Learn numpy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ✔ CREATING NUMPY ARRAYS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FROM PYTHON OBJECT\n",
    "**From lists, tuples, dictionaries etc.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Yogesh', 'Sakshi', 'Pankaj'],\n",
       "       ['Saksham', 'Sahil', 'Sandesh']], dtype='<U7')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]], np.str)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FROM INBUILT FUNCTIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### np.zeros()\n",
    "**➡ Returns a null matrix of the provided order.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.zeros([2, 4])\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### np.arange()\n",
    "**➡ Returns a 1-D array(matrix) with elements as numbers till the argument provided.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(15)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### np.empty()\n",
    "**➡ Returns a null matrix of the provided order.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[8.78864560e-312, 3.16202013e-322, 0.00000000e+000],\n",
       "       [0.00000000e+000, 1.11260619e-306, 2.27202219e+184],\n",
       "       [1.48643702e-076, 2.37817686e+184, 4.04644567e-057],\n",
       "       [1.00538902e-070, 7.86209190e-067, 2.21568078e+160]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.empty([4, 3])\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### np.identity()\n",
    "**➡ Returns a identity matrix of the provided order.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.identity(4)\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ✔ ATTIBUTES OF A NUMPY ARRAY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### shape\n",
    "**➡ Returns the shape of the matrix.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]])\n",
    "arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### T\n",
    "**➡ Returns the transpose of matrix.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Yogesh', 'Saksham'],\n",
       "       ['Sakshi', 'Sahil'],\n",
       "       ['Pankaj', 'Sandesh']], dtype='<U7')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]])\n",
    "arr.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]])\n",
    "arr.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### size\n",
    "**➡ Returns the no. of elements of array.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]])\n",
    "arr.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### nbytes\n",
    "**➡ Returns the size (in bytes) that the array occupy in the memory.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]])\n",
    "arr.nbytes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ✔ METHODS FOR A NUMPY ARRAY"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .ravel()\n",
    "**➡ Converts the array to 1-D array.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Yogesh', 'Sakshi', 'Pankaj', 'Saksham', 'Sahil', 'Sandesh'],\n",
       "      dtype='<U7')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]])\n",
    "arr.ravel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .transpose()\n",
    "**➡ Returns the transpose of matrix.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Yogesh', 'Saksham'],\n",
       "       ['Sakshi', 'Sahil'],\n",
       "       ['Pankaj', 'Sandesh']], dtype='<U7')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]])\n",
    "arr.transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .reshape()\n",
    "**➡ Changes the order of the matrix to provided one.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original (2 X 3) array:\n",
      "[['Yogesh' 'Sakshi' 'Pankaj']\n",
      " ['Saksham' 'Sahil' 'Sandesh']]\n",
      "\n",
      "Reshaped (3 X 2) array:\n",
      "[['Yogesh' 'Sakshi']\n",
      " ['Pankaj' 'Saksham']\n",
      " ['Sahil' 'Sandesh']]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[\"Yogesh\", \"Sakshi\", \"Pankaj\"],\n",
    "                [\"Saksham\", \"Sahil\", \"Sandesh\"]])\n",
    "print(f\"Original (2 X 3) array:\\n{arr}\\n\")\n",
    "\n",
    "arrReshaped = arr.reshape(3, 2)\n",
    "print(f\"Reshaped (3 X 2) array:\\n{arrReshaped}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .sum()\n",
    "**➡ Returns the sum of the element of the axis provided. By default it sums all the elements of the array.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum on axis=0: [5 7 9] | (Y-axis)\n",
      "Sum on axis=1: [ 6 15] | (X-axis)\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 2, 3],\n",
    "                [4, 5, 6]])\n",
    "print(f\"Sum on axis=0: {arr.sum(axis=0)} | (Y-axis)\")\n",
    "print(f\"Sum on axis=1: {arr.sum(axis=1)} | (X-axis)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .argmin()\n",
    "**➡ Returns the index at which the min value of array is located.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum value index: 0\n",
      "\n",
      "Minimum value index on axis=0: [0 1 1]\n",
      "Minimum value index on axis=1: [0 0]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 5, 6],\n",
    "                [3, 4, 3]])\n",
    "print(f\"Minimum value index: {arr.argmin()}\\n\")\n",
    "\n",
    "print(f\"Minimum value index on axis=0: {arr.argmin(axis=0)}\")\n",
    "print(f\"Minimum value index on axis=1: {arr.argmin(axis=1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .argmax()\n",
    "**➡ Returns the index at which the max value of array is located.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum value index: 5\n",
      "\n",
      "Maximum value index on axis=0: [1 0 1]\n",
      "Maximum value index on axis=1: [2 2]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 5, 6],\n",
    "                [3, 4, 7]])\n",
    "print(f\"Maximum value index: {arr.argmax()}\\n\")\n",
    "\n",
    "print(f\"Maximum value index on axis=0: {arr.argmax(axis=0)}\")\n",
    "print(f\"Maximum value index on axis=1: {arr.argmax(axis=1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .argsort()\n",
    "**➡ Returns an array which contains indeces, when array is arranged according to them, it will sort the array.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "argsort:\n",
      "[[0 1 2]\n",
      " [0 1 2]]\n",
      "\n",
      "argsort on axis=0:\n",
      "[[0 1 0]\n",
      " [1 0 1]]\n",
      "argsort on axis=1:\n",
      "[[0 1 2]\n",
      " [0 1 2]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 5, 6],\n",
    "                [3, 4, 7]])\n",
    "print(f\"argsort:\\n{arr.argsort()}\\n\")\n",
    "\n",
    "print(f\"argsort on axis=0:\\n{arr.argsort(axis=0)}\")\n",
    "print(f\"argsort on axis=1:\\n{arr.argsort(axis=1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .min()\n",
    "**➡ Returns the minimum value of the array.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum value: 1\n",
      "\n",
      "Minimum value on axis=0: [1 4 3]\n",
      "Minimum value on axis=1: [1 3]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 5, 6],\n",
    "                [3, 4, 3]])\n",
    "print(f\"Minimum value: {arr.min()}\\n\")\n",
    "\n",
    "print(f\"Minimum value on axis=0: {arr.min(axis=0)}\")\n",
    "print(f\"Minimum value on axis=1: {arr.min(axis=1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### .max()\n",
    "**➡ Returns the maximum value of the array.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum value: 6\n",
      "\n",
      "Maximum value on axis=0: [3 5 6]\n",
      "Maximum value on axis=1: [6 4]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[1, 5, 6],\n",
    "                [3, 4, 3]])\n",
    "print(f\"Maximum value: {arr.max()}\\n\")\n",
    "\n",
    "print(f\"Maximum value on axis=0: {arr.max(axis=0)}\")\n",
    "print(f\"Maximum value on axis=1: {arr.max(axis=1)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FOR MORE VISIT DOCUMENTATION."
   ]
  }
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