numpy_5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unique and Other Set Logic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['bob', 'john', 'micky', 'steve', 'will'], dtype='<U5')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = np.array(['bob','john','micky','bob','micky','will','steve','will'])\n",
    "np.unique(names)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contrast np.unique with the pure Python alternative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bob', 'john', 'micky', 'steve', 'will']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another function, np.in1d , tests membership of the values in one array in another,\n",
    "returning a boolean array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names_to_check =np.array(['bob','steve','will'])\n",
    "np.in1d(names_to_check,names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mathematical and Statistical Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "matrix = np.arange(1,10).reshape(3,3)\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "[[ 1  2  3]\n",
      " [ 5  7  9]\n",
      " [12 15 18]]\n",
      "[[  1   2   6]\n",
      " [  4  20 120]\n",
      " [  7  56 504]]\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(matrix))\n",
    "print(np.cumsum(matrix, axis = 0))\n",
    "print(np.cumprod(matrix, axis = 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Methods for Boolean Arrays\n",
    "To check how many true values are their\n",
    "To check if any value is true\n",
    "To check whether all values are true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "True\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "bool_arr = np.array([True,False,False,True,False,True,True])\n",
    "print(np.sum(bool_arr))\n",
    "print(np.any(bool_arr))\n",
    "print(np.all(bool_arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 5 2 6]\n",
      " [1 8 3 0]\n",
      " [7 3 1 4]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[4,5,2,6],[1,8,3,0],[7,3,1,4]]).reshape(3,4)\n",
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4, 5, 6],\n",
       "       [0, 1, 3, 8],\n",
       "       [1, 3, 4, 7]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(arr, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3, 1, 0],\n",
       "       [4, 5, 2, 4],\n",
       "       [7, 8, 3, 6]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(arr, axis = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remember when you are using np.function() and give it to array it dosen't changes\n",
    "the original array values but if you want to change the original array you should\n",
    "use these functions as arr.function(axis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3, 1, 0],\n",
       "       [4, 5, 2, 4],\n",
       "       [7, 8, 3, 6]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sort(axis = 0)\n",
    "arr\n"
   ]
  },
  {
   "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
}