bbd3c384ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x11ebe8810>"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       "  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<svg xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"213.808pt\" height=\"298.877561pt\" viewBox=\"0 0 213.808 298.877561\" xmlns=\"http://www.w3.org/2000/svg\" version=\"1.1\">\n",
+       " <metadata>\n",
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+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2024-09-19T22:45:18.229325</dc:date>\n",
+       "    <dc:format>image/svg+xml</dc:format>\n",
+       "    <dc:creator>\n",
+       "     <cc:Agent>\n",
+       "      <dc:title>Matplotlib v3.9.2, https://matplotlib.org/</dc:title>\n",
+       "     </cc:Agent>\n",
+       "    </dc:creator>\n",
+       "   </cc:Work>\n",
+       "  </rdf:RDF>\n",
+       " </metadata>\n",
+       " <defs>\n",
+       "  <style type=\"text/css\">*{stroke-linejoin: round; stroke-linecap: butt}</style>\n",
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+    "plt.imshow(obama_image)"
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+   "execution_count": 115,
+   "id": "6c57d787-8a35-4a56-8b10-ceaa08b49612",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(750, 500, 3)"
+      ]
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+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
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+    "temp6 = (guido_image * 0.6 + obama_image * 0.4).astype('u1')\n",
+    "temp6.shape"
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+   "execution_count": 116,
+   "id": "54ece4ed-4346-458a-8b57-58a9930c6dce",
+   "metadata": {},
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+       "<matplotlib.image.AxesImage at 0x11ec55fd0>"
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+     "execution_count": 116,
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+       "   <g id=\"patch_6\">\n",
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+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"p5ff3331b88\">\n",
+       "   <rect x=\"24.2\" y=\"7.2\" width=\"266.112\" height=\"266.112\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp7 = np.random.randint(0, 256, (16, 16, 3))\n",
+    "plt.imshow(temp7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6649825a-9c1e-4190-adc4-a98c8c97a553",
+   "metadata": {},
+   "source": [
+    "### 数组对象的方法\n",
+    "\n",
+    "1. 获取描述性统计信息\n",
+    "   - `sum`\n",
+    "   - `cumsum` / `cumprod`\n",
+    "   - `mean`\n",
+    "   - `np.median`\n",
+    "   - `stats.mode`\n",
+    "   - `max`\n",
+    "   - `min`\n",
+    "   - `ptp`\n",
+    "   - `np.quantile` / `stats.iqr`\n",
+    "   - `var`\n",
+    "   - `std`\n",
+    "   - `stats.variation`\n",
+    "   - `stats.skew`\n",
+    "   - `stats.kurtosis`\n",
+    "2. 其他相关方法\n",
+    "   - `round`\n",
+    "   - `argmax` / `argmin`\n",
+    "   - `nonzero`\n",
+    "   - `copy` / `view`\n",
+    "   - `astype`\n",
+    "   - `clip`\n",
+    "   - `reshape` / `resize`\n",
+    "   - `dump` / `np.load`\n",
+    "   - `tofile`\n",
+    "   - `fill`\n",
+    "   - `flatten` / `ravel`\n",
+    "   - `sort` / `argsort`\n",
+    "   - `swapaxes` / `transpose`\n",
+    "   - `tolist`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "22e444ec-9b9e-4807-986a-1b3b229d87af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U scipy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "id": "4d7745c2-30fa-4a15-bae5-39b9597c1462",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "id": "d8b8fbbc-43d5-467b-a32a-116639baedac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([76, 81, 85, 79, 83, 82, 91, 80, 87, 86, 70, 82, 84, 77, 83, 85, 76,\n",
+       "       74, 80, 80, 82, 76, 68, 77, 80, 78, 77, 73, 81, 76, 85, 81, 84, 85,\n",
+       "       74, 84, 70, 76, 78, 80, 86, 75, 94, 79, 84, 78, 72, 86, 74, 68])"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores1 = np.fromstring(\n",
+    "    '76, 81, 85, 79, 83, 82, 91, 80, 87, 86, '\n",
+    "    '70, 82, 84, 77, 83, 85, 76, 74, 80, 80, '\n",
+    "    '82, 76, 68, 77, 80, 78, 77, 73, 81, 76, '\n",
+    "    '85, 81, 84, 85, 74, 84, 70, 76, 78, 80, '\n",
+    "    '86, 75, 94, 79, 84, 78, 72, 86, 74, 68', \n",
+    "    sep=',',\n",
+    "    dtype='i8'\n",
+    ")\n",
+    "scores1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "id": "7bb13bb7-ba31-459c-85ce-ef0dc85abd96",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(3982)"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 求和\n",
+    "scores1.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "id": "f7d4eb31-33f0-436e-a53b-98276d22ddef",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(3982)"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "b450c23a-26b8-4766-b15a-5470efb8e37a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  76,  157,  242,  321,  404,  486,  577,  657,  744,  830,  900,\n",
+       "        982, 1066, 1143, 1226, 1311, 1387, 1461, 1541, 1621, 1703, 1779,\n",
+       "       1847, 1924, 2004, 2082, 2159, 2232, 2313, 2389, 2474, 2555, 2639,\n",
+       "       2724, 2798, 2882, 2952, 3028, 3106, 3186, 3272, 3347, 3441, 3520,\n",
+       "       3604, 3682, 3754, 3840, 3914, 3982])"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 累积和 - cumulative sum\n",
+    "scores1.cumsum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "882ff41a-4d0f-4a15-adf3-272acc398bda",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  76,  157,  242,  321,  404,  486,  577,  657,  744,  830,  900,\n",
+       "        982, 1066, 1143, 1226, 1311, 1387, 1461, 1541, 1621, 1703, 1779,\n",
+       "       1847, 1924, 2004, 2082, 2159, 2232, 2313, 2389, 2474, 2555, 2639,\n",
+       "       2724, 2798, 2882, 2952, 3028, 3106, 3186, 3272, 3347, 3441, 3520,\n",
+       "       3604, 3682, 3754, 3840, 3914, 3982])"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.cumsum(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "5e43fa2e-c30e-40c3-8159-89819e5e368f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(79.64)"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 算术平均\n",
+    "scores1.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "2f59ffef-619d-4a03-a496-3972d13ee33e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(79.64)"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "3bfdf16c-894e-47e8-925c-604418cc0eb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(79.44812732667022)"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 几何平均\n",
+    "stats.gmean(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "id": "d73ce1b5-3956-4c12-9237-396ffdec44fd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(79.25499854665681)"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 调和平均\n",
+    "stats.hmean(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "id": "6d165492-6d0c-4f74-a2f1-75c95ca13b2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(79.58695652173913)"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 去尾平均\n",
+    "stats.tmean(scores1, [70, 90])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "id": "99fbc4f2-220c-4ea0-9d24-8640c66c0115",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(79.58695652173913)"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(scores1[(scores1 >= 70) & (scores1 <= 90)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "8b536052-4a67-43e5-9e0a-6005bd73a66c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(80.0)"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 中位数\n",
+    "np.median(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "id": "390f8e61-a108-497d-b3cf-54bd25ae3a0e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.int64(76), np.int64(5))"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 众数\n",
+    "result = stats.mode(scores1)\n",
+    "result.mode, result.count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "bc729130-fdfa-47be-8a5c-ce7a474ab6ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(94)"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 最大值\n",
+    "scores1.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "id": "f02f74e8-e27f-4169-8fda-2ebe4504f0d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(94)"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.amax(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "0a32b324-df80-43c8-b8b0-12b36218ef2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(68)"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 最小值\n",
+    "scores1.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "95680bd0-075a-4832-b7b9-76cea6653e2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(68)"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.amin(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00d3a61a-d22b-4ee3-9327-8197abee91fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 全距（极差）\n",
+    "np.ptp(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6fd57ed-7bb3-4593-90ce-2a79a9ea8d2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 四分位距离\n",
+    "q1, q3 = np.quantile(scores1, [0.25, 0.75])\n",
+    "q3 - q1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3668ed4b-4205-4f6a-9a9d-6135a238515a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inter-quartile range\n",
+    "stats.iqr(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a3c5ed8-0331-4c94-8cac-6b947c7765fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 总体方差\n",
+    "scores1.var()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2cdb1d1-179a-425f-930f-2ef7dda46a79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.var(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "493d242d-fb94-4ed9-b316-7f722f908d99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 样本方差\n",
+    "scores1.var(ddof=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b70dac6d-24f1-473e-bb27-200384202db0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.var(scores1, ddof=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0670037e-0ae2-461f-b01a-0f47ed036813",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 总体标准差\n",
+    "np.std(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22e3361d-3bbf-4175-94e1-719ca022e5dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 样本标准差\n",
+    "np.std(scores1, ddof=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e9b8db0c-6e78-4efb-a494-62977ce1e4e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 变异系数\n",
+    "stats.variation(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "029b2ccc-0dbb-460c-b84e-ea2b34cca3fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 偏态系数\n",
+    "stats.skew(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c35b17a1-5abc-4444-acbf-d757de430dcd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 峰度系数\n",
+    "stats.kurtosis(scores1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fba00d6-866e-4ab1-9caf-18b17fe08440",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 箱线图\n",
+    "plt.boxplot(scores1, showmeans=True, whis=1.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cd46156-6a31-47c1-b3c6-4db92d91ac8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 直方图\n",
+    "plt.hist(scores1, bins=6)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c5340e8-ed88-432f-8656-f987c0972b80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 设置随机数的种子\n",
+    "np.random.seed(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2102845-986b-4e22-9a73-0964af537386",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores2 = np.random.randint(60, 101, (10, 3))\n",
+    "scores2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e9f4921-6001-45e6-bfe0-aca2fe2aa03e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores2.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e116ac94-7cc2-48bc-b563-2e3ccd262378",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores2.mean(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b7fc404-06cf-4c3e-9ef2-b67bd1d3e517",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores2.mean(axis=1).round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3cf52cc-48ca-49d5-bbe7-6d977e8e8343",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# axis=0 - 默认值 - 沿着0轴计算\n",
+    "stats.describe(scores2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8b60634-56cc-489e-b898-792925aa79a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# axis=None - 不沿着任何一个轴计算\n",
+    "stats.describe(scores2, axis=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0ba4905-ae46-40d6-a6a3-b97f830fbee5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# axis=1 - 沿着1轴计算\n",
+    "result = stats.describe(scores2, axis=1)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9adade58-2bf1-4c46-be0d-0ef1682382f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.mean.round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "24bca7b9-af35-4943-94f9-90ce5851d136",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result.variance.round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e90cd926-9f9d-4701-8f70-dc0f13ad24d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.boxplot(scores2, showmeans=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b58cd75-8df1-4593-a4cd-19c32f821f63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d792920-4ec1-4817-9e5a-b1d975dcb9a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp8 = np.random.random(10)\n",
+    "temp8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0385af53-d682-42a7-84da-a16b4353a6ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 四舍五入\n",
+    "temp9 = temp8.round(1)\n",
+    "temp9"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7864d43f-b4ce-41d6-b8e3-cd646a12a0c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 最大值的索引\n",
+    "temp8.argmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "336bc616-896f-4186-ad4d-5f6baa048564",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 最小值的索引\n",
+    "temp8.argmin()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29cab4c1-1e56-4b19-a3b2-aeb30be03a88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 调整数组的形状\n",
+    "temp10 = temp8.reshape((5, 2))\n",
+    "# temp10 = temp8.reshape((5, 2)).copy()\n",
+    "temp10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b8e2fe8-b917-4d40-ad48-d3935a1ab262",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp10.base"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e266d969-8bd5-4572-9544-2569fd718156",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp10.flags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b7e5389-cdc3-4995-99f4-dc1386a5e291",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp10.base is temp8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2f6f2561-5392-4887-b0d2-3f639fb8be43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp10[2, 1] = 0.999999\n",
+    "temp10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b739b914-57fd-4c3f-a106-a359e79391f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb79055b-d7d6-4602-b960-890d33928e04",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp8[3] = 0.0001\n",
+    "temp8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f2932d9-45d3-427c-acef-a8ecc468747d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "089a7d87-ccbc-47c8-a934-15a2eb298242",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 调整数组大小\n",
+    "temp8.resize((3, 5), refcheck=False)\n",
+    "temp8.round(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6af25425-046a-4f19-a5a5-235d6fd17753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp11 = np.resize(temp8, (4, 5)).round(1)\n",
+    "temp11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "260a3d51-8041-4515-a611-971267a0be8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 非零元素的索引\n",
+    "temp9.nonzero()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ab48494-d866-49ec-a214-586f1fa8beb2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 类型转换\n",
+    "temp12 = np.random.randint(-100, 101, 10)\n",
+    "temp12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69b3c1e2-cca1-45d5-9d0c-2ab51ab66014",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp12.astype(np.float64)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a7c4ce2-2838-4537-b70a-10f922f2806a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp12.astype('f8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f15abc61-401e-4198-84ab-4aa086cfdf75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp12.astype('i1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce590c02-4eab-4d4c-830c-07e0e887a771",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp13 = temp12.astype('u1')\n",
+    "temp13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c6b04d0-21e1-4be3-a927-095381341f57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp13.flags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "711a115b-7a7d-48c1-b9de-b51673f89ab9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp12.astype('U')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "831f8c9c-f949-442c-ac51-66b3bfc59e1b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 修剪\n",
+    "temp9.clip(min=0.3, max=0.7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d40724a4-a7fa-42c1-9ecc-3207579e7d7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将数组持久化到（文本）文件\n",
+    "temp11.tofile('temp11.txt', sep=',')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4246bae6-ab59-4ee0-ae8d-7d2cc3f60d97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp13 = np.fromfile('temp11.txt', sep=',').reshape(4, 5)\n",
+    "temp13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9d38dba-acb8-4745-be3f-76e7b2605c09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将数组持久化到（二进制）文件\n",
+    "temp11.dump('temp11')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db38d63c-e279-4002-bf02-fed4f615319a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 从二进制文件（pickle序列化）中加载数组\n",
+    "temp14 = np.load('temp11', allow_pickle=True)\n",
+    "temp14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fc26c0e-b518-4b23-a019-9e8e11e363e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp15 = np.random.randint(1, 100, (2, 3, 4))\n",
+    "temp15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1884ac0d-65f9-410d-987f-7235ccd54d0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 扁平化\n",
+    "temp16 = temp15.flatten()\n",
+    "temp16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "369c19c8-ef7c-4740-b8ba-cf47f63f9fd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 扁平化\n",
+    "temp17 = temp15.ravel()\n",
+    "temp17"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d7411df-a939-41f6-bee0-213b555ed666",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp16.base is temp15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "980f80da-d3d6-45c9-86d0-733e0098e8f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp16.flags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4db6df5-e0c4-4f7b-bc0a-50655c036e77",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp17.base is temp15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7339bcf2-a29f-4cf6-ac5f-8a81eb655698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp17.flags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06d8dee5-528e-4345-b5a7-ddc0413cda09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp16[0] = 999\n",
+    "temp16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cfa3191-5441-4de7-a300-6bd8aa11cb30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a4ce0ee-2971-48b3-8834-9113464cf9af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp17[0] = 88\n",
+    "temp17"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7dd7fda5-d25e-4a9a-9202-9a5140fc3439",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe00bb4c-1e19-4988-8020-f2f4162840f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 排序 - 返回排序后的新数组\n",
+    "np.sort(temp16)[::-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fe37ba0-9bce-4bb1-bddc-3100a755439d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 排序 - 就地排序\n",
+    "temp16.sort()\n",
+    "temp16"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afef80f1-5f26-46d3-9589-7eea82b224cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp18 = np.random.randint(1, 100, 10)\n",
+    "temp18"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43f1d0f6-2a74-4512-a6ee-d285de2ee2aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 给出索引的顺序 - 花式索引\n",
+    "temp18[temp18.argsort()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23fc033c-a649-4da8-a0f8-1c7739e3996c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 转置\n",
+    "temp11.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ac95b32-c9ce-404b-a26e-1e8a2351a44b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp11.T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "796f513d-211f-4915-969c-1850d4f569a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 交换轴\n",
+    "temp11.swapaxes(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d9d0207-45d9-4ec0-bcef-9e7072501241",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0fd160f1-710f-4591-853a-d2e960493bc4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp15.swapaxes(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77569990-b8f2-4ff1-b1f6-d17a942f1a32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp15.swapaxes(1, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5970d5d4-f955-4af8-8c54-46a7fff9d0f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将数组处理成列表\n",
+    "list1 = temp16.tolist()\n",
+    "print(list1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c3e62e2-aaef-4250-a561-0f28dd51bf79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list2 = temp11.tolist()\n",
+    "print(list2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37a0a054-34e3-4b2a-a07d-72a9599f012f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list3 = temp15.tolist()\n",
+    "print(list3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Day66-80/code/day03.ipynb b/Day66-80/code/day03.ipynb
new file mode 100644
index 000000000..8f0cb77b4
--- /dev/null
+++ b/Day66-80/code/day03.ipynb
@@ -0,0 +1,3385 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6fc07f67-318b-4d79-8d4e-4eb8a2c61be2",
+   "metadata": {},
+   "source": [
+    "## NumPy进阶"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a9d74703-47d5-44f4-8566-eb7d5476c792",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d139c565-6bf2-4bf6-9d66-d2755b29d1db",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = 'svg'\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d41a57e5-6009-455b-aff9-4f96682423fc",
+   "metadata": {},
+   "source": [
+    "### NumPy中的函数\n",
+    "\n",
+    "#### 通用一元函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5f881886-8aca-40cb-a9f3-4514e28b8fe3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.,   2.,   3.,  inf,  nan, -inf,  nan,   5.])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inf - infinity\n",
+    "# nan - not a number\n",
+    "array1 = np.array([1, 2, 3, np.inf, np.nan, -np.inf, np.nan, 5])\n",
+    "array1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b6e891cc-035c-4e98-9406-1e78e3623e76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('float64')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "674995a2-e50a-45a6-b1ad-7a9f88331dd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False, False, False,  True, False,  True, False])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isnan(array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "358641bc-510c-4f1b-9df7-5dd54de47978",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([  1.,   2.,   3.,  inf, -inf,   5.])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1[~np.isnan(array1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "20030d7d-822e-45c7-b962-3aaf706e133c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True,  True, False, False, False, False,  True])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.isfinite(array1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "357cc22a-7acf-46ee-9523-631134dc8eae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 2., 3., 5.])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array1[np.isfinite(array1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c38f23a4-7d72-4ce4-9bc4-9f8cd9433fa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0.5, 10, 72)\n",
+    "y1 = np.sin(x)\n",
+    "y2 = np.log2(x)\n",
+    "y3 = np.sqrt(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "08a272bc-8765-455b-b0d0-700872071cf4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
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+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
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+       "   <cc:Work>\n",
+       "    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n",
+       "    <dc:date>2024-09-22T23:37:54.080072</dc:date>\n",
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+       "    <dc:creator>\n",
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+       "z\n",
+       "\"/>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"pcb31c92243\">\n",
+       "   <rect x=\"24.2\" y=\"7.2\" width=\"446.4\" height=\"221.76\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# 定制画布\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "# 绘制折线图\n",
+    "plt.plot(x, y1, marker='.',  label='$y=sin(x)$')\n",
+    "plt.plot(x, y2, label='$y=log_{2}x$', linewidth=3, color='#9c9c9c')\n",
+    "plt.plot(x, y3, label='$y=\\sqrt{x}$', linestyle='-.', linewidth=0.5)\n",
+    "# 显示图例\n",
+    "plt.legend(loc='center right')\n",
+    "# 显示图表\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b292f56-dab7-469e-89ed-0fb2114902aa",
+   "metadata": {},
+   "source": [
+    "#### 通用二元函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8b67932a-481e-4e2d-9d83-00994a01d959",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array2 = np.array([0.1 + 0.2, 0.1 + 0.2 + 0.3])\n",
+    "array3 = np.array([0.3, 0.6])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "23581a64-7b02-4f3f-8a5f-ea49ec20e48a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False, False])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "array2 == array3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ddcb612c-c7aa-44c6-b8d3-1ee123fba534",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.False_"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.all(array2 == array3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "40d454ad-8c60-4132-8dde-10726180e552",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 比较两个数组元素是否（几乎）完全相等 - 有误差容忍度\n",
+    "np.allclose(array2, array3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f1aab287-9d50-4b32-94fa-26d7f183fde3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array4 = np.array([1, 2, 3, 4, 5, 6])\n",
+    "array5 = np.array([2, 4, 6, 8, 10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "ea25f2ad-e007-486b-a402-eb3436c9346c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 4, 6])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 交集\n",
+    "np.intersect1d(array4, array5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e4d2116c-c895-4597-95dc-fda67a1c99a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  2,  3,  4,  5,  6,  8, 10])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 并集\n",
+    "np.union1d(array4, array5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "5348c4f2-4222-4904-bd07-a3c85e62e4c7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 3, 5])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 差集\n",
+    "np.setdiff1d(array4, array5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "9cd3f3e5-a986-469f-97ba-c739aa4b8577",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  3,  5,  8, 10])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 对称差\n",
+    "np.setxor1d(array4, array5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "78eb3a98-5992-452e-ab13-eaf578cab7a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True, False,  True, False,  True])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 成员运算\n",
+    "# np.in1d(array4, array5)\n",
+    "np.isin(array4, array5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c7bbc624-dfdc-41d9-bfa2-7a4f3a387bce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 杰卡德相似度\n",
+    "user_a = np.array(['平板电脑', '尿不湿', '手机', '键盘', '手机支架', '奶瓶', '婴儿辅食', '基围虾', '巴沙鱼', '生抽', '沙拉酱'])\n",
+    "user_b = np.array(['平板电脑', '键盘', '充电宝', '补光灯', '生抽', '散热器', '笔记本电脑', '双肩包', '登山杖', '露营帐篷', '睡袋'])\n",
+    "user_c = np.array(['沐浴露', '维C泡腾片', '牛奶', '尿不湿', '平板电脑', '奶瓶', '婴儿辅食', '手机', '磨牙棒', '生抽', '基围虾'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "c4132979-1ef5-4e2b-93a4-2d6bfc66f38b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.15789473684210525"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.intersect1d(user_a, user_b).size / np.union1d(user_a, user_b).size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "46dda506-908b-405a-8e9b-c14090da05b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4666666666666667"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.intersect1d(user_a, user_c).size / np.union1d(user_a, user_c).size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fb5435f8-b8d4-4b37-88fb-13149a62660e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.setdiff1d(user_a, user_c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8366b08-779d-4369-9f27-a9b4b9125782",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.setdiff1d(user_c, user_a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1ce07b5-ec70-4512-814a-e210148ed205",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 余弦相似度\n",
+    "user = np.array([5, 1, 3])\n",
+    "mov1 = np.array([4, 5, 1])\n",
+    "mov2 = np.array([5, 1, 5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ab70c0f-cfe7-4e10-b162-0feefb36f884",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# linear algebra\n",
+    "# np.dot - 点积\n",
+    "# np.linalg.norm - 模长\n",
+    "np.dot(user, mov1) / (np.linalg.norm(user) * np.linalg.norm(mov1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21a1caaf-2cf4-4cf8-836c-d244f4133098",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# np.arcos - 反余弦函数 - 弧度\n",
+    "# np.degrees - 弧度换算角度\n",
+    "np.degrees(np.arccos(np.dot(user, mov1) / (np.linalg.norm(user) * np.linalg.norm(mov1))))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b90ecb1-67d0-48be-84c5-54bf212f1292",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.degrees(np.arccos(np.dot(user, mov2) / (np.linalg.norm(user) * np.linalg.norm(mov2))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c81c6238-f28c-44e8-ac54-94a69f5a6c4a",
+   "metadata": {},
+   "source": [
+    "#### 其他常用函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1df2d086-58cd-4324-b2cf-98d8d971a4d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array6 = np.array([1, 2, 3, 1, 1, 2, 2, 4, 5, 7, 3, 6, 6])\n",
+    "array6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70df0735-6680-4996-8cc3-10ac5d9102ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 去重\n",
+    "array7 = np.unique(array6)\n",
+    "array7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79ad92e0-da87-401d-aa70-0514a5b61d0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array8 = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]])\n",
+    "array9 = np.array([[4, 4, 4], [5, 5, 5], [6, 6, 6]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b099b5ee-d7df-4864-89f3-bb1c7a748a59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 在0轴方向（垂直）堆叠 - vertical\n",
+    "array10 = np.vstack((array8, array9))\n",
+    "array10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "204b8e34-43e3-4a4f-9e8c-8734e72a041f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 在1轴的方向堆叠 - horizontal\n",
+    "np.hstack((array8, array9))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3301cc02-3ecf-4516-b2ff-e0fa6a9041c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 数组的拼接\n",
+    "np.concatenate((array8, array9), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c956190c-1382-4416-9ab2-09a19f5567f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 堆叠出更高维的数组\n",
+    "np.stack((array8, array9), axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a956be87-65e9-4391-b2be-bfbaaab7e7dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.stack((array8, array9), axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df88bd16-64c3-40af-adba-f7faed516159",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将一个数组拆分成多个数组\n",
+    "np.vsplit(array10, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "592ffc62-2a47-48bc-8789-3cad653d2893",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 追加元素\n",
+    "np.append(array6, [10, 11, 12])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a3355ca-1ee7-4d8f-8567-c10fed5055f6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 插入元素\n",
+    "np.insert(array6, 1, [10, 20])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b824b66-7471-4669-aa99-8f62a6b8cb2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array11 = np.random.randint(1, 100, 10)\n",
+    "array11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ff82b6f-9a96-40c2-a4d9-4aaec7d42e9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 抽取元素 - 相当于布尔索引的作用\n",
+    "np.extract(array11 < 50, array11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc00dac0-0b97-435c-960d-06c141de0b78",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 给出一组条件和对应的处理数据的表达式，满足条件就执行对应的表达式，不满足条件取默认值\n",
+    "np.select([array11 < 30, array11 > 50], [array11 * 10, array11 // 10], default=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d7b8700-a431-4da4-99c6-54eda9f065dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 给出一个条件和两个表达式，满足条件执行表达式1，不满足条件执行表达式2\n",
+    "np.where(array11 < 50, array11 * 10, array11 // 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc672588-f36b-4951-902c-6b70ef83d1af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa96b613-cef4-475e-b88c-f6ee8194ef4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 滚动数组元素\n",
+    "np.roll(array11, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfeed7b4-d835-4a1a-9858-d27d4bc363c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.roll(array11, -2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "52b9da9d-dd2b-4fea-984c-6d4b94efedab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.roll(array10, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53aa8382-51e2-4634-bb9f-4ff84ddaef60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.roll(array10, 2, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f2de13b-2773-4a87-a854-ee9486dfcc0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "array12 = np.arange(1, 10).reshape((3, 3))\n",
+    "array12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3808483f-2811-45fd-adbb-a10bcc9d7dc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.roll(array12, 2, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7f6d8d16-2e35-4926-ba13-d5e9cf77660d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.roll(array12, 1, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29b76e3a-8414-4658-b0c6-7795f2186fbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 替换数组元素\n",
+    "np.put(array11, [1, 3, 5, 7], [33, 88])\n",
+    "array11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb4653ef-5c5a-4d3c-adb2-0f10d79ca6c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.place(array11, array11 > 50, [44, 99])\n",
+    "array11"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03bcc628-1471-44ef-ad56-88468c08548d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guido_image = plt.imread('res/guido.jpg')\n",
+    "guido_image.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16fa2543-195b-47d6-8e29-2682800f91aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(np.flip(guido_image, axis=0))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "259830fa-6cd3-43be-bffe-560050f795b9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(np.flip(guido_image, axis=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6059087d-85ba-4151-9af0-8870876a6b1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(np.flip(guido_image, axis=2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "70ebbf72-87c9-41d8-8c46-f1eccb531206",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(guido_image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "015548b9-819c-49ba-a13d-7777662a7414",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imshow(guido_image.swapaxes(0, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c4c00de-f16f-4aac-ae37-e0b9df0eb6c2",
+   "metadata": {},
+   "source": [
+    "#### 普通函数矢量化"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa07c8bc-9558-4d2c-b12c-b0e3814cbb48",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 面试官：讲一讲Python语言中的装饰器\n",
+    "# 用一个函数去装饰另一个函数或者一个类并为其提供额外的能力（横切关注功能）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02e4d3e6-9bc7-462e-8be4-800a1dcdc632",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 面试题：写一个装饰器，如果原函数返回字符串，那么将字符串每个单词首字母大写\n",
+    "from functools import wraps\n",
+    "\n",
+    "\n",
+    "def titlize_str(func):\n",
+    "\n",
+    "    @wraps(func)\n",
+    "    def wrapper(*args, **kwargs):\n",
+    "        result = func(*args, **kwargs)\n",
+    "        if isinstance(result, str):\n",
+    "            result = result.title()\n",
+    "        return result\n",
+    "\n",
+    "    return wrapper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e798956c-d543-4a50-a12b-f7314b98bf40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@titlize_str\n",
+    "def say_hello(name):\n",
+    "    return 'hello, ' + name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82145cd0-bcb2-44b8-90ef-ac461e2120bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 如果不使用@语法糖（便捷语法），也可以通过下面的方式应用装饰器\n",
+    "# say_hello = titlize_str(say_hello)\n",
+    "# say_hello('tom')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "108ca8a5-ec63-458b-b95b-df759b68cb51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "say_hello('tom')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d846bc7-718f-4344-b90e-c7803a104887",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 获取原函数\n",
+    "say_hello = say_hello.__wrapped__\n",
+    "say_hello('tom')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea620684-3375-4876-b097-49e81bf225c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 优化代码的执行性能：空间换时间\n",
+    "from functools import lru_cache\n",
+    "\n",
+    "\n",
+    "@lru_cache(maxsize=128)\n",
+    "def fib(n):\n",
+    "    \"\"\"获取第n个斐波那契数\"\"\"\n",
+    "    if n in (1, 2):\n",
+    "        return 1\n",
+    "    return fib(n - 1) + fib(n - 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f72372e-9cc9-4751-8ee7-e8d9f40727bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(1, 121):\n",
+    "    print(i, fib(i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "29d231a2-57cd-4786-85cd-1366f5378185",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 通过vectorize装饰器将普通函数做矢量化处理\n",
+    "@np.vectorize\n",
+    "def fac(n):\n",
+    "    if n == 0:\n",
+    "        return 1\n",
+    "    return n * fac(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7c04cc06-ba86-4b1b-a4e1-75e4527f6dde",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3, 4, 5, 6, 7, 8])"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "temp = np.arange(1, 9)\n",
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "38e8026a-6e75-4e1b-a646-98c86736797f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([    1,     2,     6,    24,   120,   720,  5040, 40320])"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fac(temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4ac8bb35-87f0-44d0-930e-3fc0c5fb63c3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([26, 68, 73, 33, 64, 54, 26, 40, 60, 36]),\n",
+       " array([37, 56, 65, 30, 57, 36, 61, 54, 34, 52]))"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x1 = np.random.randint(20, 80, 10)\n",
+    "x2 = np.random.randint(30, 70, 10)\n",
+    "x1, x2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "4baced9b-fee2-4c2b-b7ca-dc5914b120b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  4,  1,  3,  1, 18,  1,  2,  2,  4])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from math import gcd, lcm\n",
+    "\n",
+    "gcd = np.vectorize(gcd)\n",
+    "gcd(x1, x2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08c6a075-8b61-4716-92c5-126cd78108c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lcm = np.vectorize(lcm)\n",
+    "lcm(x1, x2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "859bba4b-a0cf-4140-a8de-b2f3fffcd355",
+   "metadata": {},
+   "source": [
+    "### 广播机制\n",
+    "\n",
+    "两个形状（shape属性）不一样的数组如果要做运算，要先通过广播机制使其形状一样才能运算。<br>\n",
+    "如果要执行广播机制使得两个数组形状一样，需要满足以下两个条件其中一个：\n",
+    "\n",
+    "1. 两个数组后缘维度（shape属性从后往前看对应的部分）相同。\n",
+    "2. 两个数组后缘维度不同，但是其中一方为1。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3339c56a-b68c-401e-a27c-134be60ccf14",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp1 = np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2], [3, 3, 3]])\n",
+    "temp2 = np.array([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4ecc9498-792c-4de1-a120-585003b19087",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp1 + temp2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cb20c46-3f87-4346-80ab-7e2786ca1475",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp3 = np.array([[1], [2], [3], [4]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58932222-bb83-43b7-8cef-b9574898dbb5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp1 + temp3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74b6c376-05ac-4049-a4b9-5f0614de780e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp4 = np.array([1 ,2, 3])\n",
+    "temp5 = np.array([[3], [2], [1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eefe6354-4614-4dfb-aa94-3d146b770b3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp4.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04dd44a5-6bea-41eb-aad0-b0a18d64a512",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp5.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "764eb0c3-991d-4a5f-a4eb-7da2c0b445bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp4 + temp5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f1022cb-c07a-4149-aafd-9f53d235da4f",
+   "metadata": {},
+   "source": [
+    "### 矩阵"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a16c29a-088a-473b-b856-3dbd6660f9cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 = np.array([[1, 0, 2], [-1, 3, 1]])\n",
+    "m2 = np.array([[3, 1], [2, 1], [1, 0]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74a54196-ca95-4425-9212-62869795aed7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1afbceea-782d-49c9-b6d4-d0848f3fdd99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc65a37a-84eb-4aac-b63f-5625a0c15a3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m1 @ m2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67be1c96-a20c-4862-87cb-8579d3a303f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.matmul(m1, m2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fd602a8-61fc-4c5c-94a6-a930bcf6fb2f",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "\\begin{cases}\n",
+    "x_1 + 2x_2 + x_3 = 8 \\\\\n",
+    "3x_1 + 7x_2 + 2x_3 = 23 \\\\\n",
+    "2x_1 + 2x_2 + x_3 = 9\n",
+    "\\end{cases}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d41b4856-79c3-4f48-9d8e-58c8d6045884",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "\\boldsymbol{A} = \\begin{bmatrix}\n",
+    "1 & 2 & 1\\\\\n",
+    "3 & 7 & 2\\\\\n",
+    "2 & 2 & 1\n",
+    "\\end{bmatrix}, \\quad\n",
+    "\\boldsymbol{x} = \\begin{bmatrix}\n",
+    "x_1 \\\\\n",
+    "x_2\\\\\n",
+    "x_3\n",
+    "\\end{bmatrix}, \\quad\n",
+    "\\boldsymbol{b} = \\begin{bmatrix}\n",
+    "8 \\\\\n",
+    "23\\\\\n",
+    "9\n",
+    "\\end{bmatrix}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "edb85b44-50b3-4115-8539-5023a19bb2a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m3 = np.arange(1, 10, dtype='f8').reshape(3, 3)\n",
+    "m3[-1, -1] = 8\n",
+    "m3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b90dbf4-05f7-43ad-a37d-20d48d03dd3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 计算矩阵的秩\n",
+    "np.linalg.matrix_rank(m3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a442ab40-97a9-4cf5-8d61-bfcaa3561655",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 逆矩阵 - 奇异矩阵不能求逆矩阵\n",
+    "# LinAlgError: Singular matrix\n",
+    "np.linalg.inv(m3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19be7ff8-6a71-40ad-8e37-20008c56be7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 有唯一解决的条件：系数矩阵的秩等于增广矩阵的秩，同时跟未知数的个数相同。\n",
+    "# 秩（rank）：线性无关的行或者列的数量。\n",
+    "# 线性相关：一个向量可以通过其他向量做线性变换（数乘和加法）得到，那么它们就是线性相关的。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87043db3-e2bd-4a70-950a-d74163afc4d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A = np.array([[1, 2, 1], [3, 7, 2], [2, 2, 1]])\n",
+    "b = np.array([8, 23, 9]).reshape(-1, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "479d272b-2b46-4374-93f3-54e13af52d59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 系数矩阵的秩\n",
+    "np.linalg.matrix_rank(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "faa35591-09da-4232-9b38-72ee2fb824cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 增广矩阵的秩\n",
+    "np.linalg.matrix_rank(np.hstack((A, b)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8d02dee-7d86-4f9d-8e33-77b5a76784a3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 解线性方程组\n",
+    "np.linalg.solve(A, b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "336ee288-5be1-41e5-89cb-e22f465efdd2",
+   "metadata": {},
+   "source": [
+    "$$\n",
+    "A \\cdot x = b\n",
+    "$$\n",
+    "$$\n",
+    "A^{-1} \\cdot A \\cdot x = A^{-1} \\cdot b\n",
+    "$$\n",
+    "$$\n",
+    "I \\cdot x = A^{-1} \\cdot b\n",
+    "$$\n",
+    "$$\n",
+    "x = A^{-1} \\cdot b\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c2bd2fd-8867-4dad-8bbc-b5b175f690b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 通过逆矩阵解线性方程组\n",
+    "np.linalg.inv(A) @ b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b876a47b-a2ab-497a-b564-69750ddb8666",
+   "metadata": {},
+   "source": [
+    "#### 补充 - 用矩阵运算实现图像处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "95e138b5-ed1d-40c3-acbc-821b6ae8cf41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 安装opencv库\n",
+    "# %pip install opencv-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "669c59e5-a477-4bf3-b87b-7486e9d2e9ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def basic_matrix(translation):\n",
+    "    \"\"\"基础变换矩阵\"\"\"\n",
+    "    return np.array([[1, 0, translation[0]], [0, 1, translation[1]], [0, 0, 1]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0338ad44-3142-428f-b1b6-45e691962900",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import copy\n",
+    "\n",
+    "def adjust_transform_for_image(img, trans_matrix):\n",
+    "    \"\"\"根据图像调整变换矩阵\"\"\"\n",
+    "    height, width, *_ = img.shape\n",
+    "    center = np.array([0.5 * width, 0.5 * height])\n",
+    "    return basic_matrix(center) @ trans_matrix @ basic_matrix(-center)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "368578be-3f94-4a37-be51-c6a5dbce1512",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "\n",
+    "def apply_transform(img, transform, border_value=(204, 204, 204)):\n",
+    "    \"\"\"仿射变换\"\"\"\n",
+    "    return cv2.warpAffine(\n",
+    "        img,\n",
+    "        transform[:2, :],\n",
+    "        dsize=(img.shape[1], img.shape[0]),\n",
+    "        flags=cv2.INTER_LINEAR,\n",
+    "        borderMode=cv2.BORDER_CONSTANT,\n",
+    "        borderValue=border_value\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28b22755-4aed-4cd2-a3a2-57bd73f8eb00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def apply(img, trans_matrix):\n",
+    "    \"\"\"应用变换\"\"\"\n",
+    "    temp_matrix = adjust_transform_for_image(img, trans_matrix)\n",
+    "    out_img = apply_transform(img, temp_matrix)\n",
+    "    return out_img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1526fbcd-ad60-4d7c-892c-5c0ae2b7ef3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def scale(img, x_ratio, y_ratio):\n",
+    "    \"\"\"缩放\"\"\"\n",
+    "    scale_matrix = np.array([\n",
+    "        [x_ratio, 0, 0], \n",
+    "        [0, y_ratio, 0], \n",
+    "        [0, 0, 1]\n",
+    "    ])\n",
+    "    return apply(img, scale_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e443448-9b6c-488b-83a5-0de7ac4fca18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rotate(img, degree):\n",
+    "    \"\"\"旋转\"\"\"\n",
+    "    rad = np.deg2rad(degree)\n",
+    "    rotate_matrix = np.array([\n",
+    "        [np.cos(rad), -np.sin(rad), 0], \n",
+    "        [np.sin(rad), np.cos(rad), 0], \n",
+    "        [0, 0, 1]\n",
+    "    ])\n",
+    "    return apply(img, rotate_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2a09187e-2707-49c2-8b8d-84ffa28b6f7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def transvect(img, ratio):\n",
+    "    \"\"\"剪切影射\"\"\"\n",
+    "    transvect_matrix = np.array([\n",
+    "        [1, ratio, 0],\n",
+    "        [0, 1, 0],\n",
+    "        [0, 0, 1]\n",
+    "    ])\n",
+    "    return apply(img, transvect_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9561e506-3b64-49e6-9e9d-8731a2951646",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scaled_img = scale(guido_image, 1.25, 0.75)\n",
+    "plt.imshow(scaled_img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56cb960b-b3f9-4109-95ba-4388a2b1762e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rotated_img = rotate(guido_image, -45)\n",
+    "plt.imshow(rotated_img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc2ac604-0fd7-43d5-be18-407b78e46f58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "transvected_img = transvect(guido_image, -0.3)\n",
+    "plt.imshow(transvected_img)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd91252a-31d4-40d5-82ef-71f22f0bd39c",
+   "metadata": {},
+   "source": [
+    "#### 补充 - 用scipy处理图像"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9052864d-a32d-4cd3-9e2a-8df1654b8a67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.ndimage import gaussian_filter, sobel\n",
+    "\n",
+    "# 获取灰度图\n",
+    "guido_image = plt.imread('res/guido.jpg')\n",
+    "gray_image = np.mean(guido_image, axis=2)\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "\n",
+    "# 灰度图\n",
+    "plt.subplot(1, 4, 1)\n",
+    "plt.imshow(gray_image, cmap=plt.cm.gray)\n",
+    "\n",
+    "# 模糊和锐化\n",
+    "plt.subplot(1, 4, 2)\n",
+    "blurred_image = gaussian_filter(gray_image, 3)\n",
+    "plt.imshow(blurred_image, cmap=plt.cm.gray)\n",
+    "\n",
+    "plt.subplot(1, 4, 3)\n",
+    "filtered_image = gaussian_filter(blurred_image, 1)\n",
+    "sharpen_image = blurred_image + 32 * (blurred_image - filtered_image)\n",
+    "plt.imshow(sharpen_image, cmap=plt.cm.gray)\n",
+    "\n",
+    "# 边缘图\n",
+    "plt.subplot(1, 4, 4)\n",
+    "# 使用索贝尔算子（邻点灰度加权差）进行边缘检测\n",
+    "edge_image = sobel(gray_image)\n",
+    "plt.imshow(edge_image, cmap=plt.cm.gray)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbbc6edf-c643-4cb4-a98b-05e806413478",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.ndimage import rotate, zoom\n",
+    "\n",
+    "plt.figure(figsize=(12, 4))\n",
+    "\n",
+    "# 旋转\n",
+    "plt.subplot(1, 3, 1)\n",
+    "rotated_image = rotate(guido_image, -16, reshape=True)\n",
+    "plt.imshow(rotated_image)\n",
+    "\n",
+    "# 旋转\n",
+    "plt.subplot(1, 3, 2)\n",
+    "rotated_image = rotate(guido_image, -16, reshape=False)\n",
+    "plt.imshow(rotated_image)\n",
+    "\n",
+    "# 缩放\n",
+    "plt.subplot(1, 3, 3)\n",
+    "scaled_image = zoom(guido_image, zoom=(0.8, 1.25, 1))\n",
+    "plt.imshow(scaled_image)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "331862d6-60cd-442f-97a4-bbbfbabfc3fa",
+   "metadata": {},
+   "source": [
+    "#### 补充 - 视频流人脸识别"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "383fa323-db82-4a8b-a13b-9cbe9b8c48d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 安装face_recognition库\n",
+    "# %pip install face_recognition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c1cf7b8-9d89-43d4-8954-616bce780183",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import face_recognition\n",
+    "# from PIL import Image\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "image = face_recognition.load_image_file('res/Solvay.jpg')\n",
+    "locations = face_recognition.face_locations(image)\n",
+    "for location in locations:\n",
+    "    top, right, bottom, left = location\n",
+    "    # Image.fromarray(image[top:bottom, left:right]).show()\n",
+    "    cv2.rectangle(image, (left, top), (right, bottom), (255, 0, 0), 2)\n",
+    "plt.imshow(image)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa76d9c2-c8c3-4cec-8c91-b6b54fe6e32e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import cv2\n",
+    "# import face_recognition\n",
+    "# import numpy as np\n",
+    "\n",
+    "# # 获取摄像头\n",
+    "# video_capture = cv2.VideoCapture(0)\n",
+    "\n",
+    "# # 加载图片获取脸部特征\n",
+    "# obama_image = face_recognition.load_image_file(\"res/obama.jpg\")\n",
+    "# obama_face_encoding = face_recognition.face_encodings(obama_image)[0]\n",
+    "# luohao_image = face_recognition.load_image_file(\"res/luohao.png\")\n",
+    "# luohao_face_encoding = face_recognition.face_encodings(luohao_image)[0]\n",
+    "# guido_image = face_recognition.load_image_file(\"res/guido.jpg\")\n",
+    "# guido_face_encoding = face_recognition.face_encodings(guido_image)[0]\n",
+    "\n",
+    "# # 保存脸部特征和对应的名字\n",
+    "# known_face_encodings = [\n",
+    "#     obama_face_encoding,\n",
+    "#     luohao_face_encoding,\n",
+    "#     guido_face_encoding\n",
+    "# ]\n",
+    "# known_face_names = [\n",
+    "#     \"Barack\",\n",
+    "#     \"Hao\",\n",
+    "#     \"Guido\"\n",
+    "# ]\n",
+    "\n",
+    "# face_locations = []\n",
+    "# face_encodings = []\n",
+    "# face_names = []\n",
+    "# process_this_frame = True\n",
+    "\n",
+    "# while True:\n",
+    "#     # 从视频中读取一帧数据\n",
+    "#     ret, frame = video_capture.read()\n",
+    "\n",
+    "#     # 调整为原始尺寸的四分之一（加速处理）\n",
+    "#     small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)\n",
+    "\n",
+    "#     # BGR转成RGB\n",
+    "#     rgb_small_frame = small_frame[:, :, ::-1]\n",
+    "\n",
+    "#     if process_this_frame:\n",
+    "#         # 找到所有的人脸位置和脸部特征保存在列表中\n",
+    "#         face_locations = face_recognition.face_locations(rgb_small_frame)\n",
+    "#         face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)\n",
+    "\n",
+    "#         face_names = []\n",
+    "#         for face_encoding in face_encodings:\n",
+    "#             # 比较脸部特征\n",
+    "#             matches = face_recognition.compare_faces(known_face_encodings, face_encoding)\n",
+    "#             name = \"Unknown\"\n",
+    "\n",
+    "#             # 通过距离判定最佳匹配并获取对应的名字\n",
+    "#             face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)\n",
+    "#             best_match_index = np.argmin(face_distances)\n",
+    "#             if matches[best_match_index]:\n",
+    "#                name = known_face_names[best_match_index]\n",
+    "\n",
+    "#             face_names.append(name)\n",
+    "\n",
+    "#     process_this_frame = not process_this_frame\n",
+    "\n",
+    "#     # 显示结果\n",
+    "#     for (top, right, bottom, left), name in zip(face_locations, face_names):\n",
+    "#         # 恢复正常的尺寸\n",
+    "#         top, right, bottom, left = top * 4, right * 4, bottom * 4, left * 4\n",
+    "#         # 绘制一个标识人脸的矩形框\n",
+    "#         cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)\n",
+    "#         # 绘制一个填写名字的矩形框\n",
+    "#         cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)\n",
+    "#         # 绘制识别出的人脸对应的名字\n",
+    "#         cv2.putText(frame, name, (left + 6, bottom - 6), cv2.FONT_HERSHEY_DUPLEX, 1.0, (255, 255, 255), 1)\n",
+    "    \n",
+    "#     cv2.imshow('Video', frame)\n",
+    "    \n",
+    "#     # 按键盘上的q键退出窗口 \n",
+    "#     if cv2.waitKey(1) & 0xFF == ord('q'):\n",
+    "#         break\n",
+    "\n",
+    "# video_capture.release()\n",
+    "# cv2.destroyAllWindows()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6989fb7-a3ab-4889-80be-f4ecce033e5c",
+   "metadata": {},
+   "source": [
+    "### 多项式"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c719ca9-de74-4c2e-aaf2-d0a9c833f512",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# NumPy老版本用poly1d表示多项式\n",
+    "p1 = np.poly1d([3, 0, 2, 1])\n",
+    "p2 = np.poly1d([1, 2, 3])\n",
+    "print(p1)\n",
+    "print(p2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6aab279-fa9f-4d4e-b597-b3c51b478777",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 多项式加法\n",
+    "print(p1 + p2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "375a1989-7231-432c-bf33-06fce490cacf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 多项式乘法\n",
+    "print(p1 * p2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4df5662a-c0bc-434d-b53f-623e09b51a76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 令x=2，计算多项式的值\n",
+    "p2(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df150df6-9904-45b7-8704-dbb033552dcb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 求导\n",
+    "print(p1.deriv())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2d3002c-59d6-4e00-b00f-69f3f6ac6609",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 求不定积分\n",
+    "print(p1.integ())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0965b9e9-42fc-425c-90cb-d265e3f6e42f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p3 = np.poly1d([1, 3, 2])\n",
+    "print(p3)\n",
+    "# 令多项式等于0，求解x\n",
+    "print(p3.roots)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5bda2d2-0d61-46db-ac48-0f7ec3438660",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(p3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a71c4799-183e-4096-9903-7edf9464feb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from numpy.polynomial import Polynomial\n",
+    "\n",
+    "# NumPy新版本用Polynomial表示多项式\n",
+    "p1 = Polynomial([1, 2, 0, 3])\n",
+    "print(p1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0002c6e3-697b-425c-a17c-802e10f07981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(p1.deriv())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f9dde21-0854-4ae6-9703-9599a4204003",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(p1.integ())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb191b28-fb2c-460b-9c87-0041b128ba16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 最高次项\n",
+    "p1.degree()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2d6c3103-c5b0-413f-b2de-324b0394e3ae",
+   "metadata": {},
+   "source": [
+    "### 最小二乘解"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7163cb46-44bb-487c-8dd2-7c530574ecc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 每月收入\n",
+    "x = np.array([3200, 4811, 5386, 5564, 6120, 6691, 6906, 7483, 7587, 7890,\n",
+    "              8090, 8300, 8650, 8835, 8975, 9070, 9100, 9184, 9247, 9313, \n",
+    "              9465, 9558, 9853, 9938, 10020, 10242, 10343, 10731, 10885, 10990, \n",
+    "              11100, 11227, 11313, 11414, 11630, 11806, 11999, 12038, 12400, 12547, \n",
+    "              12890, 13050, 13360, 13850, 14890, 14990, 15500, 16899, 17010, 19880])\n",
+    "# 每月网购支出\n",
+    "y = np.array([1761, 882, 1106, 182, 1532, 1978, 2174, 2117, 2134, 1924, \n",
+    "              2207, 2876, 2617, 2683, 3054, 3277, 3345, 3462, 3401, 3591,\n",
+    "              3596, 3671, 3829, 3907, 3852, 4288, 4359, 4099, 4300, 4367,\n",
+    "              5019, 4873, 4674, 5174, 4666, 5797, 5782, 5451, 5487, 5448,\n",
+    "              6002, 6439, 6309, 6045, 5935, 6928, 7356, 6682, 6672, 6582])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a68a1d0d-0dc9-4437-bed9-6e9aff589df1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 定性分析 - 散点图\n",
+    "plt.scatter(x, y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89030441-eefd-4a02-ad41-ba154dbf87b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import stats\n",
+    "\n",
+    "# 夏皮洛检验（正态性判定）\n",
+    "stats.shapiro(x), stats.shapiro(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "93573426-e1c0-4025-8b8f-2d83d69e103c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 定量分析 - 相关系数 - correlation coefficient\n",
+    "# 皮尔逊相关系数（标准化的协方差 - [-1, 1]）\n",
+    "# 1. 连续值且成对出现\n",
+    "# 2. 没有异常值\n",
+    "# 3. 来自于正态总体\n",
+    "np.corrcoef(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b6e47de-8e3b-4be4-a5e4-f1d95649e6f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 计算皮尔逊相关系数\n",
+    "stats.pearsonr(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8e53b1e-b2b1-4222-a69a-dcbca0041d0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "history_data = {key: value for key, value in zip(x, y)}\n",
+    "len(history_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1530478e-ec70-4f87-828f-1d6798c53c5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.random.randint(1, 100, 15).tolist()\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d246563-86fd-418f-b058-4aae08d9b9c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import heapq\n",
+    "\n",
+    "# 通过堆（heap）结构快速的找到TopN元素\n",
+    "print(heapq.nsmallest(3, data))\n",
+    "print(heapq.nlargest(5, data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "866f9fd9-58aa-4c03-9415-99aa236958d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 目标：因为月收入和网购支出之间有强相关关系，所以我们可以通过月收入预测网购支出\n",
+    "# 方法1：输入一个月收入，找到跟这个收入最接近的N条数据，用它们的平均值预测对应的网购支出\n",
+    "# KNN - k最近邻算法（找到k个最近的邻居，用这k个邻居的数据来做出预测）\n",
+    "import heapq\n",
+    "\n",
+    "\n",
+    "def predicate_by_knn(income, k=5):\n",
+    "    \"\"\"KNN算法\"\"\"\n",
+    "    keys = heapq.nsmallest(k, history_data, key=lambda x: (x - income) ** 2)\n",
+    "    return np.mean([history_data[key] for key in keys]).round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "399b9b6a-6625-4dc2-a09c-c0699c538a46",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicate_by_knn(12800)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db0ee4bf-83f8-489f-a543-80854571da60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicate_by_knn(6800)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e514003-c143-4823-91eb-57f7608f6137",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicate_by_knn(20000, k=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa44d3ad-aa3f-44db-889a-8a69b9a9be65",
+   "metadata": {},
+   "source": [
+    "回归模型：\n",
+    "$$ Y = aX + b $$\n",
+    "\n",
+    "损失函数：\n",
+    "$$ MSE = \\frac{1} {N} \\sum (\\hat{y_i} - y_i)^2 $$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "032eb740-ac9a-4526-9eb4-b6770907b07a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# MSE - Mean Squared Error\n",
+    "def get_loss(a, b):\n",
+    "    \"\"\"损失函数\"\"\"\n",
+    "    y_hat = a * x + b\n",
+    "    return np.mean((y_hat - y) ** 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d56ccc4-5a0d-4b3a-89ab-01cf690c4fba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 蒙特卡洛模拟（随机瞎蒙法）\n",
+    "import random\n",
+    "\n",
+    "min_loss = np.inf\n",
+    "ba, bb = None, None\n",
+    "\n",
+    "for _ in range(10000):\n",
+    "    a = random.random() * 0.5 + 0.5\n",
+    "    b = random.random() * 1000 - 2000\n",
+    "    curr_loss = get_loss(a, b)\n",
+    "    if curr_loss < min_loss:\n",
+    "        min_loss = curr_loss\n",
+    "        ba, bb = a, b\n",
+    "        print(min_loss)\n",
+    "print(ba, bb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af5f7d02-f921-4712-82e3-1ce43c86c708",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(x, y)\n",
+    "plt.plot(x, ba * x + bb, color='r', linewidth=4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "207c2da0-371f-40ea-b19f-f56cca8a7d30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predicate_by_regression(income):\n",
+    "    return round(ba * income + bb, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9e3af32-654c-4704-9f32-cfe41f067d06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicate_by_regression(6800)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5133079a-59f8-4435-af1e-fb5bc13c457d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predicate_by_regression(12800)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "200b9821-b8c8-41df-ae8e-d96bf5049415",
+   "metadata": {},
+   "source": [
+    "将回归模型带入损失函数：\n",
+    "$$ f(a, b) = \\frac {1} {N} \\sum_{i=1}^{N}(y_i - (ax_i + b))^2 $$\n",
+    "\n",
+    "如何让$f(a, b)$取到最小值？？？\n",
+    "\n",
+    "求偏导数，并令其等于0。\n",
+    "$$ \\frac {\\partial {f(a, b)}} {\\partial {a}} = \\frac {2} {N} \\sum_{i=1}^{N}(-x_iy_i + x_i^2a + x_ib) = 0 $$ \n",
+    "$$ \\frac {\\partial {f(a, b)}} {\\partial {b}} = \\frac {2} {N} \\sum_{i=1}^{N}(-y_i + x_ia + b) = 0 $$\n",
+    "\n",
+    "求解得到：\n",
+    "$$a = \\frac{\\sum(x_{i} - \\bar{x})(y_{i} - \\bar{y})}{\\sum(x_{i} - \\bar{x})^{2}}$$\n",
+    "$$b = \\bar{y} - a\\bar{x}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ab79db5-85c7-44ad-8ffa-2684fb499f7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_bar, y_bar = np.mean(x), np.mean(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "332777b5-bc3f-44d2-bd54-f8e9a19497aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ba = np.dot((x - x_bar), (y - y_bar)) / np.sum((x - x_bar) ** 2)\n",
+    "bb = y_bar - ba * x_bar\n",
+    "ba, bb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "795f6a40-136a-4772-9e2b-5798022e408d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(x, y)\n",
+    "plt.plot(x, ba * x + bb, color='r', linewidth=4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bfac239-7bf0-4784-a124-bbfdb4cdafe8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 拟合出一个线性回归模型\n",
+    "np.polyfit(x, y, deg=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d5adc92-73e2-40df-843b-02efb28c6ae3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 拟合出一个多项式回归模型\n",
+    "a, b, c = np.polyfit(x, y, deg=2)\n",
+    "a, b, c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c42ee939-e4b9-479e-ad73-66e806e29c6c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(x, y)\n",
+    "plt.plot(x, a * x ** 2 + b * x + c, color='r', linewidth=4)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3363f359-3615-4ffa-a040-41e39c83b38f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Polynomial.fit(x, y, deg=1).convert().coef"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Day66-80/code/day04.ipynb b/Day66-80/code/day04.ipynb
new file mode 100644
index 000000000..604f0a3e5
--- /dev/null
+++ b/Day66-80/code/day04.ipynb
@@ -0,0 +1,1572 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "196a647a-6faa-4aee-a0bf-a345852251dd",
+   "metadata": {},
+   "source": [
+    "## 深入浅出pandas\n",
+    "\n",
+    "pandas是一个支持数据分析全流程的Python开源库，它的作者Wes McKinney于2008年开始开发这个库，其主要目标是提供一个大数据分析和处理的工具。pandas封装了从数据加载、数据重塑、数据清洗到数据透视、数据呈现等一系列操作，提供了三种核心的数据类型：\n",
+    "1. `Series`：数据系列，表示一维的数据。跟一维数组的区别在于每条数据都有对应的索引，处理数据的方法比`ndarray`更为丰富。\n",
+    "2. `DataFrame`：数据框、数据窗、数据表，表示二维的数据。跟二维数组相比，`DataFrame`有行索引和列索引，而且提供了100+方法来处理数据。\n",
+    "3. `Index`：为`Series`和`DataFrame`提供索引服务。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb84f909-921a-47da-87b1-61578c871422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "get_ipython().run_line_magic('config', \"InlineBackend.figure_format = 'svg'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2102e83e-2a6d-47aa-b449-c058bea1a601",
+   "metadata": {},
+   "source": [
+    "### 创建DataFrame对象"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87dbde08-dcab-4ede-a791-b56e11dd9115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c5b2767-2074-4cdf-b1ba-beff6f425942",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stu_names = ['狄仁杰', '白起', '李元芳', '苏妲己', '孙尚香']\n",
+    "cou_names = ['语文', '数学', '英语']\n",
+    "scores_arr = np.random.randint(60, 101, (5, 3))\n",
+    "scores_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8c2a6bf-ca5e-479d-ab63-f5c3620186e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 方法一：通过二维数组构造DataFrame对象\n",
+    "df1 = pd.DataFrame(data=scores_arr, columns=cou_names, index=stu_names)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baad5381-fb7d-4cc9-9288-a05d750144af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 行索引\n",
+    "df1.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7f06b76-b60b-49cb-be72-adafb0978fca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 列索引\n",
+    "df1.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13b1275d-77e5-4d5d-b227-19db3f4196fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 值 - 二维数组\n",
+    "df1.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbf5bb11-1600-4ae4-bc95-369bc8189c20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores_dict = {\n",
+    "    '语文': [95, 91, 69, 82, 92],\n",
+    "    '数学': [86, 88, 80, 67, 100],\n",
+    "    '英语': [75, 86, 71, 94, 81]\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c300bbbd-329a-4852-bf76-78ce1de02b8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 方法二：通过数据字典构造DataFrame对象\n",
+    "df2 = pd.DataFrame(data=scores_dict, index=stu_names)\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "705c0de6-43ff-46c6-85d5-301743d18d43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 查看DataFrame信息\n",
+    "df2.info(memory_usage='deep')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "71417ac2-8f4b-4950-9336-de6fbc1f5da4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 方法三：从CSV文件加载数据创建DataFrame对象\n",
+    "df3 = pd.read_csv(\n",
+    "    'res/2023年北京积分落户数据.csv',\n",
+    "    # encoding='utf-8',  # 指定字符编码\n",
+    "    # sep='',  # 指定字段的分隔符（默认逗号）\n",
+    "    # delimiter='#',\n",
+    "    # header=0,  # 表头所在的行\n",
+    "    # quotechar='\"',  # 包裹字符串的字符（默认双引号）\n",
+    "    # index_col='公示编号',  # 索引列\n",
+    "    # usecols=['公示编号', '姓名', '积分分值'],  # 指定加载的列\n",
+    "    # nrows=10,  # 加载的行数\n",
+    "    # skiprows=np.arange(1, 101),  # 跳过哪些行\n",
+    "    # true_values=['是', 'Yes', 'YES'],  # 哪些值会被视为布尔值True\n",
+    "    # false_values=['否', 'No', 'NO'],  # 哪些值会被视为布尔值False\n",
+    "    # na_values=['---', 'N/A'],  # 哪些值会被视为空值\n",
+    "    # iterator=True,  # 开启迭代器模式\n",
+    "    # chunksize=1000,  # 每次加载的数据体量\n",
+    ")\n",
+    "df3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67b86b13-566b-4f97-86fd-3723ef21a87f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df4 = pd.read_csv('res/big_data_file.csv.gz', low_memory=False)\n",
+    "df4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e52ff38d-8e40-4532-8df9-4d2807a3e2ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df4.info(memory_usage='deep')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65e871ff-e87c-4e6b-86cc-624af7ccbdc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install pyarrow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "48fab84a-8b86-4405-966a-6bfb99582de5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df5 = pd.read_csv('res/big_data_file.csv.gz', engine='pyarrow')\n",
+    "df5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea575de9-4398-46fe-b2f8-8fb37b93179b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df5.info(memory_usage='deep')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5723cf03-b78f-4fc9-943c-f9b10036affa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install xlrd xlwt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb3387b9-3402-4b25-a5d5-ff9690a1ac06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 方法四：从Excel文件加载数据创建DataFrame对象\n",
+    "df6 = pd.read_excel(\n",
+    "    'res/2020年销售数据.xlsx',\n",
+    "    sheet_name='data',\n",
+    ")\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d06abbd8-9a34-4ab3-a75c-76e3ed8eb36c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U pymysql cryptography sqlalchemy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5aa0e35f-2a13-4c8e-a9fd-87b0bf72307e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 方法五：从数据服务器加载数据创建DataFrame对象\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "# URL \n",
+    "engine = create_engine('mysql+pymysql://guest:Guest.618@47.109.26.237:3306/hrs')\n",
+    "engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b344f17-f5a1-4d7d-ad3c-ede4b122609c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dept_df = pd.read_sql('tb_dept', engine, index_col='dno')\n",
+    "dept_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5d1ffa3-6962-4c26-ae92-a8d7bc7da0cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emp_df1 = pd.read_sql('tb_emp', engine, index_col='eno')\n",
+    "emp_df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f84b6886-09d8-4f13-89cc-487574991dba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "emp_df2 = pd.read_sql('tb_emp2', engine, index_col='eno')\n",
+    "emp_df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c60e96d2-9a0d-4901-b39c-c31760de47a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 关闭连接释放资源\n",
+    "engine.connect().close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12086a7a-c161-4753-9a8e-180f9e8b2edf",
+   "metadata": {},
+   "source": [
+    "### 查看信息"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "785e58f9-b3f7-49a6-affc-8caaa66cebf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd8a9156-3939-430d-9738-60b3d8a95563",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 获取前N行\n",
+    "df6.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b75ace23-9b92-4425-b58f-bcd81e8d72e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 获取后N行\n",
+    "df6.tail(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2b2a909-0b40-473c-bb3f-85aca1925a19",
+   "metadata": {},
+   "source": [
+    "### 操作行、列、单元格"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe964b3b-7f51-4202-b528-f5102d9be9f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 访问列\n",
+    "df6['销售日期']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2e5ccb3-4b97-4a02-8316-b1321390f286",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.销售渠道"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80ad78dc-4f47-4421-8478-ba7797350db4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6['销售渠道']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b970671-6f16-4e07-8666-715495de2832",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(df6['销售日期'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c9cb56b-6a2b-479e-8c57-c61683858387",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6[['销售渠道']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75730cd3-0459-4a62-97ee-e037256cc98a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(df6[['销售渠道']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e097e49-b762-4c9f-9d93-98abb1701d97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 访问多个列 - 花式索引\n",
+    "df6[['销售日期', '销售区域', '直接成本']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf31a169-549e-4182-8206-789f97316115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.columns[3:7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "792713c0-13bc-4810-86cc-5f6f6ce78719",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6[df6.columns[3:7]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02d43b17-15e3-44d5-844b-a50d365bf863",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 访问行 - loc属性\n",
+    "df6.loc[1944]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79da6932-f985-44dc-9f4b-e051e4749c65",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.iloc[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6246b39b-7229-4e0f-af7b-0915e707492a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 访问多行 - 花式索引\n",
+    "df6.loc[[0, 100, 58, 1000, 1000, 1000, 1099]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "77321324-0ca9-4c2e-a792-3c717189cb27",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 访问多行 - 切片索引\n",
+    "df6.loc[101:200]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5eb250eb-18e0-4181-a37a-dec55c633116",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df6[101:200]\n",
+    "df6.iloc[101:200]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2daddd7-3635-40b1-9416-c1137315948c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.iloc[-1:-101:-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9321811f-e62b-4db5-a478-cdc0934f097b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 访问单元格\n",
+    "df6.at[2, '售价']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bd1670bc-0a13-457f-95f1-352a4d61b3a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.at[2, '售价'] = 999\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7460ef03-3f45-4cc0-99a3-85039c2606b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.iat[2, -3] = 888\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34c81da6-f58f-4c36-8596-004266e9374b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 添加列\n",
+    "df6['销售额'] = df6['售价'] * df6['销售数量']\n",
+    "df6['季度'] = df6['销售日期'].dt.quarter\n",
+    "df6['月份'] = df6['销售日期'].dt.month\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3c60210-202d-4bd8-8804-1d657746b29c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 添加行 - 实际工作中基本没有意义"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bf78f3d-05a2-4c7a-a0f0-fb6659f1bd6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 删除列\n",
+    "# inplace=False - 默认设定 - 不修改原对象返回修改后的新对象\n",
+    "# inplace=True - 直接修改DataFrame对象不返回新对象 - 方法没有返回值\n",
+    "df6.drop(columns=['季度'], inplace=True)\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdf8cf10-5193-4c38-8fef-bc3d38a8a0a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 删除行\n",
+    "# df6.drop(index=[0, 1, 2, 100, 1944, 1943])\n",
+    "df6.drop(index=[0, 1, 2, 100, 1944, 1943], inplace=True)\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ddfe77d-aa92-4d6a-b2db-8469b1222ed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.drop(index=df6.index[100:200], inplace=True)\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8020bbb0-740e-496a-9224-fe3495a19c92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 重命名\n",
+    "df6.rename(columns={'销售区域': '区域', '销售渠道': '渠道', '销售订单': '订单号'}, inplace=True)\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d028d2be-0944-4b70-a3ea-f7d06cdd458f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 重置索引\n",
+    "# drop=False - 默认值 - 原来的索引变成一个普通列\n",
+    "# drop=True - 原来的索引直接丢弃\n",
+    "df6.reset_index(drop=True, inplace=True)\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb55a518-f4bd-4fac-8554-4353c0798bc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 设置索引\n",
+    "df6.set_index('订单号', inplace=True)\n",
+    "df6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "101bd804-5a90-4cd3-a545-613df6d9b8e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 筛选数据 - 布尔索引\n",
+    "df6[df6['销售额'] > 100000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64c83a43-fcb0-4ba1-9400-ae4a5b21715c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6[(df6['销售额'] > 100000) & (df6['月份'] == 6)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22c01e56-b188-40f7-9e53-3a3d2f0bcb29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6[(df6['销售额'] > 100000) | (df6['月份'] == 6)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5adb86b9-8b31-49cb-9292-94189f3714c5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.query('销售额 > 100000')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b768afa0-7066-4a1d-8f10-b88386587388",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.query('月份 == 6 and 渠道 == \"实体\"')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e57b21c-0565-4352-8924-de169497bce0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.query('销售额 > 100000 and 月份 == 6')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ef8ba56-5293-41b0-8208-85a0eed735e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 随机抽样\n",
+    "df6.sample(n=100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfcd52d7-eac4-4776-b0e3-a37e67e349f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df6.sample(frac=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c654ca8-3179-4fa2-9213-7d7029357342",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# replace=False - 无放回抽样\n",
+    "ignore_rows = np.random.choice(np.arange(1, 1946), size=int(1945 * 0.9), replace=False)\n",
+    "pd.read_excel(\n",
+    "    'res/2020年销售数据.xlsx',\n",
+    "    sheet_name='data',\n",
+    "    skiprows=ignore_rows\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2037ed6a-d616-4c67-9f5d-ea517d6e1c6b",
+   "metadata": {},
+   "source": [
+    "### 数据重塑\n",
+    "\n",
+    "1. 拼接（合并结构一致的数据）\n",
+    "2. 合并（事实表连接维度表）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2184fd4-bd44-459f-bda4-6dc11c09c219",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 拼接两个DataFrame - union\n",
+    "all_emp_df = pd.concat([emp_df1, emp_df2])\n",
+    "all_emp_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05bc65a1-42ac-463c-a089-08fb8dc60855",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 连表 - 连接事实表和维度表 - 用维度把数据分组然后再做聚合\n",
+    "# 连接两个DataFrame（内连接、左外连接、右外连接、全外连接）- join\n",
+    "# how - 连表方式 - inner、left、right、outer\n",
+    "# on - 基于哪个字段连表 - left_on、right_on\n",
+    "all_emp_df = pd.merge(all_emp_df, dept_df, how='inner', on='dno')\n",
+    "all_emp_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c6a3d52d-a04c-494d-9ee9-2dad9805b1c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 作业：在jobs目录下有若干个CVS文件，它们的数据结构是一样的，现在需要把所有CSV文件的数据拼接到一个DataFrame中\n",
+    "import os\n",
+    "\n",
+    "dfs = [pd.read_csv(os.path.join('res/jobs', filename))\n",
+    "       for filename in os.listdir('res/jobs') \n",
+    "       if filename.endswith('.csv')]\n",
+    "pd.concat(dfs, ignore_index=True).to_csv('res/all_jobs.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b9ad1e1-fe5d-45a0-8755-ac6720a32ba0",
+   "metadata": {},
+   "source": [
+    "### 数据清洗\n",
+    "\n",
+    "1. 缺失值\n",
+    "2. 重复值\n",
+    "3. 异常值\n",
+    "4. 预处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45c835c4-559f-45f1-a501-70a8c12bbbb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 甄别缺失值\n",
+    "all_emp_df.isna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd7fbdf8-ebf2-463b-ac3b-cdb24560873a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all_emp_df['comm'].isna()\n",
+    "all_emp_df['comm'].isnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4f16d30-83e9-4761-92a1-780e85e721e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# all_emp_df['comm'].notna()\n",
+    "all_emp_df['comm'].notnull()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f2a153d-ab4a-475e-9ee3-0d623a289f7f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_emp_df['comm'].notna().value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d388d57-fa1a-405b-880e-9316354a6f05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 删除空值 - 删除带有空值的行\n",
+    "all_emp_df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b40fa037-3fab-454e-a300-2e9dcf4b2b60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_emp_df.dropna(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67ae21a1-7dc1-496b-85b5-013d79d25a63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_emp_df.mgr.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66745379-9db7-42b0-ab6b-a55e870a515b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 填充空值\n",
+    "all_emp_df.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1664115-30e0-4946-ae4b-c919bb319ddc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_emp_df.comm.fillna(0).astype('i8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1743531-66a2-42a4-8c28-ad268efc848c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将空值下方的非空值向上填充 - backward fill\n",
+    "all_emp_df.comm.bfill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fcef0a0-ff29-42bd-9955-5a97595390fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将空值上方的非空值向下填充 - forward fill\n",
+    "all_emp_df.comm.ffill()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eeeb9be3-802c-44e3-80a0-465aba1a485a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 通过插值算法填充空值 - interpolate\n",
+    "all_emp_df['comm'] = all_emp_df.comm.interpolate(method='linear')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1f094c3-1cc2-4826-a04a-24150ea9cef8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_emp_df['comm'] = all_emp_df.comm.astype('i8')\n",
+    "all_emp_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a739242d-ebd2-42d2-9ec7-9a5939cbf74a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_emp_df['mgr'] = all_emp_df.mgr.fillna(-1).astype('i8')\n",
+    "all_emp_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd376d13-2245-48b8-ba14-3315d4c48f9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 甄别重复值\n",
+    "all_emp_df.ename.duplicated()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e107a38-c5e8-4e5e-9e42-71481c54e0d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_emp_df.duplicated(['ename', 'job'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "097eaaf2-1112-4e0f-b361-786bf91d6c1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计每个元素出现的频次\n",
+    "all_emp_df.ename.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6494bb56-7ac7-47df-a9f1-960b02586e31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_emp_df.job.value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "172e4d9a-63bd-44ca-98ea-e4614c8823ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计不重复的元素的个数\n",
+    "all_emp_df.ename.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6fa062c-d338-407f-8647-e84878a5642e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 删除重复值\n",
+    "# keep='first' - 默认值，重复元素保留第一项 - 'last' / False\n",
+    "all_emp_df.drop_duplicates(['ename', 'job'], keep='last', inplace=True)\n",
+    "all_emp_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "832a2ea2-6941-4364-b143-af7db9ff9701",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 异常值的甄别\n",
+    "# 数值判定法（data < Q1 - 1.5 * IQR 或者 data > Q3 + 1.5 * IQR）\n",
+    "\n",
+    "\n",
+    "def find_outliers_by_iqr(data, whis=1.5):\n",
+    "    q1, q3 = np.quantile(data, [0.25, 0.75])\n",
+    "    iqr = q3 - q1\n",
+    "    return data[(data < q1 - whis * iqr) | (data > q3 + whis * iqr)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cd5d6aa-c60e-483e-995c-a627a0dfec15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp = np.random.normal(80, 8, 50).round(0)\n",
+    "temp = np.append(temp, [120, 160, 200, 40, 20, -50])\n",
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2121dab4-0efc-4fcd-a5fe-67585552cb53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "find_outliers_by_iqr(temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "da048825-3f88-4009-9db5-159e8e883b10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "find_outliers_by_iqr(temp, whis=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0da7034b-2350-43ff-a6eb-9e7f4361bdee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# zscore判定法（三西格玛法则 ---> 68-95-99.7法则）\n",
+    "\n",
+    "\n",
+    "def find_outliers_by_zscore(data, mul=3):\n",
+    "    mu, sigma = np.mean(data), np.std(data)\n",
+    "    zscore = (data - mu) / sigma\n",
+    "    return data[np.abs(zscore) > mul]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e88616c0-a4d8-4fd8-9ec2-e761cb5ba056",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "find_outliers_by_zscore(temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c902031c-2f78-4721-9734-5c5b0ca81650",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "find_outliers_by_zscore(temp, mul=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e295014-d582-4e78-b5b9-6d9f0463ff8d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "find_outliers_by_zscore(df6.直接成本)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97b98c82-fd09-42a9-8a75-a3e71ae10fbc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 根据离群点的行索引删除行\n",
+    "df6.drop(index=find_outliers_by_zscore(df6.直接成本).index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0053ed12-c09f-4331-a6dd-487ff990c680",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "med_value = np.median(temp)\n",
+    "med_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f02c2985-1b07-4b1c-b248-aa1de9e98451",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "find_outliers_by_zscore(temp, mul=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "485adc15-f39d-419b-9869-2b366f5d88ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.in1d(temp, find_outliers_by_zscore(temp, mul=2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce92f242-1f0f-476e-ae85-91e1615783ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 替换离群点\n",
+    "np.place(temp, np.in1d(temp, find_outliers_by_zscore(temp, mul=2)), med_value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10b0b0bc-f98c-40fe-890f-976df9d9c52b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d970e838-42f2-44d0-8f2d-07ebbf6de2b0",
+   "metadata": {},
+   "source": [
+    "#### 案例1：招聘数据清洗和预处理\n",
+    "\n",
+    "1. 数据加载\n",
+    "2. 去重\n",
+    "3. 数据抽取\n",
+    "4. 拆分列\n",
+    "5. 替换值\n",
+    "6. 数据筛选"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ec417a9-457f-434e-96a6-f4fd35d75987",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df = pd.read_csv('res/all_jobs.csv')\n",
+    "jobs_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74e0e4a5-3c03-4617-9661-8cfa03b88fd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 根据URI列去重\n",
+    "jobs_df.drop_duplicates('uri', inplace=True)\n",
+    "jobs_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6cca7b8b-25f1-46b8-9946-34ba90f42116",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 通过正则表达式从列中提取信息\n",
+    "jobs_df[['salary_lower', 'salary_upper']] = jobs_df.salary.str.extract(r'(\\d+)-(\\d+)').astype('i8')\n",
+    "jobs_df['salary'] = (jobs_df.salary_lower + jobs_df.salary_upper) / 2\n",
+    "jobs_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ffaea2af-09f6-4577-9c0d-024966d6854f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df.drop(columns=['uri', 'city'], inplace=True)\n",
+    "jobs_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9ba5998-ca1d-44c8-87ca-363356074dd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 拆分列\n",
+    "jobs_df['city'] = jobs_df.site.str.split(expand=True)[0]\n",
+    "jobs_df.drop(columns='site', inplace=True)\n",
+    "jobs_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "933e9006-4f5e-4238-b6d9-940dfeb6caf1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 字符串正则表达式替换\n",
+    "jobs_df['year'] = jobs_df.year.replace(r'5-10年|10年以上', '5年以上', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d10a9c1c-a9d5-49e1-8fdf-a68b5bb3d59a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df.year.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d248e233-bac5-48d5-8a69-a1f04350867a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df['edu'] = jobs_df.edu.replace(r'中专|高中', '学历不限', regex=True)\n",
+    "jobs_df['edu'] = jobs_df.edu.replace(r'硕士|博士', '研究生', regex=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eec6fbd5-2355-4674-9e5d-7f47a5a808a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df.edu.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "352b1921-aa2b-4016-af3e-02032b2a3935",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df['job_name'] = jobs_df.job_name.str.lower()\n",
+    "jobs_df = jobs_df[jobs_df.job_name.str.contains('python|数据|产品|运营|data', regex=True)]\n",
+    "jobs_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df370013-1278-48d2-9891-8647df3c5e15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df.to_csv('res/cleand_jobs.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ee07676-737c-420e-b11a-235ff7f2c4c8",
+   "metadata": {},
+   "source": [
+    "#### 案例2：北京积分落户数据预处理\n",
+    "\n",
+    "1. 加载数据\n",
+    "2. 日期时间处理\n",
+    "3. 年龄段分箱\n",
+    "4. 落户积分归一化"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1232d023-7591-47b3-b67b-4920642dd28d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "settle_df = pd.read_csv('res/2023年北京积分落户数据.csv', index_col='公示编号')\n",
+    "settle_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "734eb268-3ad7-4e67-9661-08328075992b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "settle_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "63698465-ddcd-430c-bd96-e78abaaebda3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将字符串处理成日期\n",
+    "settle_df['出生年月'] = pd.to_datetime(settle_df['出生年月'])\n",
+    "settle_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "989c56c7-85fa-4180-9b86-5247a41cdbab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将生日换算成年龄\n",
+    "settle_df['年龄'] = (pd.to_datetime('2023-01-01') - settle_df.出生年月).dt.days // 365\n",
+    "settle_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4191c7a2-19fd-4347-ac79-2371c8e59c10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将年龄划分到年龄段 - 分箱 - 数据桶\n",
+    "settle_df['年龄段'] = pd.cut(\n",
+    "    settle_df.年龄,\n",
+    "    bins=np.arange(35, 61, 5),\n",
+    "    labels=['35~39岁', '40~44岁', '45~49岁', '50~54岁', '55~59岁'],\n",
+    "    right=False\n",
+    ")\n",
+    "settle_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea2e0c9b-0aa0-41d3-a52a-6926b797465c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计每个元素出现的频次\n",
+    "temp = settle_df.年龄段.value_counts()\n",
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "30843274-b940-4527-92ed-97db86bb4ec7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.cm.Greens(np.linspace(0.9, 0.1, 5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "375dd407-9d0a-4788-a38e-3a37efbb6d3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制柱状图\n",
+    "temp.plot(\n",
+    "    kind='bar',      # 图表类型\n",
+    "    figsize=(8, 4),  # 图表尺寸\n",
+    "    xlabel='',       # 横轴标签\n",
+    "    ylabel='Count',  # 纵轴标签\n",
+    "    width=0.5,       # 柱子宽度\n",
+    "    hatch='//',      # 柱子条纹\n",
+    "    color=plt.cm.Greens(np.linspace(0.9, 0.3, temp.size))  # 颜色值\n",
+    ")\n",
+    "\n",
+    "for i in range(temp.size):\n",
+    "    # plt.text(横坐标, 纵坐标, 标签内容)\n",
+    "    plt.text(i, temp.iloc[i] + 30, temp.iloc[i], ha='center')\n",
+    "\n",
+    "# 定制横轴的刻度\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e020ba6c-d16d-482f-ad3b-a9e855257b91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制饼图\n",
+    "temp.plot(\n",
+    "    kind='pie',\n",
+    "    ylabel='',\n",
+    "    autopct='%.1f%%',           # 自动计算并显示百分比\n",
+    "    wedgeprops={'width': 0.3},  # 环状结构部分的宽度\n",
+    "    pctdistance=0.85,           # 百分比到圆心的距离\n",
+    "    labeldistance=1.1,          # 标签到圆心的距离\n",
+    "    # shadow=True,              # 阴影效果\n",
+    "    # startangle=0,             # 起始角度\n",
+    "    counterclock=True,          # 是否反时针方向绘制\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e846eec2-6c95-409c-8b15-2b14cab3f57c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# agg - aggregate - 聚合\n",
+    "settle_df.积分分值.agg(['mean', 'max', 'min', 'std', 'skew', 'kurt'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1669102-1c03-4751-813c-b241a05718e3",
+   "metadata": {},
+   "source": [
+    "线性归一化：\n",
+    "$$\n",
+    "x^{\\prime} = \\frac{x - x_{min}}{x_{max} - x_{min}}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8d9dca7-b976-43ab-96b8-abefca66cc53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将积分分值处理成0~1范围的值\n",
+    "max_score, min_score = settle_df.积分分值.agg(['max', 'min'])\n",
+    "max_score, min_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10acd550-8422-4934-b38f-03554f86d305",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# map - 映射 - 将指定的函数作用到数据系列的每个元素上\n",
+    "# apply - 应用 - 将指定的函数应用到数据系列的每个元素上\n",
+    "settle_df['线性归一化积分'] = settle_df.积分分值.map(lambda x: (x - min_score) / (max_score - min_score)).round(2)\n",
+    "settle_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55e57b00-cb9e-4c9e-bc59-e99b738e2f5d",
+   "metadata": {},
+   "source": [
+    "zscore标准化：\n",
+    "$$\n",
+    "x^{\\prime} = \\frac{x - \\mu}{\\sigma}\n",
+    "$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5fc6260-5337-4161-99f0-d7be43d59361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mu, sigma = settle_df.积分分值.agg(['mean', 'std'])\n",
+    "settle_df['zscore评分'] = settle_df.积分分值.apply(lambda x: (x - mu) / sigma)\n",
+    "settle_df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Day66-80/code/day05.ipynb b/Day66-80/code/day05.ipynb
new file mode 100644
index 000000000..0834e84f3
--- /dev/null
+++ b/Day66-80/code/day05.ipynb
@@ -0,0 +1,673 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "97982f4b-b3d0-47fe-b6e8-a1a327d03d93",
+   "metadata": {},
+   "source": [
+    "## 深入浅出pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "834d7d19-4015-4048-a1a6-2b8c756f0115",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56becc00-f1c7-4f81-86d1-7f7b23fd65d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = 'svg'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c81bfe94-906c-4e13-b321-0e6c397aea46",
+   "metadata": {},
+   "source": [
+    "### 数据透视\n",
+    "\n",
+    "1. 数据聚合（指标统计）\n",
+    "2. 排序和头部值\n",
+    "3. 透视表和交叉表"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7836ce8b-1e1d-40e5-b01c-c228ebf60928",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_df = pd.read_excel('res/2020年销售数据.xlsx', sheet_name='data')\n",
+    "sales_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca3b2020-9470-4e49-a3ca-066dc9a437b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "224ae45b-172a-48bc-8df0-7d6c7099529a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 添加销售额、毛利润、月份列\n",
+    "sales_df['销售额'] = sales_df.售价 * sales_df.销售数量\n",
+    "sales_df['毛利润'] = sales_df.销售额 - sales_df.直接成本\n",
+    "sales_df['月份'] = sales_df.销售日期.dt.month\n",
+    "sales_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8878bfd-64de-4a6a-9ae3-36e9af40d45e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_tag(price):\n",
+    "    if price < 300:\n",
+    "        return '低端'\n",
+    "    elif price < 800:\n",
+    "        return '中端'\n",
+    "    return '高端'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cba4262-b952-4fce-9b07-fed9c5f2a2e3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 根据商品的价格添加价位标签\n",
+    "sales_df['价位'] = sales_df.售价.apply(make_tag)\n",
+    "sales_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2a2b472-55fc-4d35-8530-445e8ffbf800",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计北极星指标\n",
+    "GMV, profit, quantity = sales_df[['销售额', '毛利润', '销售数量']].sum()\n",
+    "print(f'销售额: {GMV}元')\n",
+    "print(f'毛利润: {profit}元')\n",
+    "print(f'销售数量: {quantity}件')\n",
+    "print(f'毛利率: {profit / GMV:.2%}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b6ac225-6834-462f-a5b7-8be8297b66d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计每个月的销售额和毛利润\n",
+    "temp1 = sales_df.groupby('月份')[['销售额', '毛利润']].agg('sum')\n",
+    "temp1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8f179a1-c270-458d-be84-b862ca2c143d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 使用透视表统计每个月的销售额和毛利润\n",
+    "pd.pivot_table(\n",
+    "    sales_df,\n",
+    "    index='月份',\n",
+    "    values=['销售额', '毛利润'],\n",
+    "    aggfunc='sum'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a3cb912-c47f-4d47-8bd4-ffc9eb171cba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制折线图\n",
+    "temp1.plot(\n",
+    "    kind='line',\n",
+    "    figsize=(10, 5),\n",
+    "    y=['销售额', '毛利润'],   # 放到纵轴上的数据\n",
+    "    xlabel='',              # 横轴的标签\n",
+    "    ylabel='销售额和毛利润',  # 纵轴的标签\n",
+    "    marker='^',             # 标记点符号\n",
+    ")\n",
+    "# plt.fill_between(np.arange(1, 13), temp1.销售额, where=temp1.销售额 >= 3e6, facecolor='red', alpha=0.25)\n",
+    "# plt.fill_between(np.arange(1, 13), temp1.销售额, where=temp1.销售额 < 3e6, facecolor='green', alpha=0.25)\n",
+    "# 定制纵轴的取值范围\n",
+    "plt.ylim(0, 6e6)\n",
+    "# 定制横轴的刻度\n",
+    "plt.xticks(np.arange(1, 13), labels=[f'{x}月' for x in range(1, 13)])\n",
+    "# 定制标题\n",
+    "plt.title('2020年月度销售额和毛利润', fontdict={'fontsize': 22, 'color': 'navy'})\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "389db27d-2b11-47ea-a8c6-660fcc39b863",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.cm.RdYlBu_r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "621a2487-2d15-450d-9f69-df8089616bd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 计算月环比\n",
+    "temp1['销售额月环比'] = temp1.销售额.pct_change()\n",
+    "temp1['毛利润月环比'] = temp1.毛利润.pct_change()\n",
+    "# 索引重排序\n",
+    "temp1 = temp1.reindex(columns=['销售额', '销售额月环比', '毛利润', '毛利润月环比'])\n",
+    "# 渲染输出\n",
+    "temp1.style.format(\n",
+    "    formatter={\n",
+    "        '销售额月环比': '{:.2%}',\n",
+    "        '毛利润月环比': '{:.2%}'\n",
+    "    },\n",
+    "    na_rep='-------'\n",
+    ").background_gradient(\n",
+    "    'RdYlBu_r',\n",
+    "    subset=['销售额月环比', '毛利润月环比']\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b158aea-fe6e-4938-a4f8-4b880c6f23d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制横线图\n",
+    "mu = temp1.销售额.mean()\n",
+    "temp1['diff'] = temp1.销售额 - mu\n",
+    "temp1['colors'] = temp1.销售额.map(lambda x: 'green' if x > mu else 'red')\n",
+    "\n",
+    "plt.figure(figsize=(8, 6), dpi=200)\n",
+    "plt.hlines(y=temp1.index, xmin=0, xmax=temp1['diff'], color=temp1.colors, alpha=0.6, linewidth=6)\n",
+    "plt.yticks(np.arange(1, 13), labels=[f'{x}月' for x in np.arange(1, 13)])\n",
+    "# 定制网格线\n",
+    "plt.grid(linestyle='--', linewidth=0.4, alpha=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "810936a1-f6c5-43e9-9eda-b9fe8014ba9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 各品牌对销售额贡献占比\n",
+    "temp2 = sales_df.groupby('品牌')['销售额'].sum()\n",
+    "temp2.plot(\n",
+    "    kind='pie',\n",
+    "    ylabel='',\n",
+    "    autopct='%.2f%%',  # 自动计算并显示百分比\n",
+    "    pctdistance=0.82,  # 百分比标签到圆心的距离\n",
+    "    wedgeprops=dict(width=0.35, edgecolor='w'),  # 定制环状饼图\n",
+    "    explode=[0.1, 0, 0, 0, 0],  # 分离饼图\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1465d545-14e8-435d-9b12-651df3bab9ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 各销售区域每个月的销售额\n",
+    "temp3 = sales_df.groupby(['销售区域', '月份'], as_index=False)[['销售额']].sum()\n",
+    "# pivot - 将行旋转到列上（窄表 ----> 宽表）\n",
+    "# melt - 将列旋转到行上（宽表 ----> 窄表）\n",
+    "temp3.pivot(index='销售区域', columns='月份', values='销售额').fillna(0).astype('i8')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1adf23bb-7120-416c-846e-1098aac2e1f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 创建透视表\n",
+    "pd.pivot_table(\n",
+    "    sales_df,\n",
+    "    index='销售区域',\n",
+    "    columns='月份',\n",
+    "    values='销售额',\n",
+    "    aggfunc='sum',\n",
+    "    fill_value=0,\n",
+    "    margins=True,\n",
+    "    margins_name='总计'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fdf54c9a-64c3-453d-bc5b-533fce7b25ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将价位字段处理成category类型并指定排序的顺序\n",
+    "sales_df['价位'] = sales_df.价位.astype('category').cat.reorder_categories(['高端', '中端', '低端'])\n",
+    "sales_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e706575-3260-4b37-99fe-696db4f6fe7b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计每个月各种价位产品的销量\n",
+    "temp4 = sales_df.pivot_table(\n",
+    "    index='价位',\n",
+    "    columns='月份',\n",
+    "    values='销售数量',\n",
+    "    observed=False,\n",
+    "    fill_value=0,\n",
+    "    aggfunc='sum'\n",
+    ")\n",
+    "temp4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12527025-64f5-4a0a-9bf5-6bdbc4b6f93b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 交叉表\n",
+    "pd.crosstab(\n",
+    "    index=sales_df.价位,\n",
+    "    columns=sales_df.月份,\n",
+    "    values=sales_df.销售数量,\n",
+    "    aggfunc='sum'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d99ecd49-2669-493c-ae04-974afa71dc4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "blood_types = np.array(['B', 'A', 'O', 'O', 'AB', 'B', 'O', 'B', 'AB', 'A', 'A', 'O', 'B', 'O', 'O', 'O', 'O', 'A', 'B', 'B'])\n",
+    "personality_types = np.array(['𝛃', '𝛂', '𝛂', '𝛂', '𝛃', '𝛂', '𝛄', '𝛄', '𝛂', '𝛄', '𝛃', '𝛂', '𝛂', '𝛂', '𝛄', '𝛄', '𝛂', '𝛂', '𝛂', '𝛂'])\n",
+    "\n",
+    "# 创建交叉表\n",
+    "pd.crosstab(\n",
+    "    index=blood_types,\n",
+    "    columns=personality_types,\n",
+    "    rownames=['血型'],\n",
+    "    colnames=['人格'],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cba2e9e-8335-4081-a253-5ab6d08d8559",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制堆叠柱状图\n",
+    "temp4.T.plot(\n",
+    "    figsize=(10, 4),\n",
+    "    kind='bar',\n",
+    "    width=0.6,\n",
+    "    xlabel='',\n",
+    "    ylabel='销售数量',\n",
+    "    stacked=True\n",
+    ")\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9eba1698-d8a5-4b66-b617-fc68072a670e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 让每一项数据除以对应月份的销售数量之和\n",
+    "temp5 = temp4.T.divide(temp4.sum(), axis=0)\n",
+    "temp5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ce2e80e-0af9-4162-a46d-b5d357a9362d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制百分比堆叠柱状图\n",
+    "temp5.plot(\n",
+    "    figsize=(10, 4),\n",
+    "    kind='bar',\n",
+    "    width=0.6,\n",
+    "    xlabel='',\n",
+    "    ylabel='销量占比',\n",
+    "    stacked=True\n",
+    ")\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.yticks(np.linspace(0, 1, 6), labels=[f'{x:.0%}' for x in np.linspace(0, 1, 6)])\n",
+    "plt.legend(loc='lower center')\n",
+    "\n",
+    "for i in temp5.index:\n",
+    "    y1, y2, y3 = temp5.loc[i]\n",
+    "    plt.text(i - 1, y2 / 2 + y1, f'{y2:.2%}', ha='center', va='center', fontdict={'size': 8})\n",
+    "    plt.text(i - 1, y3 / 2 + y2 + y1, f'{y3:.2%}', ha='center', va='center', fontdict={'size': 8})\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82ac6b37-302e-4913-a973-4a7f4e6daf1d",
+   "metadata": {},
+   "source": [
+    "### 作业：招聘岗位数据分析\n",
+    "\n",
+    "1. 统计出城市、招聘信息、招聘岗位的数量和平均月薪。\n",
+    "2. 统计每个城市的岗位数量从高到低排序。\n",
+    "3. 统计每个城市的平均薪资从高到低排序。\n",
+    "4. 统计招聘岗位对学历要求的占比。\n",
+    "5. 统计招聘岗位对工作年限的要求占比。\n",
+    "6. 分析薪资跟学历和工作年限的关系。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91e8b171-7568-4da1-8170-5d13fc8945bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df = pd.read_csv('res/cleaned_jobs.csv')\n",
+    "jobs_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7cadf547-f7aa-494b-beeb-678f5a09e967",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计北极星指标\n",
+    "city_count = jobs_df['city'].nunique()\n",
+    "info_count = jobs_df['company_name'].count()\n",
+    "post_count = jobs_df['pos_count'].sum()\n",
+    "salary_avg = jobs_df['salary'].mean().round(1)\n",
+    "print(f'城市数量: {city_count}')\n",
+    "print(f'信息数量: {info_count}')\n",
+    "print(f'岗位数量: {post_count}')\n",
+    "print(f'平均薪资: {salary_avg}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb9fc057-df56-4f01-a08e-ab3427dca467",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计每个城市的岗位数量从高到低排序\n",
+    "jobs_df.groupby('city')[['pos_count']].sum().sort_values(by='pos_count', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "430af08e-2ce2-4f4f-9d64-df2d09c7c9f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.pivot_table(\n",
+    "    jobs_df,\n",
+    "    index='city',\n",
+    "    values='pos_count',\n",
+    "    aggfunc='sum'\n",
+    ").sort_values(by='pos_count', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcd4aeca-bb90-42e6-b45c-6cdea2c76271",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df.groupby('city')[['salary']].mean().round(1).sort_values(by='salary', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e93f5c8-da3e-4064-ac77-748eb3167504",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计每个城市的平均薪资从高到低排序\n",
+    "pd.pivot_table(\n",
+    "    jobs_df,\n",
+    "    index='city',\n",
+    "    values='salary',\n",
+    "    aggfunc='mean'\n",
+    ").round(1).sort_values(by='salary', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ae067bf-2981-49f2-a224-28a5223dc21f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df['edu'] = jobs_df.edu.astype('category').cat.reorder_categories(['学历不限', '大专', '本科', '研究生'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a87fab8f-18af-4c0b-9546-63d79778b4d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计招聘岗位对学历要求占比\n",
+    "pd.pivot_table(\n",
+    "    jobs_df,\n",
+    "    index='edu',\n",
+    "    values='pos_count',\n",
+    "    aggfunc='sum',\n",
+    "    observed=True\n",
+    ").plot(\n",
+    "    kind='pie',\n",
+    "    ylabel='',\n",
+    "    subplots=True,\n",
+    "    legend=False,\n",
+    "    autopct='%.2f%%',\n",
+    "    pctdistance=0.85,\n",
+    "    wedgeprops={'width': 0.35}\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15202818-48a8-40bb-8701-47bc1b481f80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "jobs_df['year'] = jobs_df.year.astype('category').cat.reorder_categories(['应届生', '1年以内', '经验不限', '1-3年', '3-5年', '5年以上'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d965229-36d5-4c38-850c-3300726f208a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计招聘岗位对工作年限要求绘制饼图\n",
+    "pd.pivot_table(\n",
+    "    jobs_df,\n",
+    "    index='year',\n",
+    "    values='pos_count',\n",
+    "    aggfunc='sum',\n",
+    "    observed=True\n",
+    ").plot(\n",
+    "    kind='pie',\n",
+    "    y='pos_count',\n",
+    "    ylabel='',\n",
+    "    legend=False,\n",
+    "    autopct='%.2f%%',\n",
+    "    pctdistance=0.85,\n",
+    "    wedgeprops={'width': 0.35}\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c7ab419-0bc4-428f-be9b-6d4d617b6663",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计不同学历和工作年限平均薪资\n",
+    "temp6 = pd.pivot_table(\n",
+    "    jobs_df,\n",
+    "    index='edu',\n",
+    "    columns='year',\n",
+    "    values='salary',\n",
+    "    observed=False,\n",
+    "    fill_value=0\n",
+    ").round(1)\n",
+    "temp6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8492b17-5ae8-47f0-a058-ab303a6087a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制热力图\n",
+    "plt.imshow(temp6, cmap='Reds')\n",
+    "plt.xticks(np.arange(6), labels=temp6.columns)\n",
+    "plt.yticks(np.arange(4), labels=temp6.index)\n",
+    "\n",
+    "for i in range(temp6.index.size):\n",
+    "    for j in range(temp6.columns.size):\n",
+    "        value = temp6.iat[i, j]\n",
+    "        color = 'w' if value > salary_avg else 'k'\n",
+    "        plt.text(j, i, value, ha='center', va='center', color=color)\n",
+    "\n",
+    "# 定制颜色条\n",
+    "plt.colorbar()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3eb8cd0-fe07-43a4-9514-8a38c08ca081",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6287994-0865-432a-9932-92b05b5bf7e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "sns.heatmap(temp6, cmap='Reds', annot=True)\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel('')\n",
+    "plt.yticks(rotation=0)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Day66-80/code/day06.ipynb b/Day66-80/code/day06.ipynb
new file mode 100644
index 000000000..0cdd91fa4
--- /dev/null
+++ b/Day66-80/code/day06.ipynb
@@ -0,0 +1,809 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e4ad3216-d45c-4328-a509-3c01e0fec4d5",
+   "metadata": {},
+   "source": [
+    "## 深入浅出pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5faacbe5-d44a-4e0e-a287-19270bc1a693",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "get_ipython().run_line_magic('config', \"InlineBackend.figure_format = 'svg'\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b6101ba-4d7b-408c-8f94-76cf789ab71a",
+   "metadata": {},
+   "source": [
+    "### 科比投篮数据分析\n",
+    "\n",
+    "1. 科比使用得最多的投篮动作\n",
+    "2. 科比交手次数最多的球队\n",
+    "3. 科比有出手的比赛有多少场\n",
+    "4. 科比职业生涯（常规赛+季后赛）总得分（不含罚篮）\n",
+    "5. 科比得分最高的五场比赛（对手、投篮次数、得分、命中率）\n",
+    "6. 科比得分最多的三个赛季（赛季、投篮次数、得分、命中率）"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5008a34f-e108-4c35-a71e-0b28e2f4cbfc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 不限制最大显示的列数\n",
+    "pd.set_option('display.max_columns', None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "526e6f01-7b18-487a-bd89-fdc5a5a494b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 加载科比投篮数据\n",
+    "kobe_df = pd.read_csv('res/科比投篮数据.csv', index_col='shot_id')\n",
+    "kobe_df.tail(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "127b7ab9-568c-4982-8238-d5fcafe6e9b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kobe_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cbe2e96-0bbe-43a7-bafc-f5f956090ea4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 科比使用得最多的投篮动作是什么\n",
+    "kobe_df.action_type.value_counts().index[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46bd394d-05f3-4adc-bc69-d8ef70e1f877",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "kobe_df.groupby('action_type')['action_type'].count().nlargest(1).index[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3ebb1eab-a675-4f2f-833b-ff7c4207ec77",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 科比交手次数最多的球队是哪支队伍\n",
+    "kobe_df.drop_duplicates('game_id').opponent.value_counts().index[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b53491a-1e04-4684-94de-ddcf4eba0a9d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "kobe_df.drop_duplicates('game_id').groupby('opponent').opponent.count().idxmax()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf1b9f70-23df-4103-87db-7727329eb679",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 科比有出手的比赛有多少场\n",
+    "kobe_df.game_id.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91dfb7c3-c2d8-4eff-9df9-8be558e054d1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 统计科比常规赛和季后赛的投篮命中率\n",
+    "temp = kobe_df.dropna().pivot_table(index=['playoffs', 'shot_type'], columns=['shot_made_flag'], values='game_id', aggfunc='count')\n",
+    "temp = temp.divide(temp.sum(axis=1), axis=0)\n",
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e998d2d7-1259-479d-b74e-6e9dccf8de36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 填充shot_made_flag字段的缺失值\n",
+    "def handle(x):\n",
+    "    playoffs, shot_type, shot_made_flag = x\n",
+    "    if np.isnan(shot_made_flag):\n",
+    "        shot_made_flag = 1 if np.random.random() < temp.at[(playoffs, shot_type), 1.0] else 0\n",
+    "    return shot_made_flag\n",
+    "\n",
+    "\n",
+    "kobe_df['shot_made_flag'] = kobe_df[['playoffs', 'shot_type', 'shot_made_flag']].apply(handle, axis=1).astype('?')\n",
+    "kobe_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1fd5dbf5-e89f-4f6c-9867-cca583d08527",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 处理得分字段\n",
+    "kobe_df['point'] = kobe_df.shot_type.str[0].astype('i8')\n",
+    "kobe_df['point'] = kobe_df[['shot_made_flag', 'point']].apply(lambda x: x.loc['point'] if x.loc['shot_made_flag'] else 0, axis=1)\n",
+    "kobe_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b431b8a2-600e-4ed2-8092-0ed8598043f4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 参考数据：投篮命中数11719\n",
+    "kobe_df.shot_made_flag.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2fdf20e-54d2-43b4-a5b2-52b6f7e815c4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 参考数据：不含罚篮的投篮得分25265\n",
+    "kobe_df.point.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8929cfa5-2152-45ad-9ac4-ce9154ef8830",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 科比得分最多的赛季是哪个赛季和分数\n",
+    "kobe_df.groupby('season').point.sum().nlargest(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5bd3ccf-6654-4c84-a295-4142fd24cebe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 获得得分最高的5场比赛的game_id\n",
+    "index = kobe_df.groupby('game_id').point.sum().nlargest(5).index.values\n",
+    "index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e48b6dfb-c6f8-40c5-beda-a1e50ae742b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 用布尔索引筛选数据\n",
+    "kobe_df[np.in1d(kobe_df.game_id, index)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6ba3500-a50f-400c-a2bc-7f04cf0e3393",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 用query方法筛选数据\n",
+    "kobe_df.query('game_id in @index')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "171a9b52-e917-4b41-9472-80532cc1a72e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# 科比得分最高的五场比赛（对手、投篮次数、得分、命中率）\n",
+    "# 参考数据（含罚篮）：TOR - 81分 / POR - 65分 / DAL - 62分 / NYK - 61分 / MEM - 60分 / UTA - 60分\n",
+    "df1 = kobe_df[np.in1d(kobe_df.game_id, index)].groupby(\n",
+    "    'game_id'\n",
+    ")[['game_date', 'opponent', 'game_id', 'shot_made_flag', 'point']].agg({\n",
+    "    'game_date': 'max',\n",
+    "    'opponent': 'max',\n",
+    "    'game_id': 'count',\n",
+    "    'shot_made_flag': 'sum',\n",
+    "    'point': 'sum'\n",
+    "})\n",
+    "df1['rate'] = df1.shot_made_flag / df1.game_id\n",
+    "df1.drop(columns=['shot_made_flag'], inplace=True)\n",
+    "df1.reset_index(drop=True, inplace=True)\n",
+    "df1.set_index('game_date', inplace=True)\n",
+    "df1.rename(columns={'opponent': '对手', 'game_id': '出手次数', 'point': '得分', 'rate': '命中率'}, inplace=True)\n",
+    "df1.sort_values(by='得分', ascending=False).style.format(formatter={'命中率': '{:.2%}'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a129218e-4ceb-4004-9b91-5668b8d3940f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df2 = kobe_df.query('game_id in @index').groupby(\n",
+    "    'game_id'\n",
+    ")[['game_date', 'opponent', 'game_id', 'shot_made_flag', 'point']].agg({\n",
+    "    'game_date': 'max',\n",
+    "    'opponent': 'max',\n",
+    "    'game_id': 'count',\n",
+    "    'shot_made_flag': 'sum',\n",
+    "    'point': 'sum'\n",
+    "})\n",
+    "df2['rate'] = df2.shot_made_flag / df2.game_id\n",
+    "df2.drop(columns=['shot_made_flag'], inplace=True)\n",
+    "df2.reset_index(drop=True, inplace=True)\n",
+    "df2.set_index('game_date', inplace=True)\n",
+    "df2.rename(columns={'opponent': '对手', 'game_id': '出手次数', 'point': '得分', 'rate': '命中率'}, inplace=True)\n",
+    "df2.sort_values(by='得分', ascending=False).style.format(formatter={'命中率': '{:.2%}'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "473883f8-acfa-4d32-806d-4201c50106e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 科比得分最多的三个赛季（赛季、投篮次数、得分、命中率）\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1ae87f9-1698-48b8-be11-0edd47cae298",
+   "metadata": {},
+   "source": [
+    "### 深圳二手房数据分析\n",
+    "\n",
+    "1. 统计深圳二手房单价分布规律\n",
+    "2. 统计深圳二手房总价分布规律\n",
+    "3. 统计每个区总价和均价的均值\n",
+    "4. 深圳每个区单价Top3的商圈\n",
+    "5. 哪种户型的二手房数量最多\n",
+    "6. 总价Top10的二手房分布在哪些区"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "041ac635-9598-4a10-9e04-664678f23448",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_df = pd.read_csv('res/深圳二手房数据.csv')\n",
+    "sz_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7298d0f3-d9ef-4cee-975d-8d720711d749",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_df.drop(columns='Unnamed: 0', inplace=True)\n",
+    "sz_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f140da58-6846-4595-b365-359cdc5b7dd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 修正列名\n",
+    "sz_df.rename(columns={'hourseType': 'house_type', 'hourseSize': 'house_size'}, inplace=True)\n",
+    "sz_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "731f00a4-1db8-4aac-ba7c-7bf65ebc3b40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 将tax字段处理为bool类型\n",
+    "sz_df['tax'] = sz_df.tax.fillna('').astype('?')\n",
+    "sz_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b2bad9d-ae47-478d-8be1-139121742012",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a780b30-c8d8-4b40-b416-99b1d6572e26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 获取描述性统计信息\n",
+    "sz_df.total_price.agg(['mean', 'max', 'min', 'skew', 'kurt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2b4a48c-9775-424f-bc10-4bc1d71b3813",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_df.unit_price.agg(['mean', 'max', 'min', 'skew', 'kurt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c742fc0c-9b3c-4b8d-861a-879012677458",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_df.house_size.agg(['mean', 'max', 'min', 'skew', 'kurt'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c6a64d4-7bd8-4d04-b9a9-3d787ba9c732",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_df[sz_df.unit_price < 10000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22989374-3017-48a5-9802-61459df9f8d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 删除异常数据（单价小于10000）\n",
+    "sz_df.drop(index=sz_df[sz_df.unit_price < 10000].index, inplace=True)\n",
+    "sz_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02a1b493-6d66-4e31-8298-bbe50eae9676",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 删除面积在10平米以下200平米以上的房屋信息\n",
+    "sz_df.drop(index=sz_df.query('house_size < 10 or house_size > 200').index, inplace=True)\n",
+    "sz_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf37b2b5-1493-4a2b-a16c-3df39a99fcf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 添加一个总房间数字段\n",
+    "sz_df['rooms_num'] = sz_df.house_type.str.extract('(\\d+)室(\\d+)厅').astype('i8').sum(axis=1)\n",
+    "sz_df.rooms_num.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f605b5bd-9ccf-4946-b084-c05db4b1f78b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sz_df.tail(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f197094-8843-4575-bf87-8e6599d4dce9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 删除房间总数大于8个的房屋信息\n",
+    "sz_df.drop(index=sz_df[sz_df.rooms_num > 8].index, inplace=True)\n",
+    "sz_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "920d0e09-fa58-456a-9d3d-6329ef49ae67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 单价分布\n",
+    "sz_df.unit_price.plot(kind='hist', figsize=(9, 5), bins=15, ylabel='')\n",
+    "plt.xticks(np.arange(10000, 210001, 20000))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2fd023d2-b7a9-456e-a58d-731512711484",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 总价分布\n",
+    "sz_df.total_price.plot(kind='hist', figsize=(9, 5), bins=15, ylabel='')\n",
+    "plt.xticks(np.arange(100, 2901, 400))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1d68635-d7fa-44dc-97dc-8938cba1de80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 统计每个区总价和均价的均值\n",
+    "sz_df.pivot_table(\n",
+    "    index='area',\n",
+    "    values=['title', 'unit_price', 'total_price'],\n",
+    "    aggfunc={'title': 'count', 'unit_price': 'mean', 'total_price': 'mean'}\n",
+    ").round(1).sort_values(\n",
+    "    'unit_price', ascending=False\n",
+    ").style.format(\n",
+    "    formatter={\n",
+    "        'total_price': '￥{:.0f}万元',\n",
+    "        'unit_price': '￥{:,.0f}元'\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "538ebe18-432e-4951-8667-ce90cb73ae5e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 深圳每个区房屋平均单价Top3商圈\n",
+    "temp_df = sz_df.groupby(['area', 'position'])[['unit_price']].mean().round(1)\n",
+    "temp_df['rank'] = temp_df.unit_price.groupby('area').rank(method='dense', ascending=False).astype('i8')\n",
+    "temp_df.query('rank <= 3')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04aaf617-240d-4fc2-b702-fa3e03978347",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 深圳每个区房屋平均单价Top3商圈\n",
+    "temp_df = sz_df.groupby(['area', 'position'], as_index=False)[['unit_price']].mean().round(1)\n",
+    "temp_df = temp_df.groupby('area')[['position', 'unit_price']].apply(lambda x: x.nlargest(3, 'unit_price'))\n",
+    "temp_df.style.hide(level=1).format(formatter={'unit_price': '￥{:,.0f}元'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38f91365-6b40-40a1-b8bd-f0fd4cac9d50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 哪种户型的二手房数量最多\n",
+    "sz_df.groupby('house_type').house_type.count().nlargest(1).index[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4db07743-51cd-4007-8faf-7aa5714167d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 总价Top10的二手房分布在哪些区\n",
+    "top10 = sz_df.total_price.nlargest(10).index.values\n",
+    "# 通过花式索引获取对应的行\n",
+    "sz_df.loc[top10].groupby('area').area.count()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "870a64c4-3164-4b96-b910-fc80b5e44853",
+   "metadata": {},
+   "source": [
+    "### 销售利润下滑诊断分析"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1e63fa6-018e-4cea-9899-05bc4a4aaae2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detail_df = pd.read_excel('res/商品销售明细表.xlsx', sheet_name='Sheet1')\n",
+    "outlet_df = pd.read_excel('res/门店信息维度表.xlsx', sheet_name='Sheet1')\n",
+    "commod_df = pd.read_excel('res/商品信息维度表.xlsx', sheet_name='Sheet1')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb430f84-5628-4dc5-a63a-36520cfcf66a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detail_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "41a2f93a-3799-4253-8acd-6f3498283010",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "detail_df.rename(columns={'日期(年月日)': '销售日期'}, inplace=True)\n",
+    "detail_df['销售日期'] = pd.to_datetime(detail_df.销售日期)\n",
+    "detail_df['月份'] = detail_df.销售日期.dt.month\n",
+    "detail_df['利润额'] = detail_df.销售额 - detail_df.成本额\n",
+    "detail_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f94c5f3-c799-470f-a693-7d01afa4f88f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp1 = detail_df.groupby('月份')[['销售额', '利润额']].sum()\n",
+    "temp1['销售月环比'] = temp1.销售额.pct_change()\n",
+    "temp1['利润月环比'] = temp1.利润额.pct_change()\n",
+    "temp1[['销售额', '销售月环比', '利润额', '利润月环比']].style.format(\n",
+    "    formatter={\n",
+    "        '销售月环比': '{:.2%}',\n",
+    "        '利润月环比': '{:.2%}',\n",
+    "    },\n",
+    "    na_rep='--------'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56b4ab4a-aa35-49c5-9387-80b545799cfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp1.plot(kind='line', figsize=(9, 5), xlabel='', y=['销售额', '利润额'], color=['navy', 'coral'], marker='o')\n",
+    "plt.ylim(0, 1.4e7)\n",
+    "plt.grid(axis='y', linestyle=':', alpha=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00ca4494-b03d-4f42-890f-9d8088bb61e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.ticker as tkr\n",
+    "\n",
+    "ax = temp1.销售额.plot(kind='line', figsize=(9, 5), marker='o', color='navy', linestyle='--')\n",
+    "temp1.利润额.plot(ax=ax, kind='line', marker='*', color='darkgreen', linestyle='--', xlabel='')\n",
+    "plt.ylim(0, 14000000)\n",
+    "plt.legend(loc='lower right')\n",
+    "\n",
+    "# 基于ax构建双胞胎坐标系（共享横轴，自己定制纵轴）\n",
+    "ax2 = ax.twinx()\n",
+    "ax2.yaxis.set_major_formatter(tkr.PercentFormatter(xmax=1, decimals=0))\n",
+    "profs_rates = temp1.利润额 / temp1.销售额\n",
+    "profs_rates.plot(ax=ax2, kind='line', marker='^', color='r', linestyle=':', label='毛利率')\n",
+    "plt.ylim(0.45, 0.65)\n",
+    "plt.legend()\n",
+    "plt.grid(axis='y', linestyle=':', alpha=0.5)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "637679a6-f7e1-4de8-a17e-4541790aa99c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 事实表连接维度表\n",
+    "merged_df = pd.merge(detail_df, outlet_df, how='left', on='门店编码')\n",
+    "merged_df = pd.merge(merged_df, commod_df, how='left', on='商品编码')\n",
+    "august_df = merged_df.query('月份 == 8')\n",
+    "august_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e49793fb-6149-4366-94f6-2546fd14065e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_df2 = august_df.groupby('省份')[['销售额', '成本额']].sum()\n",
+    "temp_df2.nlargest(10, '成本额')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6959bb8b-a2e5-4948-bfc1-c78be4a3d1d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_df2.nlargest(10, '成本额').plot(kind='bar', figsize=(9, 5), xlabel='')\n",
+    "plt.xticks(rotation=0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e62162ff-af16-4a8f-af56-32b64712f0a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_df3 = august_df.query('省份 == \"湖南省\"').groupby('城市')[['销售额', '成本额']].sum()\n",
+    "temp_df3['利润率'] = (temp_df3.销售额 - temp_df3.成本额) / temp_df3.销售额\n",
+    "temp_df3.nsmallest(3, '利润率').style.format(formatter={'利润率': '{:.2%}'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5626ff61-f8dd-4871-9ad3-28c25910d36b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_df4 = august_df.query('省份 == \"湖南省\" and 城市 == \"长沙市\"').groupby('门店名称')[['销售额', '成本额']].sum()\n",
+    "temp_df4['利润率'] = (temp_df4.销售额 - temp_df4.成本额) / temp_df4.销售额\n",
+    "temp_df4.sort_values(by='利润率').style.format(formatter={'利润率': '{:.2%}'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4993f987-37ad-450c-971a-d54ff4f6bc29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "august_df = august_df.query('省份 == \"湖南省\" and 城市 == \"长沙市\" and 门店名称 == \"长沙梅溪湖店\"')\n",
+    "august_df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04dfedf1-e7f1-4493-be54-a32b5e997e0d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_df5 = august_df.groupby('商品类别')[['销售额', '成本额']].sum()\n",
+    "temp_df5['利润率'] = (temp_df5.销售额 - temp_df5.成本额) / temp_df5.销售额\n",
+    "temp_df5.sort_values(by='利润率').style.format(formatter={'利润率': '{:.2%}'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fd3e504-3292-4bab-b3dd-f2f3629f6285",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp_df6 = august_df.query('商品类别 == \"零食\"').groupby('商品名称')[['销售额', '成本额']].sum()\n",
+    "temp_df6['利润率'] = (temp_df6.销售额 - temp_df6.成本额) / temp_df6.销售额\n",
+    "temp_df6.sort_values(by='利润率').style.format(formatter={'利润率': '{:.2%}'})"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Day66-80/code/day07.ipynb b/Day66-80/code/day07.ipynb
new file mode 100644
index 000000000..465b64d2b
--- /dev/null
+++ b/Day66-80/code/day07.ipynb
@@ -0,0 +1,1246 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "89fbe897-855c-4208-848b-b411727c8eb9",
+   "metadata": {},
+   "source": [
+    "## 数据可视化详解"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5b31dba-6894-4c28-83d6-25bade7f28ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "get_ipython().run_line_magic('config', \"InlineBackend.figure_format = 'svg'\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef91deaa-1007-465c-aa39-4b5198a57d84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd7bef85-c7a9-4dad-822b-23bc36922deb",
+   "metadata": {},
+   "source": [
+    "### matplotlib\n",
+    "\n",
+    "整体架构：\n",
+    "1. 渲染层 - 底层的画布，图像的渲染，事件交互\n",
+    "2. 组件层 - 各种各样的统计图表\n",
+    "3. 脚本层 - 提供编程接口，通过调函数实现图表绘制\n",
+    "\n",
+    "绘图过程：\n",
+    "1. 创建画布 - plt.figure(figsize, dpi) --> Figure\n",
+    "2. 创建坐标系 - plt.subplot(nrows, ncols, index)\n",
+    "3. 绘制图表\n",
+    "   - 折线图：plt.plot()\n",
+    "   - 散点图：plt.scatter()\n",
+    "   - 柱状图：plt.bar() / plt.barh()\n",
+    "   - 饼图：plt.pie()\n",
+    "   - 直方图：plt.hist()\n",
+    "   - 箱线图：plt.boxplot()\n",
+    "4. 保存图表 - plt.savefig()\n",
+    "5. 显示图表 - plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73435be1-71e3-49e7-8703-7479be7f2b29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-2 * np.pi, 2 * np.pi, 120)\n",
+    "y1 = np.sin(x)\n",
+    "y2 = np.cos(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c57ddb9-5338-4008-8878-901f92cf94ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install PyQt5\n",
+    "# %pip install PyQt6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ba631ea-11ed-48d3-b06b-5efd93222f07",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 魔法指令 - 将统计图表渲染到Qt窗口\n",
+    "# %matplotlib qt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c8fdb5f-18be-4ed5-826a-6b2466630154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(8, 4), dpi=200)\n",
+    "plt.subplot(1, 1, 1)\n",
+    "plt.plot(x, y1, label='正弦', linewidth=0.5, linestyle='--', color='#D75281')\n",
+    "plt.plot(x, y2, label='余弦', marker='.', color='#0096FF')\n",
+    "plt.legend(loc='lower center')\n",
+    "# plt.savefig('aa.jpg')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80c166e9-0a24-4593-bfde-ceb592c5949c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 魔法指令 - 将统计图表渲染到浏览器\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39fbcb43-325c-4878-a23e-ae5d27ae08e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 创建画布（Figure）\n",
+    "plt.figure(figsize=(8, 4), dpi=200)\n",
+    "# 创建坐标系（Axes）\n",
+    "ax = plt.subplot(1, 1, 1)\n",
+    "ax.spines['left'].set_position('center')\n",
+    "ax.spines['bottom'].set_position('center')\n",
+    "ax.spines['top'].set_visible(False)\n",
+    "ax.spines['right'].set_visible(False)\n",
+    "# 绘制折线图\n",
+    "plt.plot(x, y1, label='正弦', color='#D75281', linewidth=2, linestyle='-.')\n",
+    "plt.plot(x, y2, label='余弦', color='#0096FF', marker='.')\n",
+    "# 定制图表的标题\n",
+    "plt.title(r'$sin(\\alpha)$和$cos(\\alpha)$曲线图', fontdict=dict(fontsize=18, color='#FFFFFF', backgroundcolor='#0F3D3E'))\n",
+    "# 定制横轴的刻度\n",
+    "plt.xticks(\n",
+    "    np.arange(-2 * np.pi, 2 * np.pi + 0.1, np.pi / 2),\n",
+    "    labels=[r'$ -2\\pi $', r'$ -\\frac{3\\pi}{2} $', r'$ -\\pi $', r'$ -\\frac{\\pi}{2} $', \n",
+    "            '0', r'$ \\frac{\\pi}{2} $', r'$ \\pi $', r'$ \\frac{3\\pi}{2} $', r'$ 2\\pi $']\n",
+    ")\n",
+    "# 定制纵轴的刻度\n",
+    "plt.yticks(np.arange(-1, 1.5, 0.5))\n",
+    "# 添加标注（文字和箭头）\n",
+    "plt.annotate(r'$ sin(\\alpha) $', xytext=(0.5, -0.5), xy=(0, 0), color='#EF5B0C', \n",
+    "             arrowprops=dict(arrowstyle='fancy', connectionstyle='arc3, rad=0.25', color='darkgreen'))\n",
+    "# 定制图例\n",
+    "plt.legend(loc='lower right')\n",
+    "# 保存图表\n",
+    "# plt.savefig('aa.jpg')\n",
+    "# 显示图表\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e72a3b87-6cf9-4709-abe1-4c0a67d9fe00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x2 = np.linspace(0.1, 10.1, 60)\n",
+    "y3 = np.log2(x2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf5cecf5-3316-4f60-ae1c-42ed31467827",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 创建画布（Figure）\n",
+    "plt.figure(figsize=(8, 6), dpi=200)\n",
+    "# 创建坐标系（Axes）\n",
+    "plt.subplot(2, 2, 1)\n",
+    "# 绘制折线图\n",
+    "plt.plot(x, y1, label='正弦', color='#D75281', linewidth=2, linestyle='-.')\n",
+    "# 创建坐标系\n",
+    "plt.subplot(2, 2, 2)\n",
+    "plt.plot(x, y2, label='余弦', color='#0096FF', marker='.')\n",
+    "# 创建坐标系\n",
+    "# plt.subplot(2, 2, (3, 4))\n",
+    "plt.subplot(2, 1, 2)\n",
+    "# 绘制散点图\n",
+    "plt.plot(x2, y3, color='darkgreen', marker='*')\n",
+    "# 显示图表\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eaf4e484-6821-4e8c-839f-fae932b68224",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 创建画布（Figure）\n",
+    "plt.figure(figsize=(8, 6), dpi=200)\n",
+    "# 创建网格对象（GridSpec）\n",
+    "grid = plt.GridSpec(2, 3)\n",
+    "plt.subplot(grid[:, 0])\n",
+    "plt.subplot(grid[0, 1:])\n",
+    "plt.subplot(grid[1, 1])\n",
+    "plt.subplot(grid[1, 2])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1d3b736-da33-479c-9d0c-a84145dd59e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 月收入\n",
+    "income = np.fromstring('5550, 7500, 10500, 15000, 20000, 25000, 30000, 40000', sep=',')\n",
+    "income"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cedebe7e-58eb-456a-bb64-4e75d1729c92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 月网购支出\n",
+    "outcome = np.fromstring('800, 1800, 1250, 2000, 1800, 2100, 2500, 3500', sep=',')\n",
+    "outcome"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eab6ea54-8c49-46a8-918c-8ce7aebd521e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.scatter(income, outcome)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef2ee591-6fa9-4b02-9f10-6f2dc776b8fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 网购次数\n",
+    "nums = np.array([5, 3, 10, 5, 12, 20, 8, 10])\n",
+    "nums"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "239a9331-c6a4-450c-bc13-f4e3c2969f2e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.get_cmap('rainbow_r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b9abf01-b4a8-4b3e-8de4-8284f6fc3efe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制散点图 ---> 气泡图（引入第三个变量）\n",
+    "plt.scatter(income, outcome, s=nums * 30, c=nums, cmap='rainbow_r')\n",
+    "plt.colorbar()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a36fd15-7779-4dd5-a205-478f1ae1506c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data1 = np.random.randint(100, 500, 4)\n",
+    "data2 = np.random.randint(200, 600, 4)\n",
+    "data3 = np.random.randint(300, 500, 4)\n",
+    "quarter = np.arange(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc760daf-de0c-478d-a84b-d5ed0fd5a5e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "errs = np.random.randint(10, 30, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "143a0b7b-a3ff-4a76-9032-fd236826966c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 柱状图\n",
+    "plt.bar(quarter-0.2, data1, label='A组', hatch='//', width=0.2)\n",
+    "plt.bar(quarter, data2, label='B组', hatch='xxx', width=0.2)\n",
+    "plt.bar(quarter+0.2, data3, label='C组', width=0.2, yerr=errs)\n",
+    "plt.xticks(quarter, labels=[f'Q{i}' for i in range(1, 5)])\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a802edea-f2f7-4265-809f-78318d1c1e57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 堆叠柱状图\n",
+    "plt.bar(quarter, data1, label='A组', width=0.4)\n",
+    "plt.bar(quarter, data2, label='B组', width=0.4, bottom=data1)\n",
+    "plt.bar(quarter, data3, label='C组', width=0.4, bottom=data1 + data2)\n",
+    "plt.xticks(quarter, labels=[f'Q{i}' for i in range(1, 5)])\n",
+    "for i in range(quarter.size):\n",
+    "    plt.text(i, data1[i] // 2, data1[i], ha='center', va='center', color='w')\n",
+    "    plt.text(i, data1[i] + data2[i] // 2, data2[i], ha='center', color='w')\n",
+    "    plt.text(i, data1[i] + data2[i] + data3[i] // 2, data3[i], ha='center', color='w')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f01c63f-f1da-42af-a04a-e45d95c2262b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 计算每组数据的占比\n",
+    "temp_df = pd.DataFrame(data={\n",
+    "    'A组': data1,\n",
+    "    'B组': data2,\n",
+    "    'C组': data3,\n",
+    "})\n",
+    "temp_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccb6a3f1-ae06-467d-9cc0-00b3eb1b9b84",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pct_data = temp_df.apply(lambda x: x / temp_df.sum(axis=1))\n",
+    "pct_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ec0bf97-62ed-4ec7-8998-b1277d752098",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.ticker as tkr\n",
+    "\n",
+    "# 绘制百分比堆叠柱状图\n",
+    "data1, data2, data3 = pct_data.A组, pct_data.B组, pct_data.C组\n",
+    "plt.bar(quarter, data1, label='A组', width=0.4)\n",
+    "plt.bar(quarter, data2, label='B组', width=0.4, bottom=data1)\n",
+    "plt.bar(quarter, data3, label='C组', width=0.4, bottom=data1 + data2)\n",
+    "plt.xticks(quarter, labels=[f'Q{i}' for i in range(1, 5)])\n",
+    "# plt.yticks(np.arange(0, 1.1, 0.2), labels=[f'{i}%' for i in range(0, 101, 20)])\n",
+    "plt.gca().yaxis.set_major_formatter(tkr.PercentFormatter(xmax=1, decimals=0))\n",
+    "for i in range(quarter.size):\n",
+    "    plt.text(i, data1[i] / 2, f'{data1[i] * 100:.1f}%', ha='center', color='w')\n",
+    "    plt.text(i, data1[i] + data2[i] / 2, f'{data2[i] * 100:.1f}%', ha='center', color='w')\n",
+    "    plt.text(i, data1[i] + data2[i] + data3[i] / 2, f'{data3[i] * 100:.1f}%', ha='center', color='w')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98d80c5c-2f1a-41e2-9222-d396bb4a580e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = ['苹果', '香蕉', '桃子', '荔枝', '石榴', '山竹', '榴莲']\n",
+    "data = np.random.randint(100, 500, 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33086285-51e7-40af-b7fe-c775b63beff0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制饼图\n",
+    "plt.figure(figsize=(5, 5), dpi=120)\n",
+    "plt.pie(\n",
+    "    data, \n",
+    "    labels=labels,  # 每块饼对应的标签\n",
+    "    labeldistance=1.1,  # 标签到圆心的距离\n",
+    "    autopct='%.1f%%',  # 自动计算和显示百分比\n",
+    "    pctdistance=0.88,  # 百分比到圆心的距离\n",
+    "    explode=[0, 0, 0.05, 0, 0, 0, 0.1],  # 分离距离（分离饼图）\n",
+    "    shadow=True,  # 阴影效果\n",
+    "    wedgeprops={'width': 0.25, 'edgecolor': 'w'},  # 楔子属性（环状饼图）\n",
+    "    textprops={'fontsize': 9, 'color': 'k'}  # 文本属性\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14813133-5f35-4324-b001-17708872d80c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1 = np.random.normal(0, 1, 5000)\n",
+    "x2 = np.random.random(100000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2607c7d9-77e6-4b01-8d15-1e8b61dc6dba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制直方图（概率密度）\n",
+    "plt.hist(x1, bins=20, density=True, color='darkcyan')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c5b6ed0-0c80-4040-8cdd-51c00083d252",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制直方图（累积分布）\n",
+    "plt.hist(x2, bins=10, density=True, cumulative=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "80db5ad6-30e8-47e3-a092-5b2a934f35c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = x1[::5]\n",
+    "data.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a14acac1-6515-4b23-befc-0f28f3193b88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制箱线图\n",
+    "plt.boxplot(data, showmeans=True, notch=True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8c5c940-7f83-4fc2-803f-b302d78cd30d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 堆叠折线图（面积图）\n",
+    "plt.figure(figsize=(8, 4))\n",
+    "days = np.arange(7)\n",
+    "sleeping = [7, 8, 6, 6, 7, 8, 10]\n",
+    "eating = [2, 3, 2, 1, 2, 3, 2]\n",
+    "working = [7, 8, 7, 8, 6, 2, 3]\n",
+    "playing = [8, 5, 9, 9, 9, 11, 9]\n",
+    "plt.stackplot(days, sleeping, eating, working, playing)\n",
+    "plt.legend(['睡觉', '吃饭', '工作', '玩耍'], fontsize=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66c79bd0-8253-4fa2-b303-696598e952d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "category_names = ['Strongly disagree', 'Disagree',\n",
+    "                  'Neither agree nor disagree', 'Agree', 'Strongly agree']\n",
+    "results = {\n",
+    "    'Question 1': [10, 15, 17, 32, 26],\n",
+    "    'Question 2': [26, 22, 29, 10, 13],\n",
+    "    'Question 3': [35, 37, 7, 2, 19],\n",
+    "    'Question 4': [32, 11, 9, 15, 33],\n",
+    "    'Question 5': [21, 29, 5, 5, 40],\n",
+    "    'Question 6': [8, 19, 5, 30, 38]\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def survey(results, category_names):\n",
+    "    \"\"\"\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    results : dict\n",
+    "        A mapping from question labels to a list of answers per category.\n",
+    "        It is assumed all lists contain the same number of entries and that\n",
+    "        it matches the length of *category_names*.\n",
+    "    category_names : list of str\n",
+    "        The category labels.\n",
+    "    \"\"\"\n",
+    "    labels = list(results.keys())\n",
+    "    data = np.array(list(results.values()))\n",
+    "    data_cum = data.cumsum(axis=1)\n",
+    "    category_colors = plt.colormaps['RdYlGn'](\n",
+    "        np.linspace(0.15, 0.85, data.shape[1]))\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(9.2, 5))\n",
+    "    ax.invert_yaxis()\n",
+    "    ax.xaxis.set_visible(False)\n",
+    "    ax.set_xlim(0, np.sum(data, axis=1).max())\n",
+    "\n",
+    "    for i, (colname, color) in enumerate(zip(category_names, category_colors)):\n",
+    "        widths = data[:, i]\n",
+    "        starts = data_cum[:, i] - widths\n",
+    "        rects = ax.barh(labels, widths, left=starts, height=0.5,\n",
+    "                        label=colname, color=color)\n",
+    "\n",
+    "        r, g, b, _ = color\n",
+    "        text_color = 'white' if r * g * b < 0.5 else 'darkgrey'\n",
+    "        ax.bar_label(rects, label_type='center', color=text_color)\n",
+    "    ax.legend(ncols=len(category_names), bbox_to_anchor=(0, 1),\n",
+    "              loc='lower left', fontsize='small')\n",
+    "\n",
+    "    return fig, ax\n",
+    "\n",
+    "\n",
+    "survey(results, category_names)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36499ccc-a243-4f96-9759-454d3267bb9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 雷达图（极坐标折线图）\n",
+    "labels = np.array(['速度', '力量', '经验', '防守', '发球', '技术'])\n",
+    "malong_values = np.array([93, 95, 98, 92, 96, 97])\n",
+    "shuigu_values = np.array([30, 40, 65, 80, 45, 60])\n",
+    "angles = np.linspace(0, 2 * np.pi, labels.size, endpoint=False)\n",
+    "# 加一条数据让图形闭合\n",
+    "malong_values = np.append(malong_values, malong_values[0])\n",
+    "shuigu_values = np.append(shuigu_values, shuigu_values[0])\n",
+    "angles = np.append(angles, angles[0])\n",
+    "\n",
+    "# 创建画布\n",
+    "plt.figure(figsize=(4, 4), dpi=120)\n",
+    "# 创建坐标系\n",
+    "ax = plt.subplot(projection='polar')\n",
+    "# 绘图和填充\n",
+    "plt.plot(angles, malong_values, color='r', linewidth=2, label='马龙')\n",
+    "plt.fill(angles, malong_values, color='r', alpha=0.25)\n",
+    "plt.plot(angles, shuigu_values, color='g', linewidth=2, label='水谷隼')\n",
+    "plt.fill(angles, shuigu_values, color='g', alpha=0.25)\n",
+    "# 设置文字和网格线\n",
+    "# ax.set_thetagrids(angles[:-1] * 180 / np.pi, labels, fontsize=10)\n",
+    "# ax.set_rgrids([0, 20, 40, 60, 80, 100], fontsize=10)\n",
+    "ax.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3939fab-09a5-4e21-9bee-0081acddb36e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 玫瑰图（圆形柱状图）\n",
+    "group1 = np.random.randint(20, 50, 4)\n",
+    "group2 = np.random.randint(10, 60, 4)\n",
+    "x = np.array([f'A组-Q{i}' for i in range(1, 5)] + [f'B组-Q{i}' for i in range(1, 5)])\n",
+    "y = np.array(group1.tolist() + group2.tolist())\n",
+    "theta = np.linspace(0, 2 * np.pi, x.size, endpoint=False)\n",
+    "width = 2 * np.pi / x.size\n",
+    "\n",
+    "# 产生随机颜色\n",
+    "colors = np.random.rand(8, 3)\n",
+    "# 将柱状图投影到极坐标\n",
+    "ax = plt.subplot(projection='polar')\n",
+    "plt.bar(theta, y, width=width, color=colors, bottom=0)\n",
+    "ax.set_thetagrids(theta * 180 / np.pi, x, fontsize=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0390d2a-a0a9-483a-a4bc-ce1c5059f30b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %matplotlib qt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d338e6e3-bf98-428b-bac2-693bb1a2a760",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制3D曲面\n",
+    "from mpl_toolkits.mplot3d import Axes3D\n",
+    "\n",
+    "fig = plt.figure(figsize=(8, 4), dpi=120)\n",
+    "ax = Axes3D(fig, auto_add_to_figure=False)\n",
+    "fig.add_axes(ax)\n",
+    "x = np.arange(-2, 2, 0.1)\n",
+    "y = np.arange(-2, 2, 0.1)\n",
+    "x, y = np.meshgrid(x, y)\n",
+    "z = (1 - y ** 5 + x ** 5) * np.exp(-x ** 2 - y ** 2)\n",
+    "ax.plot_surface(x, y, z)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6dc5a3e1-d5a0-4f83-b8a6-7f69015a1ba9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f1235e1-f300-4ae2-ad7a-b97523bedfa1",
+   "metadata": {},
+   "source": [
+    "### seaborn\n",
+    "\n",
+    "对matplotlib进行了封装，定制了默认的样式，简化了调用matplotlib函数时需要传入的参数。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25a64a4e-bacc-43da-ad92-ec7ae2191b38",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa94138e-a642-45d5-957e-b094bdabed91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "# 设置使用默认主题（样式、配色方案、字体方案、……）\n",
+    "sns.set_theme()\n",
+    "# sns.set_theme(font_scale=1.2, style='darkgrid', palette='Dark2')\n",
+    "\n",
+    "plt.rcParams['font.sans-serif'].insert(0, 'SimHei')\n",
+    "plt.rcParams['axes.unicode_minus'] = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dabdf3ae-f8dc-42ad-a929-909b0325ca3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import ssl\n",
+    "\n",
+    "# ssl._create_default_https_context = ssl._create_unverified_context"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ddf6e866-1115-404d-b548-a6070e4e065e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tips_df = sns.load_dataset('tips')\n",
+    "# tips_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "500326a6-ff54-41e0-8c93-8307107ce52d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tips_df = pd.read_excel('res/tips.xlsx')\n",
+    "tips_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de1ae073-3fe9-45a1-9db8-21ed75de45ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tips_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5028d1d5-7d7e-4e34-a17b-80bae21f6324",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.histplot(data=tips_df, x='total_bill')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11b422dd-68ff-4881-9164-2343b7be0375",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制直方图\n",
+    "# kde - kernel density estimation - 拟合概率密度曲线\n",
+    "sns.histplot(data=tips_df, x='total_bill', kde=True, stat='density')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b05e0b86-5296-4f05-a64c-34395e9f565a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tips_df[['total_bill', 'tip']].corr(method='pearson')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8eb7d2be-9981-4750-999f-2335824ff619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制点对图\n",
+    "# sns.pairplot(data=tips_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c9f97b00-ecab-4602-8a9a-16cb30db687e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tips_df.query('sex == \"Female\"')[['total_bill', 'tip']].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8d8e63c7-97b6-41b4-bf97-10fc1f14617a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# tips_df.query('sex == \"Male\"')[['total_bill', 'tip']].corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17ac77e3-adac-4ac9-8e7a-f911b014b598",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.color_palette('tab10')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d31d89fd-f0c3-4698-8a2f-85dfb7e852ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pairplot(data=tips_df, hue='sex', palette='tab10')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1d78fd7-64d7-4c37-a738-379ca63ab7ac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.color_palette('winter')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5097260c-30e6-48b7-997c-a2b815b3eb0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pairplot(data=tips_df, hue='sex', palette='winter')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "145a9efe-30b9-447d-bcf6-c74c2233b572",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制联合分布图\n",
+    "sns.jointplot(data=tips_df, x='total_bill', y='tip', hue='sex')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bdcb5a1a-d27f-41d6-9d91-97fad47c31d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制联合分布图\n",
+    "sns.jointplot(data=tips_df, x='total_bill', y='tip', hue='smoker')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f83e9d23-93b3-4d1b-81cd-d7b89613aefa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制联合分布图\n",
+    "sns.jointplot(data=tips_df, x='total_bill', y='tip', hue='time')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bfc55394-74e6-45d6-9a7a-68d26221a8f2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制线性回归模型图\n",
+    "# lm - linear regression model\n",
+    "sns.lmplot(data=tips_df, x='total_bill', y='tip', hue='smoker')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef565a16-3847-466b-a22f-9ecc42265c31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制箱线图\n",
+    "sns.boxplot(data=tips_df, x='day', y='total_bill')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a36af19-38af-4f53-9c9a-f0a6ef8f02d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 绘制小提琴图\n",
+    "sns.violinplot(data=tips_df, x='day', y='total_bill')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27067b07-bf54-4905-a7a3-0fc9f91885f8",
+   "metadata": {},
+   "source": [
+    "### pyecharts\n",
+    "\n",
+    "对Apache的echarts库用Python语言进行了封装，可以绘制美观且交互性极佳的统计图表。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afecad24-897c-4729-9034-640f4d38ef7e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U pyecharts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fab295e-0e60-4fd4-9852-77f23bea19fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 配置Notebook的类型是JupyterLab\n",
+    "from pyecharts.globals import CurrentConfig, NotebookType\n",
+    "\n",
+    "CurrentConfig.NOTEBOOK_TYPE = NotebookType.JUPYTER_LAB"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1e12a132-50d5-40a9-9b3f-e67b224ef7cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyecharts.charts import Bar\n",
+    "\n",
+    "a = np.random.randint(10, 50, 6)\n",
+    "b = np.random.randint(20, 40, 6)\n",
+    "\n",
+    "bar = Bar()\n",
+    "bar.add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
+    "bar.add_yaxis(\"商家A\", a.tolist())\n",
+    "bar.add_yaxis(\"商家B\", b.tolist())\n",
+    "bar.load_javascript()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "baaaab27-4cc2-4584-904f-9e7bc6181d95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bar.render_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "617bd760-447e-4b6f-b6a4-41dc126be5d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyecharts.charts import Bar\n",
+    "from pyecharts import options as opts\n",
+    "from pyecharts.globals import ThemeType\n",
+    "\n",
+    "# 创建柱状图对象\n",
+    "bar = Bar(init_opts=opts.InitOpts(width='640px', height='480px', theme=ThemeType.LIGHT))\n",
+    "# 修改全局配置项\n",
+    "bar.set_global_opts(\n",
+    "    # 定制标题\n",
+    "    title_opts=opts.TitleOpts(\n",
+    "        title='2022年各品类销售额',\n",
+    "        title_link='http://www.qfedu.com',\n",
+    "        pos_left='center'\n",
+    "    ),\n",
+    "    # 定制图例\n",
+    "    legend_opts=opts.LegendOpts(\n",
+    "        is_show=True,\n",
+    "        pos_top='bottom'\n",
+    "    ),\n",
+    "    # 定制工具箱\n",
+    "    toolbox_opts=opts.ToolboxOpts(\n",
+    "        is_show=True,\n",
+    "        pos_left='right',\n",
+    "        pos_top='center',\n",
+    "        orient='vertical'\n",
+    "    )\n",
+    ")\n",
+    "# 添加横轴的数据\n",
+    "bar.add_xaxis([\"衬衫\", \"羊毛衫\", \"雪纺衫\", \"裤子\", \"高跟鞋\", \"袜子\"])\n",
+    "# 添加纵轴的数据\n",
+    "bar.add_yaxis(\"商家A\", [5, 20, 36, 10, 45, 20])\n",
+    "bar.add_yaxis(\"商家B\", [15, 22, 23, 18, 37, 40])\n",
+    "# 让浏览器加载JS文件\n",
+    "bar.load_javascript()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5734c4da-1a6d-418a-a2d4-80dfb196da8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 渲染图表\n",
+    "bar.render_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ce15768-7810-4699-9795-51188525ca83",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyecharts import options as opts\n",
+    "from pyecharts.charts import Funnel\n",
+    "\n",
+    "steps = ['曝光', '点击', '加购', '下单', '支付']\n",
+    "vdata = [10000, 5000, 2000, 1200, 880]\n",
+    "\n",
+    "f = Funnel()\n",
+    "f.add('转化', [(step, data) for step, data in zip(steps, vdata)])\n",
+    "f.set_global_opts(\n",
+    "    title_opts=opts.TitleOpts(\n",
+    "        title='转化漏斗',\n",
+    "        pos_left='10%',\n",
+    "        title_textstyle_opts=opts.TextStyleOpts(\n",
+    "            font_family='微软雅黑',\n",
+    "            font_size=28\n",
+    "        )\n",
+    "    )\n",
+    ")\n",
+    "f.load_javascript()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78f6113c-ad73-4a25-a20b-b88b0b8fbd58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f.render_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2add1c4-9ab9-493e-b312-a972795c6f3c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 注意：pyecharts库只接受原生Python数据类型（不支持numpy的数组，pandas的Series或DataFrame）\n",
+    "import pyecharts.options as opts\n",
+    "from pyecharts.charts import Pie\n",
+    "\n",
+    "x_data = [\"直接访问\", \"邮件营销\", \"联盟广告\", \"视频广告\", \"搜索引擎\"]\n",
+    "y_data = [335, 310, 234, 135, 1548]\n",
+    "\n",
+    "pie_chart = Pie(init_opts=opts.InitOpts(\n",
+    "    width='640px',\n",
+    "    height='480px'\n",
+    "))\n",
+    "pie_chart.add(\n",
+    "    series_name=\"访问来源\",\n",
+    "    data_pair=[list(z) for z in zip(x_data, y_data)],\n",
+    "    radius=[\"50%\", \"70%\"],\n",
+    "    label_opts=opts.LabelOpts(is_show=False, position=\"center\"),\n",
+    ")\n",
+    "pie_chart.set_global_opts(legend_opts=opts.LegendOpts(pos_left=\"legft\", orient=\"vertical\"))\n",
+    "pie_chart.set_series_opts(\n",
+    "    label_opts=opts.LabelOpts(formatter=\"{b}: {d}%\")\n",
+    ")\n",
+    "pie_chart.load_javascript()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b478655-7774-43ad-a0ca-1fd7082d5a4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pie_chart.render_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be870fdd-71c9-4b98-82bf-dc5f7c81503a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baidu = pd.read_excel('res/2022年股票数据.xlsx', sheet_name='BIDU', index_col='Date')\n",
+    "baidu = baidu[::-1][['Open', 'Close', 'Low', 'High']].round(2)\n",
+    "baidu"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e5eec52d-7ba1-4c30-8fcf-582971528ac1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyecharts import options as opts\n",
+    "from pyecharts.charts import Kline\n",
+    "\n",
+    "x_data = baidu.index.strftime('%m月%d日').values.tolist()\n",
+    "y_data = baidu.values.tolist()\n",
+    "\n",
+    "kline_chart = Kline()\n",
+    "kline_chart.add_xaxis(x_data)\n",
+    "kline_chart.add_yaxis('', y_data)\n",
+    "kline_chart.set_global_opts(\n",
+    "    xaxis_opts=opts.AxisOpts(is_scale=True),\n",
+    "    yaxis_opts=opts.AxisOpts(\n",
+    "        is_scale=True,\n",
+    "        splitarea_opts=opts.SplitAreaOpts(\n",
+    "            is_show=True,\n",
+    "            areastyle_opts=opts.AreaStyleOpts(opacity=1)\n",
+    "        ),\n",
+    "    ),\n",
+    "    datazoom_opts=[\n",
+    "        opts.DataZoomOpts(\n",
+    "            pos_bottom='2%',\n",
+    "            range_start=40,\n",
+    "            range_end=60,\n",
+    "        )\n",
+    "    ],\n",
+    ")\n",
+    "kline_chart.load_javascript()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26dfb364-9edd-4a0a-8ac1-4b7146971e44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kline_chart.render_notebook()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ce7fde5-2f11-48f3-a4fc-2d9f3303a13b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 安装地图数据\n",
+    "# %pip uninstall -y echarts-countries-pypkg\n",
+    "# %pip uninstall -y echarts-china-provinces-pypkg\n",
+    "# %pip uninstall -y echarts-china-cities-pypkg\n",
+    "# %pip uninstall -y echarts-china-counties-pypkg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97d8b5ca-6c66-4500-a74a-8fa521a51549",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyecharts import options as opts\n",
+    "from pyecharts.charts import Map\n",
+    "\n",
+    "data = [\n",
+    "    ('广东省', 594), ('浙江省', 438), ('四川省', 316), ('北京市', 269), ('山东省', 248),\n",
+    "    ('江苏省', 234), ('湖南省', 196), ('福建省', 166), ('河南省', 153), ('辽宁省', 152),\n",
+    "    ('上海市', 138), ('河北省', 86), ('安徽省', 79), ('湖北省', 75), ('黑龙江省', 70), \n",
+    "    ('陕西省', 63), ('吉林省', 59), ('江西省', 56), ('重庆市', 46), ('贵州省', 39),\n",
+    "    ('山西省', 37), ('云南省', 33), ('广西壮族自治区', 28), ('天津市', 22), ('新疆维吾尔自治区', 24),\n",
+    "    ('海南省', 18), ('台湾省', 11), ('甘肃省', 7), ('青海省', 3), ('内蒙古自治区', 17), \n",
+    "    ('宁夏回族自治区', 1), ('西藏自治区', 1), ('香港特别行政区', 12), ('澳门特别行政区', 2)\n",
+    "]\n",
+    "\n",
+    "map_chart = Map(init_opts=opts.InitOpts(width='1000px', height='1000px'))\n",
+    "map_chart.add('', data, 'china', is_roam=False)\n",
+    "map_chart.load_javascript()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cb71a3f2-f08b-435a-ad75-a4b6a6be6f91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "map_chart.render_notebook()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Day66-80/res/contents_of_data_analysis.png b/Day66-80/res/contents_of_data_analysis.png
new file mode 100644
index 000000000..3599354d5
Binary files /dev/null and b/Day66-80/res/contents_of_data_analysis.png differ
diff --git "a/Day81-90/82.k\346\234\200\350\277\221\351\202\273\347\256\227\346\263\225.md" "b/Day81-90/82.k\346\234\200\350\277\221\351\202\273\347\256\227\346\263\225.md"
index a8c6134be..4e865b669 100644
--- "a/Day81-90/82.k\346\234\200\350\277\221\351\202\273\347\256\227\346\263\225.md"
+++ "b/Day81-90/82.k\346\234\200\350\277\221\351\202\273\347\256\227\346\263\225.md"
@@ -36,7 +36,7 @@ $$
 
 ### 数据集介绍
 
-接下来为大家隆重介绍一下我们后续会使用到的一个重要的数据集——鸢尾花数据集（iris dataset）。鸢尾花数据集是机器学习领域中最著名、最经典的数据集之一，由英国统计学家 *Ronald A. Fisher* 于 1936 年在他的论文*《The Use of Multiple Measurements in Taxonomic Problems》*中首次引入，被广泛用于机器学习算法的入门和实验。
+接下来为大家隆重介绍一下我们后续会使用到的一个重要的数据集——鸢尾花数据集（iris dataset）。鸢尾花数据集是机器学习领域中最著名、最经典的数据集之一，由植物学家 *Edgar S. Anderson* 在加拿大魁北克加斯帕半岛采集，由英国统计学家 *Ronald A. Fisher* 于 1936 年在他的论文*《The Use of Multiple Measurements in Taxonomic Problems》*中首次引入，被广泛用于机器学习算法的入门和实验。
 
 <img src="res/02_iris_dataset.png" style="zoom:38%;">
 
diff --git "a/Day81-90/88.\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213.md" "b/Day81-90/88.\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213.md"
index 545b3ca2c..7a199eea3 100755
--- "a/Day81-90/88.\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213.md"
+++ "b/Day81-90/88.\347\245\236\347\273\217\347\275\221\347\273\234\346\250\241\345\236\213.md"
@@ -148,7 +148,7 @@ weighted avg       0.45      0.37      0.23        30
 
 > **注意**：由于创建`MLPClassifier`时没有指定`random_state`参数，所以代码每次执行的结果可能并不相同。
 
-模型的预测准确率只有`0.37`，大家对这个结果是不是感觉到非常失望，我们煞费苦心构建的模型预测效果竟然如此拉胯。别紧张，上面代码中我们创建神经网络模型时，`hidden_layer_sizes`参数设置的是`(1, )`，它表示我们的网络只有 1 个隐藏层，而且隐藏层只有 1 个神经元。我们只要增加隐藏层和神经元的数量，让模型可以更好的学习特征和目标之间的映射关系，预测的效果就会好起来。下面，我们将`hidden_layer_sizes`参数调整为`(32, 32, 32)`，即模型有三个隐藏层，每层有 32 个神经元，再来看看代码执行的结果。
+模型的预测准确率只有`0.37`，大家对这个结果是不是感觉到非常失望，我们煞费苦心构建的模型预测效果竟然如此拉胯。别紧张，上面代码中我们创建神经网络模型时，`hidden_layer_sizes`参数设置的是`(1, )`，它表示我们的网络只有 1 个隐藏层，而且隐藏层只有 1 个神经元，这个神经元承受了太多（它真的，我哭死）。接下俩，我们需要增加隐藏层和神经元的数量，让模型可以更好的学习特征和目标之间的映射关系，这样预测的效果就会好起来。下面，我们将`hidden_layer_sizes`参数调整为`(32, 32, 32)`，即模型有三个隐藏层，每层有 32 个神经元，再来看看代码执行的结果。
 
 ```python
 from sklearn.datasets import load_iris
@@ -176,7 +176,7 @@ y_pred = model.predict(X_test)
 print(classification_report(y_test, y_pred))
 ```
 
-> **说明**：大家可以试着运行上面的代码，看看有没有获得更好的结果。当然，大家也可以重新设置`hidden_layer_sizes`参数，看看会得到怎样的结果。
+> **说明**：大家可以试着运行上面的代码，看看有没有获得更好的结果。当然，模型准确率为 1 也未必就值得高兴，因为你可能训练了一个过拟合的模型。无论如何，大家可以试着重新设置`hidden_layer_sizes`参数，看看会得到怎样的结果。
 
 下面，我们还是对`MLPClassifier`几个比较重要的超参数做一个说明。
 
@@ -396,7 +396,7 @@ Test MSE: 8.7226
 Test R2: 0.8569
 ```
 
-通过上面的输出可以看到，随着神经网络模型不断的前向传播和反向传播，损失变得越来越小，模型的拟合变得越来越好。在预测的时候，我们利用训练得到的模型参数进行一次正向传播，就完成了从特征到目标值的映射，模型评估的两个指标看起来还不错。目前，神经网络被广泛应用于模式识别、图像处理、语音识别等领域，是深度学习中最核心的技术。对深度学习有兴趣的读者，可以关注我的另一个项目[“深度学习就是大力出奇迹”](https://github.com/jackfrued/Deep-Learning-Is-Nothing)，目前该项目仍然在创造更新中。
+通过上面的输出可以看到，随着神经网络模型不断的前向传播和反向传播，损失变得越来越小，模型的拟合变得越来越好。在预测的时候，我们利用训练得到的模型参数进行一次正向传播，就完成了从特征到目标值的映射，评估回归模型的两个指标 MSE 和 $\small{R^{2}}$ 看起来还不错哟。目前，神经网络被广泛应用于模式识别、图像处理、语音识别等领域，是深度学习中最核心的技术。对深度学习有兴趣的读者，可以关注我的另一个项目[“深度学习就是大力出奇迹”](https://github.com/jackfrued/Deep-Learning-Is-Nothing)，目前该项目仍然在创作更新中。
 
 ### 模型优缺点
 
diff --git "a/Day81-90/90.\346\234\272\345\231\250\345\255\246\344\271\240\345\256\236\346\210\230.md" "b/Day81-90/90.\346\234\272\345\231\250\345\255\246\344\271\240\345\256\236\346\210\230.md"
index 0746d1394..36c0d143c 100755
--- "a/Day81-90/90.\346\234\272\345\231\250\345\255\246\344\271\240\345\256\236\346\210\230.md"
+++ "b/Day81-90/90.\346\234\272\345\231\250\345\255\246\344\271\240\345\256\236\346\210\230.md"
@@ -379,9 +379,9 @@ test['FamilySize'] = test['SibSp'] + test['Parch'] + 1
 # 删除多余特征
 test.drop(columns=['Name', 'Ticket', 'SibSp', 'Parch'], inplace=True)
 
-# 逻辑回归模型
+# 使用逻辑回归模型
 passenger_id, X_test = test.index, test
-# XGBoost模型
+# 使用XGBoost模型
 # passenger_id, X_test = test.index, xgb.DMatrix(test)
 
 y_test_pred = model.predict(X_test)
diff --git "a/Python\345\255\246\344\271\240\350\265\204\346\272\220\346\261\207\346\200\273.md" "b/Python\345\255\246\344\271\240\350\265\204\346\272\220\346\261\207\346\200\273.md"
new file mode 100755
index 000000000..f913353a8
--- /dev/null
+++ "b/Python\345\255\246\344\271\240\350\265\204\346\272\220\346\261\207\346\200\273.md"
@@ -0,0 +1,108 @@
+## Python学习资源汇总
+
+最近有很多小伙伴在找 Python 的相关学习资源，给大家做一个汇总吧，大家就不需要到处打听了，而且网上的资源良莠不齐，给大家整理一些优质的资源，让大家少走弯路。温馨提示一下，下面的资源选一些适合自己的就行了，并非每个都值得学习和研究。
+
+### Python学习教程
+
+#### 图文教程
+
+1. [《从零开始学Python》](https://www.zhihu.com/column/c_1216656665569013760)- 我自己在知乎创作的专栏，欢迎大家打卡学习
+2. [《基于Python的数据分析》](https://www.zhihu.com/column/c_1217746527315496960)- 我自己在知乎创作的专栏，欢迎大家学习交流
+3. [《说走就走的AI之旅》](https://www.zhihu.com/column/c_1628900668109946880)- 我自己在知乎创作的专栏，欢迎大家学习交流
+4. [《Python - 100天从新手到大师》](https://github.com/jackfrued/Python-100-Days) - 我自己在 GitHub 分享的 Python 学习项目
+5. [《Python 3教程》](https://www.runoob.com/python3/python3-tutorial.html)- 菜鸟教程上的 Python 课程，上面还有很多其他学习资源
+6. [《Python教程》](https://liaoxuefeng.com/books/python/introduction/index.html)- 廖雪峰个人网站上的 Python 课程，上面还有一些其他学习资源
+
+#### 视频教程
+
+1. [《从零开始学Python》](https://space.bilibili.com/1177252794/lists/1222205)- 我自己分享在 Bilibili 的 Python 入门视频
+2. [《快速上手Python语言》](https://www.zhihu.com/education/video-course/1491848366791700480)- 在知乎知学堂上传的一套之前讲课的随堂视频
+3. [《Python进阶课程》](https://space.bilibili.com/1177252794/lists/4128173)- 我自己分享在 Bilibili 的 Python 进阶随堂视频
+4. [《Python数据分析三剑客》](https://space.bilibili.com/1177252794/lists/502289)- 我自己分享在 Bilibili 的 Python 数据分析随堂视频
+5. [《AI Python for Beginners》](https://www.deeplearning.ai/short-courses/ai-python-for-beginners/)- 吴恩达（Andrew Ng）老师的 Python 入门课
+6. [《AI for Everyone》](https://www.deeplearning.ai/courses/ai-for-everyone/)- 吴恩达（Andrew Ng）老师的 AI 通识课
+7. [《Deep Learning Specilizaiton》](https://www.deeplearning.ai/courses/deep-learning-specialization/)- 吴恩达（Andrew Ng）老师的深度学习专项课程
+8. [《100 Days of Code: The Complete Python Pro Bootcamp》](https://www.udemy.com/course/100-days-of-code/) - Udemy 上很受欢迎的一整套 Python 课程（付费）
+9. [《Python for Data Science and Machine Learning Bootcamp》](https://www.udemy.com/course/python-for-data-science-and-machine-learning-bootcamp/) - Udemy 上一套评分很高的数据科学课程（付费）
+10. [《PyTorch: Deep Learning and Artificial Intelligence》](https://www.udemy.com/course/pytorch-deep-learning/) - Udemy 好评课程（付费）
+
+> **说明**：吴恩达老师的课程在 YouTube 和 Bilibili 上也有很多人分享，YouTube 上面也有很多免费的 Python 课程曾经让我觉得受益匪浅。这些课程很多都是言简意赅、直击问题的，不像国内很多培训机构，动不动就分享七百集的课程或者八百G的学习资料，让很多人误以为点赞收藏就是学会。国内外各种学习平台也很多，有人喜欢 Udemy，有人喜欢 Coursera，我只是把我自己看过觉得不错的课程分享出来，大家可以根据需要自己去对应的平台查找，当然更重要的是有计划的学习起来。
+
+#### 资源网站
+
+1. [Python 官方网站](https://python.org) - 下载 Python 解释器、查看官方文档、了解社区动态等
+2. [Online Python](<https://www.online-python.com/)、[Python-Fiddle](https://python-fiddle.com/)、[Google Colab](https://colab.research.google.com/) - 在线编写运行 Python 代码
+3. [Coddy](https://coddy.tech) - 在线学习和练习 Python，有点像学外语的“多邻国”
+4. [Django](https://www.djangoproject.com/)、[FastAPI](https://fastapi.tiangolo.com/) 官方网站 - Python 服务接口开发框架
+5. [NumPy](https://numpy.org/)、[Pandas](https://pandas.pydata.org/)、[Matplotlib](https://matplotlib.org/stable/) 官方网站 - Python 数据分析三大神器
+6. [Polars](https://pola.rs/) 官方网站 - pandas 的高性能替代方案
+7. [CuPy](https://cupy.dev/)、[cuDF](https://github.com/rapidsai/cudf)、[RAPIDS](https://rapids.ai/) 官方网站 - 用 GPU 加速数学科学
+8. [Scikit-learn](https://scikit-learn.org/stable/)、[PyTorch](https://pytorch.org/)、[Tensorflow](https://www.tensorflow.org/)、[Keras](https://keras.io/)官方网站 - 机器学习、深度学习框架
+9. [Hugging Face](https://huggingface.co/) 官方网站 - transformers 库提供了大量的预训练模型，助力深度学习模型开发
+10. [Kaggle](https://www.kaggle.com/) 官方网站 - 全球知名的数据科学和机器学习平台
+11. [GitHub](https://github.com/) 官方网站 - 全球最大代码托管平台，上面有很多优质的代码和资源
+12. [YouTube](https://www.youtube.com/) 网站 - 全球最大的视频分享平台，有很多很棒的学习视频
+13. [Bilibili](https://www.bilibili.com/) 网站 - 原本是一个二次元平台，现在也是年轻人的学习平台
+14. [中国大学 MOOC](https://www.icourse163.org/)、[网易云课堂](https://study.163.com/)、[慕课网](https://www.imooc.com/)、[Udemy]()、[Coursera]()、[Udacity]()、[edX](https://www.edx.org/) - 在线学习平台
+15. [DeepLearning.ai](https://www.deeplearning.ai/) - 吴恩达（Andrew Ng）老师创办的深度学习教育平台
+16. [力扣](https://leetcode.cn/)、[牛客网](https://www.nowcoder.com/)、[HackerRank](https://www.hackerrank.com/)、[topcoder](https://www.topcoder.com/) - 在线刷题、比赛平台
+17. [NVIDIA深度学习培训中心](https://link.zhihu.com/?target=https%3A//www.nvidia.cn/training/)、[DataCamp](https://link.zhihu.com/?target=https%3A//www.datacamp.com/) - 有免费和付费的学习资源
+18. [BecomingHuman.ai](https://link.zhihu.com/?target=https%3A//becominghuman.ai/) - 上面有很多关于 AI 的话题，有一些精美的 Cheat Sheet（知识速查表）
+
+
+### Python参考书籍
+
+#### 入门读物
+
+1. 《Python编程从入门到实践》（*Python Crash Course*）- 著名的蟒蛇书，推荐
+2. 《Python基础教程》（*Beginning Python From Novice to Professional*）- 入门经典
+3. 《Python学习手册》（*Learning Python*）- 著名的老鼠书
+4. 《Python编程导论》（*Introduction to computation and programming using Python*）- 我自己很喜欢这本书
+
+#### 进阶读物
+
+1. 《Python Cookbook》- 照着菜谱做，肯定不出错，进阶首选
+2. 《流畅的Python》（*Fluent Python*）- 成为高手的必经之路
+3. 《Effective Python：编写高质量Python代码的59个有效方法》（*Effective Python 59 Specific Ways to Write Better Python*）
+4. 《Python高级编程》（*Expert Python Programming*）- 著名的花书
+5. 《Python项目开发实战》- 有很多工程化项目开发的知识
+
+#### 数据采集
+
+1. 《Python 3网络爬虫开发实战》- 个人推荐
+2. 《Python网络书籍擦剂》- 著名的穿山甲书
+
+#### 数据分析
+
+1. 《利用Python进行数据分析》（*Python for Data Analysis*）- pandas 作者写的书
+2. 《Python数据科学手册》（*Python Data Science Handbook*）- O'Reilly 动物数系列的鬣蜥书
+3. 《Python数据分析实战》（*Python Data Analytics With Pandas, NumPy, and Matplotlib*）- 个人推荐
+4. 《Python数据可视化编程实战》（*Python Data Visualization Cookbook*）- 重点在数据可视化
+5. 《深入浅出数据科学》（*Principles of Data Science*）- 我自己很喜欢这本书
+6. 《面向数据科学家的实用统计学》（*Practical Statistics for Data Scientists*）- 很实用的统计学
+7. 《数据科学入门》（*Data Science from Scratch*）
+
+#### 机器学习
+
+1. 《机器学习实战》（*Machine Learning in Aciton*）- 入门推荐
+2. 《机器学习（鸢尾花数学大系：从加减乘除到机器学习）》- 姜伟生博士非常用心的作品
+3. 《深度学习入门：基于Python的理论与实现》- 日本获奖书籍，入门推荐
+4. 《深度学习》（*Deep Learning*）- 深度学习经典神作
+5. 《动手学深度学习》（*Dive into Deep Learning*）- 阿斯顿·张和李沐强强联合出品
+6. 《Python深度学习》（*Deep Learning with Python*）- 我自己很喜欢这本书
+7. 《机器学习实战》（*Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow*）- 著名的蜥蜴书，从机器学习到深度学习
+
+#### 大模型
+
+1. 《BERT基础教程：Transformer大模型实战》（*Getting Started with Google BERT*）- 五星好评书籍
+
+2. 《Build a Large Language Model》
+3. 《Hands-On Large Language Models》
+4. 《大模型应用开发极简入门：基于GPT-4和ChatGPT》（*Developing Apps with GPT-4 and ChatGPT*）
+
+#### 测试运维
+
+1. 《Selenium自动化测试：基于Python语言》（*Learning Selenium Testing Tools with Python*）
+2. 《pytest测试实战》（*Python Testing with pytest*）
+3. 《Python自动化运维实战》（*Hands-On Enterprise Automation with Python*）
+4. 《Python自动化运维技术与最佳实践》
\ No newline at end of file