ipynb/read_and_write_data1/jun_anl_read_data1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **16 Чтение и запись данных. Часть 1 (обновление от 02.07.2020)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Курс ведёт **Андрей Мещеряков** Data Scientist в EPAM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **16.1** *Что такое данные и какие они бывают*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### План модуля\n",
    "\n",
    "* Что такое данные\n",
    "* Чтение и запись данных в формате CSV\n",
    "* Форматирование дат\n",
    "* Чтение и запись данных в формате XLSX\n",
    "* Домашнее задание"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Что такое данные\n",
    "\n",
    "**Данные** - это цифровой след деятельности человека или машины<br>\n",
    "Данные бывают **структурированные** и **неструктурированные**<br>\n",
    "**Неструктурированные данные** - это данные в своей исходной форме. Большинство существующих данных является неструктурированным. Это данные в исходной форме: текст, книга, письмо и т.д.<br>\n",
    "**Структурированные данные** - это данные с чётко заданной структурой (**схемой**). Наилучший пример: это БД или таблицы, в которых столбцы как раз и задают жёсткую структуру данных.<br>\n",
    "Структурированные данные легко собирать, анализировать и хранить, в то время как неструктурированные данные - не организованы и требуют дополнительной проработки.<br>\n",
    "Мы, люди, обрабатываем данные неструктурированно. Компьютеры же обрабатывают данные структурированно. Анализировать неструктурированные данные с помощью компьютеров - гораздо сложнее."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Свойства данных\n",
    "\n",
    "Возможность перехода данных от неструктурированных к структурированным.<br>\n",
    "Пример: письмо из рассылки - это неструктурированные данные, но мы можем структурировать их.<br>\n",
    "Сохраним каждую выделенную часть письма (дата, тема, адрес, текст и т.д.) в отдельную ячейку электронной таблицы. Т.о. мы можем обработать все письма в нашем ящике и проанализировать данные.<br>\n",
    "Письма от какого отправителя приходят чаще всего? Или в какое время мы чаще получаем письма? такую структурированную информацию анализировать гораздо легче.<br>\n",
    "Любую неструктурированную информацию можно превратить в структурированную. Однако, дата-саентисты чаще сталкиваются именно с переводом из неструктурированных в структурированные данные.<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Инструменты обработки данных\n",
    "\n",
    "Для обработки почти любых структурированных данных достаточно двух инструментов:\n",
    "* **pandas**\n",
    "* **SQL**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Средства хранения данных\n",
    "\n",
    "Любые данные нужно как-то хранить и передавать. БОльшая часть всех существующих данных хранится ввиде разных файлов в памяти компьютеров и серверов.<br>\n",
    "Существует огромное количество форматов различных файлов: табличный, текстовые, видео, аудио, изображения и мн. др.<br>\n",
    "Обычно понять формат файла (и понять как его читать) можно по его расширению. Например, .xlsx - это документ в формате Excel, .doc и .docx - это Word, а .jpeg - это изображение, сжатое методом jpeg.<br>\n",
    "Чаше всего дата-саентист работает с табличными данными в форматах csv и xlsx - их мы и рассмотрим в следующих уроках."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выводы\n",
    "\n",
    "* Данные бывают структурированные и неструктурированные\n",
    "* Структурированные данные проще обрабатывать, чем неструктурированные\n",
    "* Неструктурированные данные можно привести к структурированным, и наоборот\n",
    "* Для хранения данных используются файлы разных форматов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **16.2** *Чтение файлов в формате csv*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### План урока\n",
    "\n",
    "* Что такое формат CSV\n",
    "* Почему CSV - наиболее популярный формат табличных данных\n",
    "* Как прочитать файл CSV\n",
    "* Важные особенности формата CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Что такое формат CSV\n",
    "\n",
    "**CSV**(comma-separated values; значения, разделённые запятыми) - текстовый формат, позволяющий хранить табличные данные\n",
    "В таких файлах каждая строка - это строка таблицы, а столбцы внутри каждой строки обычно отделяются друг от друга запятыми ','"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Почему CSV - наиболее популярный формат табличных данных\n",
    "\n",
    "* Лекго читается людьми\n",
    "* Содержит структурированные данные\n",
    "* Поддерживается почти всеми системами хранения данных\n",
    "\n",
    "Почему CSV оказался таким распространённым форматом? Этот формат является одновременно текстовым и табличным.<br>\n",
    "Поэтому, как текстовый - он **читается достаточно легко людьми** и может быть открыт с помощью простого блокнота если вам нужно просто взглянуть на данные.<br>\n",
    "И, как табличный формат, существенно облегчает анализ записанных в нём данных, поскольку данные в нём структурированные.<br>\n",
    "Однако, у этого формата есть и свои недостатки. Поскольку в одном файле может быть только одна таблицы со значениями, в один csv файл нельзя сразу сохранить несколько листов как в Excel.<br>\n",
    "Также, формат csv не поддерживает объединённые ячейки, форматирование ячеек, или формулы.<br>\n",
    "Поэтому, если нам нужно просто записать данные, чтобы потом проанализировать, - то csv - это наиболее удобный и простой формат данных.<br>\n",
    "Но обрабатывать их придётся с помощью чего-то ещё. Кроме того, формат csv поддерживается всеми системами хранения данных.<br>\n",
    "Если нужно, например, перенести данные из одной СУБД в другую, а затем преобразовать их в файл Excel и построить диаграммы для отчёта, то формат csv отлично подходит для этого.\n",
    "\n",
    "### Чтение файла"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
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       "      <th>AvgTemperature</th>\n",
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       "      <td>59.1</td>\n",
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       "      <th>1</th>\n",
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       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
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       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
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      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1  2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4  2012-01-05      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-les/2-1.csv')\n",
    "df.head()\n",
    "# в первой строке файла содержатся заголовки столбцов таблицы"
   ]
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       "      <td>US</td>\n",
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       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
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       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
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       "         Date Country    State        City  AvgTemperature\n",
       "0  2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1  2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4  2012-01-05      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-les/2-2.csv', sep=';') # тут используется разделитель ; вместо ,\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тут мы видим дополнительный крайний левый столбец, выделенный **жирным**. Его не было в исходном csv-файле, но тут он появился.<br>\n",
    "На самом деле, в каждом **pandas** `DataFrame` есть особый столбец, который называется ***index*** и который служит для нумерации строк в таблице.<br>\n",
    "По смыслу он похож на первичный ключ в таблице баз данных, или номера строк в Excel. Это уникальный индентификатор (**id**) создаваемый для каждой строки.<br>\n",
    "Обычно это просто числа, номера строк, но могут быть и другие уникальные значения. При загрузке из csv-файла ***index*** был создан автоматически.<br>\n",
    "Однако, мы также можем указать вручную какой столбец при загрузке датафрейма использовать как ***index***.<br>\n",
    "Для того чтобы при загрузке файла пометить какой-либо столбец как индексный, нужно передать его название или порядковый номер в параметр `index_col`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
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   "outputs": [
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       "      <th>AvgTemperature</th>\n",
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       "      <td>59.1</td>\n",
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       "      <td>43.0</td>\n",
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       "      <th>2</th>\n",
       "      <td>44302</td>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
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       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
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       "      <td>44303</td>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
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       "      <th>4</th>\n",
       "      <td>44304</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   index        Date Country    State        City  AvgTemperature\n",
       "0  44300  2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1  44301  2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2  44302  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3  44303  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4  44304  2012-01-05      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-les/2-3.csv')\n",
    "df.head()\n",
    "# тут столбец index уже выделен за нас"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
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       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44301</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44302</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44303</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44304</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country    State        City  AvgTemperature\n",
       "index                                                         \n",
       "44300  2012-01-01      US  Alabama  Birmingham            59.1\n",
       "44301  2012-01-02      US  Alabama  Birmingham            43.0\n",
       "44302  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "44303  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "44304  2012-01-05      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-les/2-3.csv', index_col='index') # зададим при помощи параметра index_col название столбца, котоырй будет использован как индекс\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что индекс нового датафрейма совпадает со столбцом index из предыдущей версии. Значит, мы правильно интерпретировали структуру данных.\n",
    "\n",
    "Иногда бывает так, что при загрузке файла первый столбец называется ***Unnamed*** - как в следующем примере.<br>\n",
    "Скрей всего такой файл тоже индексный, но при сохранении файла ему не задали названия.<br>\n",
    "Мы можем передать в параметр `index_col` не название, а порядковый номер столбца индекса. В нашем случае 0.<br>\n",
    "И тогда мы тоже правильно интерпретируем структуру данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1  2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4  2012-01-05      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-les/2-4.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выводы\n",
    "\n",
    "* CSV - текстовый формат, позволяющий хранить личные данные\n",
    "* CSV поддерживается почти всеми системами хранения данных\n",
    "* При чтении файла CSV важно правильно указать разделитель и название индекса"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Практика\n",
    "\n",
    "#### Задание 1\n",
    "\n",
    "Откройте файл Practice1_1.csv, загрузите его в DataFrame с помощью библиотеки pandas и выведите первые 5 строк этого файла на экран. Выведите на экран название **предпоследнего** столбца этого файла. Обратите внимание, что при загрузке файла вам может потребоваться явно указать разделитель столбцов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-12-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-12-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>77.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-12-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>79.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>80.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-12-06</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>78.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country                   State                  City  \\\n",
       "0  2012-12-02      US  Additional Territories  San Juan Puerto Rico   \n",
       "1  2012-12-03      US  Additional Territories  San Juan Puerto Rico   \n",
       "2  2012-12-04      US  Additional Territories  San Juan Puerto Rico   \n",
       "3  2012-12-05      US  Additional Territories  San Juan Puerto Rico   \n",
       "4  2012-12-06      US  Additional Territories  San Juan Puerto Rico   \n",
       "\n",
       "   AvgTemperature  \n",
       "0            79.4  \n",
       "1            77.9  \n",
       "2            79.8  \n",
       "3            80.2  \n",
       "4            78.2  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/Practice1_1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'City'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Задание 2. \n",
    "\n",
    "Откройте файл Practice1_2.csv и загрузите его в DataFrame с помощью библиотеки pandas, указав правильный разделитель столбцов и выбрав столбец-индекс."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56334</th>\n",
       "      <td>2012-12-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56335</th>\n",
       "      <td>2012-12-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>77.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56336</th>\n",
       "      <td>2012-12-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>79.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56337</th>\n",
       "      <td>2012-12-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>80.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56338</th>\n",
       "      <td>2012-12-06</td>\n",
       "      <td>US</td>\n",
       "      <td>Additional Territories</td>\n",
       "      <td>San Juan Puerto Rico</td>\n",
       "      <td>78.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country                   State                  City  \\\n",
       "56334  2012-12-02      US  Additional Territories  San Juan Puerto Rico   \n",
       "56335  2012-12-03      US  Additional Territories  San Juan Puerto Rico   \n",
       "56336  2012-12-04      US  Additional Territories  San Juan Puerto Rico   \n",
       "56337  2012-12-05      US  Additional Territories  San Juan Puerto Rico   \n",
       "56338  2012-12-06      US  Additional Territories  San Juan Puerto Rico   \n",
       "\n",
       "       AvgTemperature  \n",
       "56334            79.4  \n",
       "56335            77.9  \n",
       "56336            79.8  \n",
       "56337            80.2  \n",
       "56338            78.2  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/Practice1_2.csv', sep=';', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Задание 3\n",
    "\n",
    "Откройте файл Practice1_3.csv, загрузите его в DataFrame с помощью библиотеки pandas и выведите первые 5 строк этого файла на экран. Выведите на экран все записи, в которых значение температуры превышает 30 градусов. Обратите внимание, что вам может потребоваться явно указать разделитель столбцов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-02-12</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>29.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-02-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "1  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "2  2012-01-13      US  Alabama  Birmingham            31.6\n",
       "3  2012-02-12      US  Alabama  Birmingham            29.5\n",
       "4  2012-02-13      US  Alabama  Birmingham            34.8"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/Practice1_3.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>2012-01-03</td>\n",
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       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-02-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>32.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>30.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-02-11</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>34.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-02-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Huntsville</td>\n",
       "      <td>34.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>34.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012-02-12</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Country    State        City  AvgTemperature\n",
       "0   2012-01-03      US  Alabama  Birmingham            30.9\n",
       "1   2012-01-04      US  Alabama  Birmingham            34.7\n",
       "2   2012-01-13      US  Alabama  Birmingham            31.6\n",
       "4   2012-02-13      US  Alabama  Birmingham            34.8\n",
       "5   2012-12-27      US  Alabama  Birmingham            34.3\n",
       "6   2012-12-30      US  Alabama  Birmingham            34.5\n",
       "8   2012-01-04      US  Alabama  Huntsville            32.6\n",
       "9   2012-01-13      US  Alabama  Huntsville            30.1\n",
       "10  2012-02-11      US  Alabama  Huntsville            34.8\n",
       "12  2012-02-13      US  Alabama  Huntsville            31.5\n",
       "13  2012-12-30      US  Alabama  Huntsville            34.1\n",
       "14  2012-01-04      US  Alabama  Montgomery            34.3\n",
       "15  2012-02-12      US  Alabama  Montgomery            33.3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['AvgTemperature'] > 30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **16.3** *Запись файлов в формате csv*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этом уроке мы научимся записывать данные из файла в формате csv, чтобы данные можно было сохранять и передавать другим для их обработки\n",
    "\n",
    "### План урока\n",
    "\n",
    "* Демонстрация практического задания к предыдущему уроку\n",
    "* Сохранение данных в файл CSV\n",
    "* Указание дополнительных атрибутов при сохранении данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Сохранение DataFrame в файл CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1  2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4  2012-01-05      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-les/2-1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для хранения данных только в табличном виде лучше всего подходят файлы в формате csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./data-les/result.csv', index_label='index') # передаём параметр index_label чтобы сохранился столбец с индексами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "      <td>Joshua Harrison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Mauricio Hinton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "      <td>Scott Kaur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "      <td>Davion Moss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "      <td>Mauricio Hinton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature             Name\n",
       "0  2012-01-01      US  Alabama  Birmingham            59.1  Joshua Harrison\n",
       "1  2012-01-02      US  Alabama  Birmingham            43.0  Mauricio Hinton\n",
       "2  2012-01-03      US  Alabama  Birmingham            30.9       Scott Kaur\n",
       "3  2012-01-04      US  Alabama  Birmingham            34.7      Davion Moss\n",
       "4  2012-01-05      US  Alabama  Birmingham            44.1  Mauricio Hinton"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-les/3-1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./data-les/result1.csv', sep=';', index_label='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "      <td>Joshua Harrison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Mauricio Hinton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "      <td>Scott Kaur</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "      <td>Davion Moss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "      <td>Mauricio Hinton</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country    State        City  AvgTemperature  \\\n",
       "index                                                            \n",
       "0      2012-01-01      US  Alabama  Birmingham            59.1   \n",
       "1      2012-01-02      US  Alabama  Birmingham            43.0   \n",
       "2      2012-01-03      US  Alabama  Birmingham            30.9   \n",
       "3      2012-01-04      US  Alabama  Birmingham            34.7   \n",
       "4      2012-01-05      US  Alabama  Birmingham            44.1   \n",
       "\n",
       "                  Name  \n",
       "index                   \n",
       "0      Joshua Harrison  \n",
       "1      Mauricio Hinton  \n",
       "2           Scott Kaur  \n",
       "3          Davion Moss  \n",
       "4      Mauricio Hinton  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-les/result1.csv', sep=';', index_col='index')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Итоги\n",
    "\n",
    "* Для сохранения DataFrame в файле формата CSV используется метод `to_csv()`\n",
    "* При сохранении файла CSV можно указать разделитель и название индекса"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Практика\n",
    "\n",
    "#### Задание 1. \n",
    "\n",
    "Скачайте файл Practice2_1.csv, загрузите его в DataFrame с помощью библиотеки pandas и выведите первые 5 строк этого файла на экран. После этого сохраните этот DataFrame в файл MyPractice2_1.csv, при этом укажите в качестве разделителя символ точки с запятой `;` и задайте столбцу-индексу имя \"index\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/02/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/03/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/04/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/05/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  01/01/2012      US  Alabama  Birmingham            59.1\n",
       "1  01/02/2012      US  Alabama  Birmingham            43.0\n",
       "2  01/03/2012      US  Alabama  Birmingham            30.9\n",
       "3  01/04/2012      US  Alabama  Birmingham            34.7\n",
       "4  01/05/2012      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/Practice2_1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./data-practice/MyPractice2_1.csv', sep=';', index_label='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/02/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/03/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/04/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/05/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country    State        City  AvgTemperature\n",
       "index                                                         \n",
       "0      01/01/2012      US  Alabama  Birmingham            59.1\n",
       "1      01/02/2012      US  Alabama  Birmingham            43.0\n",
       "2      01/03/2012      US  Alabama  Birmingham            30.9\n",
       "3      01/04/2012      US  Alabama  Birmingham            34.7\n",
       "4      01/05/2012      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/MyPractice2_1.csv', sep=';', index_col='index')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TVDoNeBSa7ac"
   },
   "source": [
    "#### Задание 2. \n",
    "\n",
    "Снова выведите первые 5 строк файла Practice2_1.csv на экран. Обратите внимание на столбец `AvgTemperature`. В нем указана температура в градусах Фаренгейта. На основе столбца `AvgTemperature` cоздайте новый столбец с названием `AvgTemperature_2`, который будет содержать значения температуры, возведенные в квадрат. Затем сохраните этот DataFrame в файл MyPractice2_1.csv, используя в качестве разделителя запятую."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
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       "      <td>US</td>\n",
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       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  01/01/2012      US  Alabama  Birmingham            59.1\n",
       "1  01/02/2012      US  Alabama  Birmingham            43.0\n",
       "2  01/03/2012      US  Alabama  Birmingham            30.9\n",
       "3  01/04/2012      US  Alabama  Birmingham            34.7\n",
       "4  01/05/2012      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/Practice2_1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "      <th>AvgTemperature_2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "      <td>3492.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/02/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1849.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/03/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "      <td>954.81</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/04/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1204.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/05/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "      <td>1944.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature  AvgTemperature_2\n",
       "0  01/01/2012      US  Alabama  Birmingham            59.1           3492.81\n",
       "1  01/02/2012      US  Alabama  Birmingham            43.0           1849.00\n",
       "2  01/03/2012      US  Alabama  Birmingham            30.9            954.81\n",
       "3  01/04/2012      US  Alabama  Birmingham            34.7           1204.09\n",
       "4  01/05/2012      US  Alabama  Birmingham            44.1           1944.81"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AvgTemperature_2'] = df['AvgTemperature'] ** 2\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./data-practice/MyPractice2_1.csv', sep=',', index_label='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>3492.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/02/2012</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/03/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "      <td>954.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/04/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1204.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/05/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "      <td>1944.81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country    State        City  AvgTemperature  \\\n",
       "index                                                            \n",
       "0      01/01/2012      US  Alabama  Birmingham            59.1   \n",
       "1      01/02/2012      US  Alabama  Birmingham            43.0   \n",
       "2      01/03/2012      US  Alabama  Birmingham            30.9   \n",
       "3      01/04/2012      US  Alabama  Birmingham            34.7   \n",
       "4      01/05/2012      US  Alabama  Birmingham            44.1   \n",
       "\n",
       "       AvgTemperature_2  \n",
       "index                    \n",
       "0               3492.81  \n",
       "1               1849.00  \n",
       "2                954.81  \n",
       "3               1204.09  \n",
       "4               1944.81  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/MyPractice2_1.csv', index_col='index')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **16.4** *Форматирование дат*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На этом уроке мы разберём форматирование дат. Мы узнаем почему форматирование дат является такой существенной задачей анализа данных.<br>\n",
    "Какие форматы дат встречаются чаще всего и как с помощью **pandas** привести все даты к этому формату.\n",
    "\n",
    "### План урока\n",
    "\n",
    "* Демонстрация практического задания к предыдущему уроку\n",
    "* Важность проблемы форматирования дат\n",
    "* Самые частые форматы дат\n",
    "* Использование **pandas** для форматирования дат"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Существует огромное количество способов записи дат, которые зависят от стран, того, как там принято записывать даты<br>\n",
    "Форматы могут быть разными: 25.07.2020, 25.07.20, 25-07-2020, 2020.07.25, 07/25/2020 или 07.25.2020\n",
    "\n",
    "Проблема возникает, когда мы работаем с данными, собранными в разных частях Мира.<br>\n",
    "Предположим, мы анализируем отчёты какой-либо компании, имеющей офисы в США, Сингапуре, Дании и России.<br>\n",
    "И нам нужно объединять отчёты каждого офиса в один глобальный отчёт по всей компании.<br>\n",
    "В каждой стране будут записаны даты в своём формате - и мы не сможем просто соединить отчёты по дате, т.к. с т.з. **pandas** даты записаны совершенно по-разному.<br>\n",
    "Ситуация может ещё более усугубиться в определённые дни и месяцы. Представим что мы отрываем один из отчётов и видим дату 11/08/12 - какая это дата?<br>\n",
    "11 августа 2012 года или 8 ноября 2012? А может быть вообще 12 августа 2011 года? Сложно сказать... И такая неразбериха доставляет немало хлопот аналитикам.<br>\n",
    "Какой же есть выход из этой ситуации? Решение напрашивается само собой: это стандартизация форматов дат, т.е. приведение всех дат к одному и тому же виду.\n",
    "\n",
    "### Стандартный формат дат\n",
    "11/08/12 - ?\n",
    "Стандарт **ISO 8601:** предписывает записывать даты в следующем виде: **2012-08-11**\n",
    "\n",
    "Почему такой формат хорош? Во-первых, соблюдается естественная иерархия: год-месяц-день - от большего к меньшему,<br>\n",
    "а во-вторых, такие даты очень просто сортировать\n",
    "\n",
    "### Форматирование дат в **pandas**\n",
    "\n",
    "В библиотеке **pandas** есть особый тип данных `datetime`, позволяющих хранить в нём даты.<br>\n",
    "Преимущества использования отдельного типа данных для дат, а не строк в том, что мы сможем легко переводить даты из одного формата в другой, производить всякие вычисления над датами,<br>\n",
    "например вычесть из даты семь дней, чтобы узнать, какое было число семь дней назад.<br>\n",
    "Со строками проводить такие операции проблематично."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Преобразование строковых признаков в дату"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Здесь первый столбец, ***Date***, по умолчанию будет загружен как строка. Но можно сделать так, чтобы при чтении данных этот столбец сразу будет представлен в нужно форматею<br>\n",
    "Для этого в функции `read_csv` нужно указать два параметра: первый `parse_dates` - в нём мы передаём имена столбцов, которые должны быть интерпретированы как даты.<br>\n",
    "Pandas сможет сам найти год в дате, стоит ли он первым или последним - не важно, однако, ему будет трудно определеить где месяц, а где день. Поэтому нам нужно ему помочь.<br>\n",
    "Для этого есть второй параметр `dayfirst`. Это бинарный параметр, т.е. он принимает значения: *True* или *False*. Мы задаём этому парамерту значение *True*, если день стоит левее чем месяц (день-месяц).<br>\n",
    "Если же у нас месяц стоит левее дня (месяц-день), то в параметре нужно задать значение *False*.<br>\n",
    "Но мы пока не можем определить что находится раньше: день или месяц. Давайте выведем первые 30 строк на экран:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-01-07</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>60.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-01-08</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-01-10</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-01-11</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>56.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012-01-12</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012-01-14</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012-01-15</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012-01-16</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012-01-17</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012-01-18</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2012-01-19</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>40.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012-01-20</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2012-01-21</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>64.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012-01-22</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>54.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012-01-23</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>66.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2012-01-24</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2012-01-25</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2012-01-26</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>63.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2012-01-27</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>51.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2012-01-28</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2012-01-29</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2012-01-30</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1  2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4  2012-01-05      US  Alabama  Birmingham            44.1\n",
       "5  2012-01-06      US  Alabama  Birmingham            46.3\n",
       "6  2012-01-07      US  Alabama  Birmingham            60.6\n",
       "7  2012-01-08      US  Alabama  Birmingham            57.4\n",
       "8  2012-01-09      US  Alabama  Birmingham            59.8\n",
       "9  2012-01-10      US  Alabama  Birmingham            59.4\n",
       "10 2012-01-11      US  Alabama  Birmingham            56.4\n",
       "11 2012-01-12      US  Alabama  Birmingham            46.8\n",
       "12 2012-01-13      US  Alabama  Birmingham            31.6\n",
       "13 2012-01-14      US  Alabama  Birmingham            37.7\n",
       "14 2012-01-15      US  Alabama  Birmingham            44.0\n",
       "15 2012-01-16      US  Alabama  Birmingham            51.0\n",
       "16 2012-01-17      US  Alabama  Birmingham            59.3\n",
       "17 2012-01-18      US  Alabama  Birmingham            50.1\n",
       "18 2012-01-19      US  Alabama  Birmingham            40.8\n",
       "19 2012-01-20      US  Alabama  Birmingham            57.9\n",
       "20 2012-01-21      US  Alabama  Birmingham            64.2\n",
       "21 2012-01-22      US  Alabama  Birmingham            54.1\n",
       "22 2012-01-23      US  Alabama  Birmingham            66.2\n",
       "23 2012-01-24      US  Alabama  Birmingham            52.6\n",
       "24 2012-01-25      US  Alabama  Birmingham            57.6\n",
       "25 2012-01-26      US  Alabama  Birmingham            63.8\n",
       "26 2012-01-27      US  Alabama  Birmingham            51.7\n",
       "27 2012-01-28      US  Alabama  Birmingham            46.0\n",
       "28 2012-01-29      US  Alabama  Birmingham            40.5\n",
       "29 2012-01-30      US  Alabama  Birmingham            44.7"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/Practice2_1.csv', parse_dates=['Date'], dayfirst=False) # добавили параметры parse_dates и dayfirst\n",
    "df.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь **pandas** перевёл даты к стандартному формату. И мы теперь можем использовать все преимущества этого типа.<br>\n",
    "Например, давайте создадим столбец в датафрейме, в котором укажем день недели для каждой даты.<br>\n",
    "Сделать это довольно просто - нужно только вызвать специальный метод у столбца с датами `day_name`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "      <th>Day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Monday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "      <td>Tuesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "      <td>Wednesday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "      <td>Thursday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country    State        City  AvgTemperature Day_of_week\n",
       "0 2012-01-01      US  Alabama  Birmingham            59.1      Sunday\n",
       "1 2012-01-02      US  Alabama  Birmingham            43.0      Monday\n",
       "2 2012-01-03      US  Alabama  Birmingham            30.9     Tuesday\n",
       "3 2012-01-04      US  Alabama  Birmingham            34.7   Wednesday\n",
       "4 2012-01-05      US  Alabama  Birmingham            44.1    Thursday"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Day_of_week'] = df['Date'].dt.day_name()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы получили новый столбец с днями недели"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Полный список методов и полей, доступных при работе с датами, вы можете посмотреть в документации pandas: https://pandas.pydata.org/pandas-docs/stable/reference/series.html#datetimelike-properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Практика\n",
    "\n",
    "#### Задание 1.\n",
    "\n",
    "Скачайте файл Practice3_1.csv, загрузите его в DataFrame с помощью библиотеки pandas и выведите первые 5 строк этого файла на экран. Определите, какой формат дат используется в столбце `Date`. После этого загрузите файл Practice3_1.csv в DataFrame снова, использовав параметры `parse_dates` и `dayfirst`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/01/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02/01/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03/01/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04/01/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05/01/2012</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  01/01/2012      US  Alabama  Birmingham            59.1\n",
       "1  02/01/2012      US  Alabama  Birmingham            43.0\n",
       "2  03/01/2012      US  Alabama  Birmingham            30.9\n",
       "3  04/01/2012      US  Alabama  Birmingham            34.7\n",
       "4  05/01/2012      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/Practice3_1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country    State        City  AvgTemperature\n",
       "0 2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1 2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2 2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3 2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4 2012-01-05      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data-practice/Practice3_1.csv', parse_dates=['Date'], dayfirst=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "edg9eYS35ceZ"
   },
   "source": [
    "#### Задание 2. \n",
    "\n",
    "На основе столбца \"Date\" создайте новый столбец \"Month\", который будет содержать _номер месяца_ (от 1 до 12), соответствующего дате в каждой строке. Затем сохраните этот DataFrame в файл MyPractice3_1.csv, используя в качестве разделителя точку с запятой `;`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month'] = df['Date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('./data-practice/MyPractice3_1.csv', sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-01-07</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>60.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-01-08</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-01-10</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-01-11</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>56.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012-01-12</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>31.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012-01-14</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>37.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012-01-15</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012-01-16</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012-01-17</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012-01-18</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>50.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2012-01-19</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>40.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012-01-20</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2012-01-21</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>64.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012-01-22</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>54.1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012-01-23</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>66.2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2012-01-24</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>52.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2012-01-25</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2012-01-26</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>63.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2012-01-27</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>51.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2012-01-28</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2012-01-29</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>40.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2012-01-30</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2012-01-31</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>52.8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>58.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2012-02-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2012-02-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>55.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2012-02-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>60.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2012-02-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2012-02-06</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>49.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2012-02-07</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>47.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2012-02-08</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>49.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>2012-02-09</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>41.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2012-02-10</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>2012-02-11</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>38.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>2012-02-12</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>29.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2012-02-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2012-02-14</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2012-02-15</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>47.4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>2012-02-16</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>58.3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2012-02-17</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>53.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2012-02-18</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2012-02-19</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>48.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2012-02-20</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>42.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2012-02-21</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2012-02-22</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>58.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2012-02-23</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>68.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2012-02-24</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>64.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2012-02-25</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>45.2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2012-02-26</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>50.7</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2012-02-27</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>58.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2012-02-28</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>58.9</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>2012-02-29</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>65.1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature  Month\n",
       "0  2012-01-01      US  Alabama  Birmingham            59.1      1\n",
       "1  2012-01-02      US  Alabama  Birmingham            43.0      1\n",
       "2  2012-01-03      US  Alabama  Birmingham            30.9      1\n",
       "3  2012-01-04      US  Alabama  Birmingham            34.7      1\n",
       "4  2012-01-05      US  Alabama  Birmingham            44.1      1\n",
       "5  2012-01-06      US  Alabama  Birmingham            46.3      1\n",
       "6  2012-01-07      US  Alabama  Birmingham            60.6      1\n",
       "7  2012-01-08      US  Alabama  Birmingham            57.4      1\n",
       "8  2012-01-09      US  Alabama  Birmingham            59.8      1\n",
       "9  2012-01-10      US  Alabama  Birmingham            59.4      1\n",
       "10 2012-01-11      US  Alabama  Birmingham            56.4      1\n",
       "11 2012-01-12      US  Alabama  Birmingham            46.8      1\n",
       "12 2012-01-13      US  Alabama  Birmingham            31.6      1\n",
       "13 2012-01-14      US  Alabama  Birmingham            37.7      1\n",
       "14 2012-01-15      US  Alabama  Birmingham            44.0      1\n",
       "15 2012-01-16      US  Alabama  Birmingham            51.0      1\n",
       "16 2012-01-17      US  Alabama  Birmingham            59.3      1\n",
       "17 2012-01-18      US  Alabama  Birmingham            50.1      1\n",
       "18 2012-01-19      US  Alabama  Birmingham            40.8      1\n",
       "19 2012-01-20      US  Alabama  Birmingham            57.9      1\n",
       "20 2012-01-21      US  Alabama  Birmingham            64.2      1\n",
       "21 2012-01-22      US  Alabama  Birmingham            54.1      1\n",
       "22 2012-01-23      US  Alabama  Birmingham            66.2      1\n",
       "23 2012-01-24      US  Alabama  Birmingham            52.6      1\n",
       "24 2012-01-25      US  Alabama  Birmingham            57.6      1\n",
       "25 2012-01-26      US  Alabama  Birmingham            63.8      1\n",
       "26 2012-01-27      US  Alabama  Birmingham            51.7      1\n",
       "27 2012-01-28      US  Alabama  Birmingham            46.0      1\n",
       "28 2012-01-29      US  Alabama  Birmingham            40.5      1\n",
       "29 2012-01-30      US  Alabama  Birmingham            44.7      1\n",
       "30 2012-01-31      US  Alabama  Birmingham            52.8      1\n",
       "31 2012-02-01      US  Alabama  Birmingham            58.5      2\n",
       "32 2012-02-02      US  Alabama  Birmingham            59.7      2\n",
       "33 2012-02-03      US  Alabama  Birmingham            55.9      2\n",
       "34 2012-02-04      US  Alabama  Birmingham            60.7      2\n",
       "35 2012-02-05      US  Alabama  Birmingham            57.9      2\n",
       "36 2012-02-06      US  Alabama  Birmingham            49.5      2\n",
       "37 2012-02-07      US  Alabama  Birmingham            47.9      2\n",
       "38 2012-02-08      US  Alabama  Birmingham            49.8      2\n",
       "39 2012-02-09      US  Alabama  Birmingham            41.4      2\n",
       "40 2012-02-10      US  Alabama  Birmingham            43.4      2\n",
       "41 2012-02-11      US  Alabama  Birmingham            38.4      2\n",
       "42 2012-02-12      US  Alabama  Birmingham            29.5      2\n",
       "43 2012-02-13      US  Alabama  Birmingham            34.8      2\n",
       "44 2012-02-14      US  Alabama  Birmingham            43.5      2\n",
       "45 2012-02-15      US  Alabama  Birmingham            47.4      2\n",
       "46 2012-02-16      US  Alabama  Birmingham            58.3      2\n",
       "47 2012-02-17      US  Alabama  Birmingham            53.9      2\n",
       "48 2012-02-18      US  Alabama  Birmingham            54.0      2\n",
       "49 2012-02-19      US  Alabama  Birmingham            48.9      2\n",
       "50 2012-02-20      US  Alabama  Birmingham            42.9      2\n",
       "51 2012-02-21      US  Alabama  Birmingham            56.0      2\n",
       "52 2012-02-22      US  Alabama  Birmingham            58.7      2\n",
       "53 2012-02-23      US  Alabama  Birmingham            68.2      2\n",
       "54 2012-02-24      US  Alabama  Birmingham            64.0      2\n",
       "55 2012-02-25      US  Alabama  Birmingham            45.2      2\n",
       "56 2012-02-26      US  Alabama  Birmingham            50.7      2\n",
       "57 2012-02-27      US  Alabama  Birmingham            58.1      2\n",
       "58 2012-02-28      US  Alabama  Birmingham            58.9      2\n",
       "59 2012-02-29      US  Alabama  Birmingham            65.1      2"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('./data-practice/MyPractice3_1.csv', sep=';', parse_dates=['Date'], dayfirst=True, index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **16.5** *Чтение и запись файлов в формате XLSX*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этом урое мы научимся работать с файлами Excel\n",
    "\n",
    "### План урока\n",
    "\n",
    "* Демонстрация практического задания к предыдущему уроку\n",
    "* Что такое формат XLSX\n",
    "* Чем XLSX отличается от CSV\n",
    "* Как читать файлы формата XLSX\n",
    "* Как записывать файлы формата XLSX"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Что такое формат XLSX\n",
    "\n",
    "**XLSX** - бинарный формат храенения данных Excel\n",
    "\n",
    "Excel имеет гораздо больше возможностей, чем CSV!\n",
    "\n",
    "Некоторые особенности формата XLSX:\n",
    "* Несколько таблиц в одном файле\n",
    "* Форматирование и объединение ячеек\n",
    "* Формулы для автоматического вычисления значений ячеек\n",
    "\n",
    "Однако, в **pandas** эти возможности использовать нелья. Болле того, некоторые возможности, такие как, например, нескольких листов, - могут усложнить чтение данных.<br>\n",
    "Чуть позже, мы с вами научимся работать с такими данными. А пока, давайте попробуем читать данные из файлов Excel\n",
    "\n",
    "### Чтение и запись файлов формата XLSX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date Country    State        City  AvgTemperature\n",
       "0 2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1 2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2 2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3 2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4 2012-01-05      US  Alabama  Birmingham            44.1"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('./data-les/5-1.xlsx')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>score</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>cassandra tu \"Yeah, well, that's just like, u...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2014-02-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jake</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2013-03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rick Bennette \"Rick Bennette\"</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2013-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RustyBill \"Sunday Rocker\"</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2014-02-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SEAN MASLANKA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2014-02-21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               name  score       date\n",
       "0  cassandra tu \"Yeah, well, that's just like, u...    5.0 2014-02-28\n",
       "1                                              Jake    5.0 2013-03-16\n",
       "2                     Rick Bennette \"Rick Bennette\"    5.0 2013-08-28\n",
       "3                         RustyBill \"Sunday Rocker\"    5.0 2014-02-14\n",
       "4                                     SEAN MASLANKA    5.0 2014-02-21"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('./data-les/5-2.xlsx', parse_dates=['date'], dayfirst=False) # сначала загрузим в таком же режиме\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы загрузили первый лист xlsx файла. Но как же теперь переключиться на второй лист?<br>\n",
    "В этом же датафрейме это сделать невозможно. Дело в том, что датафрей **pandas** может содержать только одну таблицу.<br>\n",
    "Поэтому, чтобы работать со вторым листом, нужно прочитать данные ещё раз и загрузить их во второй датафрейм.<br>\n",
    "Ещё раз прочитаем этот файл и в вызов функции `read_excel` добавим параметр `sheet_name`. В этот параметр передадим имя второго листа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Date</th>\n",
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       "      <th>City</th>\n",
       "      <th>AvgTemperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-01-03</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-01-04</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012-01-06</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2012-01-07</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>60.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-01-08</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-01-09</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-01-10</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2012-01-11</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>56.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2012-01-12</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2012-01-14</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>37.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2012-01-15</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2012-01-16</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2012-01-17</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>59.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2012-01-18</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>50.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2012-01-19</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>40.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012-01-20</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2012-01-21</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>64.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2012-01-22</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>54.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2012-01-23</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>66.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2012-01-24</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>52.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2012-01-25</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>57.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2012-01-26</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>63.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2012-01-27</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>51.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2012-01-28</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2012-01-29</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>40.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2012-01-30</td>\n",
       "      <td>US</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>44.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Country    State        City  AvgTemperature\n",
       "0  2012-01-01      US  Alabama  Birmingham            59.1\n",
       "1  2012-01-02      US  Alabama  Birmingham            43.0\n",
       "2  2012-01-03      US  Alabama  Birmingham            30.9\n",
       "3  2012-01-04      US  Alabama  Birmingham            34.7\n",
       "4  2012-01-05      US  Alabama  Birmingham            44.1\n",
       "5  2012-01-06      US  Alabama  Birmingham            46.3\n",
       "6  2012-01-07      US  Alabama  Birmingham            60.6\n",
       "7  2012-01-08      US  Alabama  Birmingham            57.4\n",
       "8  2012-01-09      US  Alabama  Birmingham            59.8\n",
       "9  2012-01-10      US  Alabama  Birmingham            59.4\n",
       "10 2012-01-11      US  Alabama  Birmingham            56.4\n",
       "11 2012-01-12      US  Alabama  Birmingham            46.8\n",
       "12 2012-01-13      US  Alabama  Birmingham            31.6\n",
       "13 2012-01-14      US  Alabama  Birmingham            37.7\n",
       "14 2012-01-15      US  Alabama  Birmingham            44.0\n",
       "15 2012-01-16      US  Alabama  Birmingham            51.0\n",
       "16 2012-01-17      US  Alabama  Birmingham            59.3\n",
       "17 2012-01-18      US  Alabama  Birmingham            50.1\n",
       "18 2012-01-19      US  Alabama  Birmingham            40.8\n",
       "19 2012-01-20      US  Alabama  Birmingham            57.9\n",
       "20 2012-01-21      US  Alabama  Birmingham            64.2\n",
       "21 2012-01-22      US  Alabama  Birmingham            54.1\n",
       "22 2012-01-23      US  Alabama  Birmingham            66.2\n",
       "23 2012-01-24      US  Alabama  Birmingham            52.6\n",
       "24 2012-01-25      US  Alabama  Birmingham            57.6\n",
       "25 2012-01-26      US  Alabama  Birmingham            63.8\n",
       "26 2012-01-27      US  Alabama  Birmingham            51.7\n",
       "27 2012-01-28      US  Alabama  Birmingham            46.0\n",
       "28 2012-01-29      US  Alabama  Birmingham            40.5\n",
       "29 2012-01-30      US  Alabama  Birmingham            44.7"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('./data-les/5-2.xlsx', sheet_name='Sheet2', parse_dates=['Date'], dayfirst=False) # с датами работаем аналогичным образом как и с csv файлами\n",
    "df.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Отлично! Теперь научимся сохранять данные в формате Excel. Иногда это более предпочтительно, чем данные в формате csv.\n",
    "Например, когда на основе наших данных другие коллеги будут строить отчёт прямо в Excel. И им просто удобнее работать прямо в нашем файле, а не создавать новый и копировать в него данные.\n",
    "Итак, вызываем метод `to_excel`, аналогичный методу `to_csv`. В параметре метода хранится путь к сохраняемому файлу. И можно передать название для столбца индекса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel('./data-les/result.xlsx', index_label='index', sheet_name='Sheet1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelWriter('./data-les/result.xlsx',\n",
    "                    mode='a', engine='openpyxl') as writer:  \n",
    "    df.to_excel(writer, sheet_name='Sheet2') # записываем датафрейм в новый лист того же Excel файла"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выводы\n",
    "\n",
    "* Данные можно разделить на структурированные и неструктурированные\n",
    "* Формат CSV наиболее популярен и поддерживается почти всеми системами хранения данных\n",
    "* Формат XLSX представляет больше возможностей, но используется реже\n",
    "* Даты могут быть записаны в разных форматах, и при работе с ними, важно привести их к стандарту"
   ]
  }
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