1st_simplilearn_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6c660e1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "681d5d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "First_numpy_array = np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6284566a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "print (First_numpy_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e6a2e024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_with_zero = np.zeros((3,3))\n",
    "array_with_zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1e2bfad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_with_ones = np.ones((3,3))\n",
    "array_with_ones "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "027e8489",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+000, 6.95186734e-310, 4.94065646e-324],\n",
       "       [            nan, 0.00000000e+000, 1.94693963e-308]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_with_empty = np.empty((2,3))\n",
    "array_with_empty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9668ad76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_arrange = np.arange(12)\n",
    "np_arrange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b6bbc766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_arrange.reshape(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4ce13755",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.  , 2.25, 3.5 , 4.75, 6.  ])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np_linspace = np.linspace(1,6,5)\n",
    "np_linspace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a1fbd6af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    }
   ],
   "source": [
    "oneD_array = np.arange(15)\n",
    "print(oneD_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "88340c0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twoD_array = oneD_array.reshape(3,5)\n",
    "twoD_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6c2b15a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 3,  4,  5],\n",
       "        [ 6,  7,  8]],\n",
       "\n",
       "       [[ 9, 10, 11],\n",
       "        [12, 13, 14],\n",
       "        [15, 16, 17]],\n",
       "\n",
       "       [[18, 19, 20],\n",
       "        [21, 22, 23],\n",
       "        [24, 25, 26]]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threeD_array = np.arange(27).reshape(3,3,3)\n",
    "threeD_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c1e1a74",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}