Q20 solution Basic stats-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "%matplotlib inline\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>53.700681</td>\n",
       "      <td>89</td>\n",
       "      <td>104.185353</td>\n",
       "      <td>28.762059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>50.013401</td>\n",
       "      <td>92</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>50.013401</td>\n",
       "      <td>92</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>45.696322</td>\n",
       "      <td>92</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>50.504232</td>\n",
       "      <td>92</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>322</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>50</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>238</td>\n",
       "      <td>19.197888</td>\n",
       "      <td>115</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>263</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>295</td>\n",
       "      <td>19.833733</td>\n",
       "      <td>119</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>236</td>\n",
       "      <td>12.101263</td>\n",
       "      <td>107</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     HP        MPG  VOL          SP         WT\n",
       "0    49  53.700681   89  104.185353  28.762059\n",
       "1    55  50.013401   92  105.461264  30.466833\n",
       "2    55  50.013401   92  105.461264  30.193597\n",
       "3    70  45.696322   92  113.461264  30.632114\n",
       "4    53  50.504232   92  104.461264  29.889149\n",
       "..  ...        ...  ...         ...        ...\n",
       "76  322  36.900000   50  169.598513  16.132947\n",
       "77  238  19.197888  115  150.576579  37.923113\n",
       "78  263  34.000000   50  151.598513  15.769625\n",
       "79  295  19.833733  119  167.944460  39.423099\n",
       "80  236  12.101263  107  139.840817  34.948615\n",
       "\n",
       "[81 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"C:/Users/LENOVO/Desktop/DS/Assingments/Basic stats-1/Cars.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     53.700681\n",
       "1     50.013401\n",
       "2     50.013401\n",
       "3     45.696322\n",
       "4     50.504232\n",
       "        ...    \n",
       "76    36.900000\n",
       "77    19.197888\n",
       "78    34.000000\n",
       "79    19.833733\n",
       "80    12.101263\n",
       "Name: MPG, Length: 81, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=data[\"MPG\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34.422075728024666"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.131444731795982"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3475939251582705"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#P(MPG>38)\n",
    "1-stats.norm.cdf(38,loc=df.mean(),scale=df.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7293498762151616"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#P(MPG<40)\n",
    "stats.norm.cdf(40,loc=df.mean(),scale=df.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8988852898457339"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#P(20<MPG<50)\n",
    "b=stats.norm.cdf(50,loc=34.422,scale=9.131)\n",
    "s=stats.norm.cdf(20,loc=34.422,scale=9.131)\n",
    "b-s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  6., 10.,  4., 10., 15., 17.,  8.,  5.,  5.]),\n",
       " array([12.10126289, 16.26120474, 20.42114659, 24.58108844, 28.74103029,\n",
       "        32.90097213, 37.06091398, 41.22085583, 45.38079768, 49.54073953,\n",
       "        53.70068138]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.hist(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x22cfaec1108>,\n",
       "  <matplotlib.lines.Line2D at 0x22cfaec12c8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x22cfaeaca08>,\n",
       "  <matplotlib.lines.Line2D at 0x22cfaea2c08>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x22cfaec1648>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x22cfaec1288>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x22cfaeac2c8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "plt.boxplot(df)"
   ]
  },
  {
   "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
}