binomial_distribution_using_scipy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Binomial Distribution Using Scipy\n",
    "#### PMF: probability mass function & CDF: cumulative distribution function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats as st\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A manufacturer has 12% defect rate in production. The buyer decides to test 20 random pieces and will accept the supply if there are 2 or less defective pieces. What is the probability of getting accepted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5631315224061949"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume the event of finding a defect piece is success\n",
    "# Given, number of sample : 20 & probability of success: 0.12\n",
    "# To find, probability of 2 or less successes\n",
    "st.binom.cdf(2, 20, 0.12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A manufacturer has 12% defect rate in production. The buyer decides to test 20 random pieces and will accept the supply if there are exactly 2 defective pieces. What is the probability of getting accepted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2740338370274878"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume the event of finding a defect piece is success\n",
    "# Given, number of sample : 20 & probability of success: 0.12\n",
    "# To find, probability of 2 (exact) successes\n",
    "st.binom.pmf(2, 20, 0.12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A manufacturer has 12% defect rate in production. The buyer decides to test 20 random pieces and will accept the supply if there are more than 2 defective pieces. What is the probability of getting accepted?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4368684775938051"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assume the event of finding a defect piece is success\n",
    "# Given, number of sample : 20 & probability of success: 0.12\n",
    "# To find, probability of getting more that 2 defective (2 excluded)\n",
    "st.binom.sf(2, 20, 0.12) # same as 1-st.binom.cdf(2, 20, 0.12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A manufacturer has 12% defect rate in production. The buyer decides to test 20 random pieces. Plot the probabilities of getting 0,1,2,3 ... 20 defectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_axis = np.arange(0,21)\n",
    "x_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.75627936e-02, 2.11534892e-01, 2.74033837e-01, 2.24209503e-01,\n",
       "       1.29939598e-01, 5.67009156e-02, 1.93298576e-02, 5.27177935e-03,\n",
       "       1.16817838e-03, 2.12396069e-04, 3.18594103e-05, 3.94951367e-06,\n",
       "       4.03927535e-07, 3.38960169e-08, 2.31109206e-09, 1.26059567e-10,\n",
       "       5.37185655e-12, 1.72359034e-13, 3.91725077e-15, 5.62284799e-17,\n",
       "       3.83375999e-19])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_axis = st.binom.pmf(x_axis, 20, 0.12)\n",
    "y_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=x_axis, y=y_axis)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A manufacturer has 12% defect rate in production. The buyer decides to test 20 random pieces. Plot the probabilities of getting 0 or less, 1 or less, 2 or less ... 20 or less defectives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_axis = np.arange(0,21)\n",
    "x_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07756279, 0.28909769, 0.56313152, 0.78734103, 0.91728062,\n",
       "       0.97398154, 0.9933114 , 0.99858318, 0.99975135, 0.99996375,\n",
       "       0.99999561, 0.99999956, 0.99999996, 1.        , 1.        ,\n",
       "       1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "       1.        ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_axis = st.binom.cdf(x_axis, 20, 0.12)\n",
    "y_axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ySP3rGF15/o9dmwNL2X+SAL8EfGhgfQEPjaqfwIRjcJH6U4HPdtOfBF6ySO1iWdP7+Bt3NAb6amD/vPk5BgTqLCVZB/w0o1H2kLoV3cu8A8Anq2pI/TsYPZAeHLLPMQXckOTmjL5uoa+fBA4C7+1O+bw7yWOW2IfzmfBAWkhV3Qv8HnAP8A1G1zPcMGATe4CzkjwxybGMRldrp9Qs5KTqrqPofp64hG3MyquBjw8tSvKfkuwHXgG8eWDtucC9VXXr0P3Os6077bNz0CmDkacAz07y+SSfSfKsJfbh2cD9VfXlgXUXA5d199/vAb81sH4PcG43/TJ6HINjWbPk4+9oDPReXyOw7J1IHgtcBVw8NuKZqqq+W1WnMRpZnZHkaT33+ULgQFXdPLS/Y362qk5n9C2Yr09yVs+6lYxePl5RVT8N/AOjl3yDZHQB2rnAnw+sO57R6OQU4MnAY5L8ct/6qrqD0SmKTwKfYHS67tDEoqNYkjcx6v+fDK2tqjdV1dqudtu09vP2eSzwJgY+CYy5Avgp4DRGT8xvH1i/EjgeOBP4TeDPutH2UBcwcFDReR3wxu7+eyPdK9YBXs3ocXczo1MpD0xq/HCyZtzRGOhH/GsEkjyK0R38J1V19VK3052u+DSwqWfJzwLnJvkao1NNz0vygSXs977u5wHgI4xOY/UxB8zNe0XxYUYBP9Rm4AtVdf/Aul8AvlpVB6vqn4CrgX85ZANV9Z6qOr2qzmL0Unjo6Azg/nTfFtr9XPQl/3JJ8irghcArqjuZukQfZJGX/Iv4KUZPqLd2x+Ea4AtJfrzvBqrq/m5Q8yDwh/Q//h4yB1zdnb78a0avVhd9Y3Yh3Sm7FwN/OnDfAK9idOzBaFAyqP9V9aWqen5VPZPRE8pXJvRzoaxZ8vF3NAZ6n68aWDbdSOA9wB1V9ftLqF/10KcSkjyaUUh9qU9tVf1WVa2pqnWMfu//XlW9R6jdPh+T5HEPTTN6g63XJ36q6pvA/iSndovOBr44ZP+dpY6M7gHOTHJs93c4m9F5xd6SnNj9PJnRA3op/biW0YOa7udHl7CNJUuyCbgEOLeqvrOE+vXzZs+l5/EHUFW3V9WJVbWuOw7nGL1x980B+5//1dnn0fP4m+ca4Hndtp7C6M35od9e+AvAl6pqbmAdjAaQz+mmn8fAQcG8Y/BHgP8AbF+k3WJZs/Tjr++7pz/MG6Nzn3cxemZ708DaDzF6mfdPjA7G1wys/zlGp3huA27pbucMqH868Ddd/R4mvMM+ZTvPZQmfcmF0HvzW7rZ3CfffacDurv/XAMcPrD8W+FvgCUv8vX+HUQDtAf6Y7pMOA+o/x+hJ6Fbg7KUcL8ATgU8xeiB/CvixgfXnddP/CNwPXD+wfh+j95EeOv4mfUplofqruvvvNuBjwOqlPl6Y8ompRfb/x8Dt3f6vBZ40sP4Y4APd7/AF4HlD+w+8D7hoiX//nwNu7o6hzwPPHFj/Bkb5dRfwVror8heoXTBrhhx/4zcv/ZekRhyNp1wkSUtgoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RG/D8wpE827RrmmQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=x_axis, y=y_axis)\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}