Code files uploaded

Author: ClaraJosephPackt

Exploring+data+(Exploratory+Data+Analysis)+(1).ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Context"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In reality, we don't use the `NumPy` package directly much for data analysis. But Python libraries such as `Pandas`, `Scikit-learn` are all built upon `NumPy`, so it is a foundation library.\n",
+    "\n",
+    "It is *optional* for this course to learn `NumPy`. However, if you are curious about `NumPy`, this notebook includes some very basic concepts for `NumPy`, which will give you an idea of what `NumPy` is.\n",
+    "\n",
+    "For a detailed `NumPy` tutorial, you can read **Python NumPy Tutorial: Practical Basics for Data Science** (https://www.justintodata.com/python-numpy-tutorial-basics-for-data-science/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# What is `NumPy`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`NumPy` is the fundamental library for array/scientific computing in Python. \n",
+    "\n",
+    "`NumPy` introduces powerful **n-dimensional arrays**, which are more memory efficient and faster than Python lists.\n",
+    "\n",
+    "`NumPy` makes Python easier for numerical operations, and it also provides useful linear algebra, Fourier transform, and random number capabilities."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Import `NumPy`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# `NumPy` array creation and basic attributes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The main object within `NumPy` is the multi-dimensional array (`ndarray`). It’s a table of elements (usually numbers), all the same type, indexed by a tuple of non-negative integers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below we create a multi-dimensional array from Python lists as an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = np.array([[1,2,3], [4,5,6], [7,8,9]])\n",
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let’s also look at its important attributes:\n",
+    "\n",
+    "- `ndim`: the number of axes (dimensions).\n",
+    "- `shape`: the dimensions of the array as a tuple. For a matrix with n rows and m columns, the shape will be (n,m).\n",
+    "- `size`: the total number of elements of the array, which equals the product of the elements of shape.\n",
+    "- `dtype`: the data type of the elements in the array. \n",
+    "\n",
+    "`NumPy` supports more numerical types than Python does. For instance, `NumPy` has its data types like `numpy.int32` and `numpy.float64`. For a complete list of data types in `NumPy`, take a look at the official data types document (https://numpy.org/devdocs/user/basics.types.html). \n",
+    "We'll cover more on dtypes in the course."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 3)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dtype('int32')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many of the `NumPy` array operations are similar to `Pandas`, which we'll explore throughout the course. So we won't cover them here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}