Tutorial2_NumPy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d7a76d67",
   "metadata": {},
   "source": [
    "# Probability and Statistics Fall 1400\n",
    "## Python Tutorial - Part 2_NumPy\n",
    "* Prepared by : Shayan Vassef\n",
    "* Email : sh.vassef@ut.ac.ir\n",
    "\n",
    "![Python](Python-Tutorial.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13ef0bf",
   "metadata": {},
   "source": [
    "# Let's get started !"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62811974",
   "metadata": {},
   "source": [
    "\n",
    "**This notebook will just go through the following topics in order:**\n",
    "\n",
    "* NumPy\n",
    "    * Built-in Methods\n",
    "    * Random\n",
    "    * Array Attributes and Methods\n",
    "    * Indexing and Selection\n",
    "    * Broadcasting\n",
    "    * Conditional Selection\n",
    "    * Operations\n",
    "    * Functions\n",
    "* pandas\n",
    "* Matplotlib\n",
    "* Seaborn\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d8f509a",
   "metadata": {},
   "source": [
    "# NumPy "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7378f062",
   "metadata": {},
   "source": [
    "What makes Numpy so important is that almost all of the libraries in the PyData ecosystem (pandas, scipy, scikit-learn, etc.) rely on NumPy as one of their main building blocks. Plus we will use it to generate data for our analysis examples later on!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb02f11",
   "metadata": {},
   "source": [
    "## Numpy Installation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d28ff8fb",
   "metadata": {},
   "source": [
    " If you have Anaconda, install NumPy by going to your terminal or command prompt and typing:\n",
    "\n",
    "<font color='red'>conda install numpy</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b070faf",
   "metadata": {},
   "source": [
    " You can also employ the pip method to install or update your desire library:\n",
    " \n",
    "<font color='red'>pip install numpy</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30f99a0b",
   "metadata": {},
   "source": [
    "## Numpy Importing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fd1757eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2cf192",
   "metadata": {},
   "source": [
    "### <font color='green'>NumPy Arrays</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9934f398",
   "metadata": {},
   "source": [
    "**arrays and lists are easily convertible**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "31e012b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [1,2,3]\n",
    "my_array = np.array([1,2,3])\n",
    "my_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cb3112f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(my_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e8004688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_matrix = [[1,2,3],[4,5,6],[7,8,9]]\n",
    "my_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ae932c70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(my_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59fb2a6f",
   "metadata": {},
   "source": [
    "### <font color='green'>Built-in Methods</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93d8d7ed",
   "metadata": {},
   "source": [
    "### arange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62255e4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2223a073",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-5931af6822e0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "np.arange(0,10,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7a31d83",
   "metadata": {},
   "source": [
    "### zeros and ones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8d3a7d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "2e187646",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7cf104ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "56b01031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((5,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0890228",
   "metadata": {},
   "source": [
    "### linspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3bedb54e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  5., 10.])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,10,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fec5860e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  , 2.25, 2.5 ,\n",
       "       2.75, 3.  , 3.25, 3.5 , 3.75, 4.  , 4.25, 4.5 , 4.75, 5.  ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,5,21)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f50fe9e3",
   "metadata": {},
   "source": [
    "### eye"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "83f8bb85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90b2ec63",
   "metadata": {},
   "source": [
    "### <font color='green'>Random</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "195e82b0",
   "metadata": {},
   "source": [
    "### rand \n",
    "\n",
    "Creates an array of the given shape and populates it with random samples from a uniform distribution over [0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2162053d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.10284176, 0.94206904])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "79488304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.66079333, 0.16336672, 0.47545672, 0.04967573, 0.44870916],\n",
       "       [0.39969553, 0.42619501, 0.94158406, 0.94378594, 0.79318951]])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "a6ce749b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5003333566049761"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.random.rand(1,10**6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "2ed06f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08324084865386719"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(np.random.rand(1,10**6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af4199c1",
   "metadata": {},
   "source": [
    "### randn \n",
    "\n",
    "Returns a sample (or samples) from the \"standard normal\" distribution [σ = 1]. Unlike rand which is uniform, values closer to zero are more likely to appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "3591818d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.04327251, -0.94104415])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ae788a8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.95508977,  1.39559558, -1.67640166],\n",
       "       [-0.79371526,  2.33788267, -0.72581334],\n",
       "       [ 2.42903808,  0.73841752, -0.43092908]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(3,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22f15297",
   "metadata": {},
   "source": [
    "### randint \n",
    "\n",
    "Returns random integers from low (inclusive) to high (exclusive)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b58537c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "75061a0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([50, 10, 72, 38, 86, 20, 97, 13, 56, 32])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100,10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e85ea9c",
   "metadata": {},
   "source": [
    "### seed \n",
    "Can be used to set the random state, so that the same \"random\" results can be reproduced. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "4a223074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.37454012, 0.95071431, 0.73199394, 0.59865848])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "np.random.rand(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "af94e4c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.37454012, 0.95071431, 0.73199394, 0.59865848])"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "np.random.rand(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2114b3c",
   "metadata": {},
   "source": [
    "### <font color='green'>Array Attributes and Methods</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42824943",
   "metadata": {},
   "source": [
    "### Reshape \n",
    "\n",
    "Returns an array containing the same data with a new shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "883f6c31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  3,  6,  9, 12, 15, 18, 21, 24])"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr=np.arange(0,25,3)\n",
    "myarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "52b71ba5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  6],\n",
       "       [ 9, 12, 15],\n",
       "       [18, 21, 24]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr.reshape(3,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee042a8a",
   "metadata": {},
   "source": [
    "### max, min, argmax, argmin\n",
    "\n",
    "These are useful methods for finding max or min values, or to find their index locations using argmin or argmax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5595da21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "d0767f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "03cf11b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr.argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e5b22508",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr.argmin()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c435e962",
   "metadata": {},
   "source": [
    "### Shape \n",
    "\n",
    "Shape is an attribute that arrays have (not a method):  [[reference](https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.ndarray.shape.html)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "e9a48f5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9,)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one dimensional !\n",
    "myarr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "044e61fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  6,  9, 12, 15, 18, 21, 24]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Two dimensional !\n",
    "myarr=myarr.reshape(1,9)\n",
    "myarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8dd3d2f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 9)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4a4d5c2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0],\n",
       "       [ 3],\n",
       "       [ 6],\n",
       "       [ 9],\n",
       "       [12],\n",
       "       [15],\n",
       "       [18],\n",
       "       [21],\n",
       "       [24]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr.reshape(9,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d222cc3d",
   "metadata": {},
   "source": [
    "### <font color='green'>Indexing and Selection</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45b155d7",
   "metadata": {},
   "source": [
    "### 1D array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "133c114d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27, 30, 33])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr=np.arange(0,36,3)\n",
    "myarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ea7889fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "85992cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 6, 9])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Just like indexing inside a list\n",
    "myarr[1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b70a3144",
   "metadata": {},
   "source": [
    "### 2D array(Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9a8bb68f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  6,  9, 12, 15, 18, 21, 24, 27, 30, 33]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newarr=myarr.reshape((1,len(myarr)))\n",
    "newarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "92db4b82",
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 0 with size 1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-107-3d6e0e7dd146>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# So why is that ?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mnewarr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 1"
     ]
    }
   ],
   "source": [
    "# So why is that ?\n",
    "newarr[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c477414a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "# arr[row][column] or arr[row , column]\n",
    "print(newarr[0][3])\n",
    "print(newarr[0,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "17638dfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  3,  6,  9],\n",
       "       [12, 15, 18, 21],\n",
       "       [24, 27, 30, 33]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D2array=newarr.reshape((3,4))\n",
    "D2array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "99d2e54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting one item\n",
    "D2array[2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "39bd6746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 15, 18, 21],\n",
       "       [24, 27, 30, 33]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting several items\n",
    "D2array[1:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "9073c97a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 18],\n",
       "       [27, 30]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D2array[1:3,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "efe6581b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 18],\n",
       "       [24, 30]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D2array[1:3,0:3:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c36334",
   "metadata": {},
   "source": [
    "### More Indexing Help\n",
    "Indexing a 2D matrix can be a bit confusing at first, especially when you start to add in step size. Try google image searching *NumPy indexing* to find useful images, like this one:\n",
    "![numpy-indexing](numpy-indexing.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dbf3e70",
   "metadata": {},
   "source": [
    "### <font color='green'>Broadcasting</font> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c89b7706",
   "metadata": {},
   "source": [
    "**With lists, you can only reassign parts of a list with new parts of the same size and shape. That is, if you wanted to replace the first 5 elements in a list with a new value, you would have to pass in a new 5 element list. With NumPy arrays, you can broadcast a single value across a larger set of values:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "8675019c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 20, 20, 20, 20, 15, 18, 21, 24, 27, 30, 33])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myarr[0:5]=20\n",
    "myarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "26e1430d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 18, 21, 24, 27, 30, 33])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Narray=myarr[5:]\n",
    "Narray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "5c9b4e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "Narray[:]=30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d34115e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30, 30, 30, 30, 30, 30, 30])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Narray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "116e2042",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The original array also changed !!\n",
    "myarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "460beeaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#In order to cancel the changing in our original array , we should creat a copy of that :\n",
    "new_array=myarr.copy()\n",
    "new_array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d34256ff",
   "metadata": {},
   "source": [
    "### <font color='green'>Conditional Selection</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "139e9152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "3e213c5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr>5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "5038b27f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7, 8, 9])"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[arr>5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19f08485",
   "metadata": {},
   "source": [
    "### <font color='green'>Operations</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "d680952b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10, 12, 14, 16, 18])"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr+arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "f71a50e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1, -2, -3, -4, -5, -6, -7, -8, -9])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr-arr*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "85732cce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16, 25, 36, 49, 64, 81], dtype=int32)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "a87a1d6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 0.5       , 0.33333333, 0.25      , 0.2       ,\n",
       "       0.16666667, 0.14285714, 0.125     , 0.11111111])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20f5c96d",
   "metadata": {},
   "source": [
    "### <font color='green'>Functions</font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6333465f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.41421356, 1.73205081, 2.        , 2.23606798,\n",
       "       2.44948974, 2.64575131, 2.82842712, 3.        ])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "2d5c6fd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.71828183e+00, 7.38905610e+00, 2.00855369e+01, 5.45981500e+01,\n",
       "       1.48413159e+02, 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,\n",
       "       8.10308393e+03])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "e925dac3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.84147098,  0.90929743,  0.14112001, -0.7568025 , -0.95892427,\n",
       "       -0.2794155 ,  0.6569866 ,  0.98935825,  0.41211849])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sin(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "2062ee35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.69314718, 1.09861229, 1.38629436, 1.60943791,\n",
       "       1.79175947, 1.94591015, 2.07944154, 2.19722458])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "da09d25e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "f16e9788",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.666666666666667"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "a5b6ce59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.581988897471611"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "e054e2d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "f40de294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr=arr.reshape((3,3))\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "39272d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6, 15, 24])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9c10caf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 15, 18])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9efd7764",
   "metadata": {},
   "source": [
    "<img src='axis.png' width=600/>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e8457a",
   "metadata": {},
   "source": [
    "Thats enough for NumPy ! , See u in the next tutorial ;)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4a211ff",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
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