Numpy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0e6b23ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing numpy\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "244ee9ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creating array objects with numpy\n",
    "\n",
    "a = np.array([1,2,3])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3037cb43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    }
   ],
   "source": [
    "# accessing the elements\n",
    "\n",
    "print(a[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "300ee0b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We also have a Python List. What's so special about numpy array?\n",
    "\n",
    "# Benefits of usign numpy array over a python list,\n",
    "# 1. Less memory\n",
    "# 2. Fast\n",
    "# 3. Convenient "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "196f8c78",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comparing numpy with a python list\n",
    "\n",
    "import time\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8d3ed663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28000\n"
     ]
    }
   ],
   "source": [
    "l = range(1000)\n",
    "print(sys.getsizeof(5)*len(l)) # In the given code, the 5 is being used as a placeholder to represent an integer. This code attempts to calculate the total memory usage (in bytes) for a list of 1000 integers, assuming that each integer takes the same amount of memory as the integer 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "18f4ce6c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000\n",
      "4000\n"
     ]
    }
   ],
   "source": [
    "array = np.arange(1000)\n",
    "print(array.size)\n",
    "print(array.size*array.itemsize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5399c790",
   "metadata": {},
   "outputs": [],
   "source": [
    "# SO, from the above example we can see that, numpy array taking 4000 byte memory whereas, python list taking 28000 byte."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1284f4ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python list took:  2587.4154567718506\n",
      "numpy took:  317.57259368896484\n"
     ]
    }
   ],
   "source": [
    "SIZE = 10000000\n",
    "l1 = range(SIZE)\n",
    "l2 = range(SIZE)\n",
    "\n",
    "a1 = np.arange(SIZE)\n",
    "a2 = np.arange(SIZE)\n",
    "\n",
    "# python list\n",
    "start = time.time()\n",
    "result = [(x+y) for x,y in zip(l1,l2)]\n",
    "print(\"python list took: \", (time.time() - start)*1000)\n",
    "\n",
    "\n",
    "# numpy array\n",
    "start = time.time()\n",
    "result = a1 + a2\n",
    "print(\"numpy took: \", (time.time()-start)*1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "57bd34b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 7, 9])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = np.array([1,2,3])\n",
    "a2 = np.array([4,5,6])\n",
    "a1 + a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e8b4e59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 - a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cec14c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 10, 18])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1*a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ef09f49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.25, 0.4 , 0.5 ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1/a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9c846f10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numpy : basic array operations\n",
    "\n",
    "import numpy as np\n",
    "a = np.array([5,6,9])\n",
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "228f52a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2],[3,4],[5,6]])\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e523461e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim    # tells the dimensions of an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "384942b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "db803655",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "877fdc3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2],[3,4],[5,6]], dtype = np.float64)\n",
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b0574ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size # Total no. of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "96c1db04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.+0.j, 2.+0.j],\n",
       "       [3.+0.j, 4.+0.j],\n",
       "       [5.+0.j, 6.+0.j]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2],[3,4],[5,6]], dtype = complex)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "460eddb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2c738adc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "74ccd81d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9901c15b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(0, 5)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = range(5)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "14c8520e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "23f63054",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3a378bb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4ff6c749",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,7,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4bcd59c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.44444444, 1.88888889, 2.33333333, 2.77777778,\n",
       "       3.22222222, 3.66666667, 4.11111111, 4.55555556, 5.        ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1,5,10)      # It generate 10 number which is linearly spaced."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abcc3f64",
   "metadata": {},
   "source": [
    "\"Linearly spaced\" refers to a sequence of values that are equally spaced at constant intervals. In mathematics and programming, creating a linearly spaced sequence means that the difference between consecutive values is the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c1208595",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[1,2],[3,4],[5,6]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "0cfd0115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b8147b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "28443391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [5],\n",
       "       [6]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape(6,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "aa453a1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ravel()     # making the array one dimensional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a3746eb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a         # All the above operations, It doesn't going to Alter original array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b17a1de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a0ce4ea4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "111503d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ed0c08e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9, 12])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "ec7bf988",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 11])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.sum(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "51b928fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 1.41421356],\n",
       "       [1.73205081, 2.        ],\n",
       "       [2.23606798, 2.44948974]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "444bfb28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.707825127659933"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a)   # standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e42e6d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some Basic Mathematical Operations\n",
    "\n",
    "a = np.array([[1,2],[3,4]])\n",
    "b = np.array([[5,6],[7,8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "037c28c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a58b53af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 6],\n",
       "       [7, 8]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b5d3063f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6,  8],\n",
       "       [10, 12]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c914ed90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[19, 22],\n",
       "       [43, 50]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matrix multiplication\n",
    "\n",
    "a.dot(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fffc968c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5, 12],\n",
       "       [21, 32]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "a1017cac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2       , 0.33333333],\n",
       "       [0.42857143, 0.5       ]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a/b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5731f005",
   "metadata": {},
   "outputs": [],
   "source": [
    "# numpy: slicing/stacking and indexing wiht boolean arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f4dd3ca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6, 7]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing and Slicing\n",
    "\n",
    "n = [6,7,8]\n",
    "n[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "89c95ec4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n[-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "14b42461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "25367628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 7])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([6,7,8])\n",
    "a[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "5cd2edb0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4a2eae1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 7, 8],\n",
       "       [1, 2, 3],\n",
       "       [9, 3, 2]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[6,7,8],[1,2,3],[9,3,2]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "cd80c58e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e15b22f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 7, 8],\n",
       "       [1, 2, 3]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8769403e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8, 3])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:2,2]   # 2nd Element of 0th row and 1st row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b59bf252",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 3, 2])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "a80c8c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 3])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[-1,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "928a8692",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 8],\n",
       "       [2, 3],\n",
       "       [3, 2]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,1:3]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "2757780c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 7, 8],\n",
       "       [1, 2, 3],\n",
       "       [9, 3, 2]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterate through an array\n",
    "\n",
    "a = np.array([[6,7,8],[1,2,3],[9,3,2]])\n",
    "a\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "01049fa9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 7 8]\n",
      "[1 2 3]\n",
      "[9 3 2]\n"
     ]
    }
   ],
   "source": [
    "for row in a:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c556f9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\n",
      "7\n",
      "8\n",
      "1\n",
      "2\n",
      "3\n",
      "9\n",
      "3\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for cell in a.flat:\n",
    "    print(cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "71b731b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(6).reshape(3,2)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "6ecf9d95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6,  7],\n",
       "       [ 8,  9],\n",
       "       [10, 11]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(6,12).reshape(3,2)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "98e37311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1],\n",
       "       [ 2,  3],\n",
       "       [ 4,  5],\n",
       "       [ 6,  7],\n",
       "       [ 8,  9],\n",
       "       [10, 11]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.vstack((a,b))  # vertical stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5bd532fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  6,  7],\n",
       "       [ 2,  3,  8,  9],\n",
       "       [ 4,  5, 10, 11]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.hstack((a,b))  # horizontal stacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9d762f0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(30).reshape(2,15)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bb926fb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[ 0,  1,  2,  3,  4],\n",
       "        [15, 16, 17, 18, 19]]),\n",
       " array([[ 5,  6,  7,  8,  9],\n",
       "        [20, 21, 22, 23, 24]]),\n",
       " array([[10, 11, 12, 13, 14],\n",
       "        [25, 26, 27, 28, 29]])]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vertical slicing into three equal sized arrays\n",
    "\n",
    "np.hsplit(a,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d0b40fac",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = np.hsplit(a,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "2ff335aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3ebe51dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  6,  7,  8,  9],\n",
       "       [20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "39d1e674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 11, 12, 13, 14],\n",
       "       [25, 26, 27, 28, 29]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1989ed38",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = np.vsplit(a,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "2ddf9e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [15, 16, 17, 18, 19]])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "14cacfee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  6,  7,  8,  9],\n",
       "       [20, 21, 22, 23, 24]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "9f6dec4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(12).reshape(3,4)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "33b98989",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, False],\n",
       "       [False,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a > 4\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "27c164b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[b] # wherever it found true in b it return those elements from the original array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "741bd314",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4, -1, -1, -1],\n",
       "       [-1, -1, -1, -1]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[b] = -1\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "997fd4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Iterarte through numpy array\n",
    "\n",
    "a = np.arange(12).reshape(3,4)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "e95ec44f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n",
      "[4 5 6 7]\n",
      "[ 8  9 10 11]\n"
     ]
    }
   ],
   "source": [
    "for row in a:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "b398706d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for row in a:\n",
    "    for cell in row:\n",
    "        print(cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "09a706c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for cell in a.flatten():\n",
    "    print(cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "20198f5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "# numpy.nditer\n",
    "\n",
    "for x in np.nditer(a,order='c'):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "95c96b9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "4\n",
      "8\n",
      "1\n",
      "5\n",
      "9\n",
      "2\n",
      "6\n",
      "10\n",
      "3\n",
      "7\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "for x in np.nditer(a,order='F'):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "10b6c2b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 4 8]\n",
      "[1 5 9]\n",
      "[ 2  6 10]\n",
      "[ 3  7 11]\n"
     ]
    }
   ],
   "source": [
    "for x in np.nditer(a,order='F',flags = ['external_loop']):\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "2b2f3a04",
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in np.nditer(a,op_flags = ['readwrite']):\n",
    "    x[...] = x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "d7ec30b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   4,   9],\n",
       "       [ 16,  25,  36,  49],\n",
       "       [ 64,  81, 100, 121]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "68e13c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3],\n",
       "       [ 7],\n",
       "       [11]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iterating through two numpy array simultaneously\n",
    "\n",
    "b = np.arange(3,15,4).reshape(3,1)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "311da88e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3\n",
      "1 3\n",
      "4 3\n",
      "9 3\n",
      "16 7\n",
      "25 7\n",
      "36 7\n",
      "49 7\n",
      "64 11\n",
      "81 11\n",
      "100 11\n",
      "121 11\n"
     ]
    }
   ],
   "source": [
    "for x,y in np.nditer([a,b]):\n",
    "    print(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a9c68c1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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