Q9 a & b Solution Basic stats-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>speed</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>6</th>\n",
       "      <td>7</td>\n",
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       "      <td>18</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
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       "      <td>26</td>\n",
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       "      <th>8</th>\n",
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       "      <td>34</td>\n",
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       "      <td>11</td>\n",
       "      <td>17</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "      <td>34</td>\n",
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       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
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       "      <th>18</th>\n",
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       "      <td>46</td>\n",
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       "      <td>14</td>\n",
       "      <td>80</td>\n",
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       "      <th>24</th>\n",
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       "      <th>25</th>\n",
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       "      <td>54</td>\n",
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       "      <th>26</th>\n",
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       "      <td>32</td>\n",
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       "      <td>40</td>\n",
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       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>56</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
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       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>19</td>\n",
       "      <td>36</td>\n",
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       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>19</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>20</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>22</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
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       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>24</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>24</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>25</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Index  speed  dist\n",
       "0       1      4     2\n",
       "1       2      4    10\n",
       "2       3      7     4\n",
       "3       4      7    22\n",
       "4       5      8    16\n",
       "5       6      9    10\n",
       "6       7     10    18\n",
       "7       8     10    26\n",
       "8       9     10    34\n",
       "9      10     11    17\n",
       "10     11     11    28\n",
       "11     12     12    14\n",
       "12     13     12    20\n",
       "13     14     12    24\n",
       "14     15     12    28\n",
       "15     16     13    26\n",
       "16     17     13    34\n",
       "17     18     13    34\n",
       "18     19     13    46\n",
       "19     20     14    26\n",
       "20     21     14    36\n",
       "21     22     14    60\n",
       "22     23     14    80\n",
       "23     24     15    20\n",
       "24     25     15    26\n",
       "25     26     15    54\n",
       "26     27     16    32\n",
       "27     28     16    40\n",
       "28     29     17    32\n",
       "29     30     17    40\n",
       "30     31     17    50\n",
       "31     32     18    42\n",
       "32     33     18    56\n",
       "33     34     18    76\n",
       "34     35     18    84\n",
       "35     36     19    36\n",
       "36     37     19    46\n",
       "37     38     19    68\n",
       "38     39     20    32\n",
       "39     40     20    48\n",
       "40     41     20    52\n",
       "41     42     20    56\n",
       "42     43     20    64\n",
       "43     44     22    66\n",
       "44     45     23    54\n",
       "45     46     24    70\n",
       "46     47     24    92\n",
       "47     48     24    93\n",
       "48     49     24   120\n",
       "49     50     25    85"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_csv(\"C:/Users/LENOVO/Desktop/DS/Assingments/Basic stats-1/Q9_a.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index    0.000000\n",
       "speed   -0.117510\n",
       "dist     0.806895\n",
       "dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2., 3., 4., 6., 8., 5., 7., 8., 1., 6.]),\n",
       " array([ 4. ,  6.1,  8.2, 10.3, 12.4, 14.5, 16.6, 18.7, 20.8, 22.9, 25. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 9,
     "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(data[\"speed\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index   -1.200000\n",
       "speed   -0.508994\n",
       "dist     0.405053\n",
       "dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 4.,  8., 14.,  6.,  7.,  4.,  3.,  3.,  0.,  1.]),\n",
       " array([  2. ,  13.8,  25.6,  37.4,  49.2,  61. ,  72.8,  84.6,  96.4,\n",
       "        108.2, 120. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 11,
     "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(data[\"dist\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Unnamed: 0</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>104.185353</td>\n",
       "      <td>28.762059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0          SP         WT\n",
       "0            1  104.185353  28.762059\n",
       "1            2  105.461264  30.466833\n",
       "2            3  105.461264  30.193597\n",
       "3            4  113.461264  30.632114\n",
       "4            5  104.461264  29.889149\n",
       "..         ...         ...        ...\n",
       "76          77  169.598513  16.132947\n",
       "77          78  150.576579  37.923113\n",
       "78          79  151.598513  15.769625\n",
       "79          80  167.944460  39.423099\n",
       "80          81  139.840817  34.948615\n",
       "\n",
       "[81 rows x 3 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2=pd.read_csv(\"C:/Users/LENOVO/Desktop/DS/Assingments/Basic stats-1/Q9_b.csv\")\n",
    "data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0.000000\n",
       "SP            1.611450\n",
       "WT           -0.614753\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0   -1.200000\n",
       "SP            2.977329\n",
       "WT            0.950291\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 8., 12., 32., 10., 10.,  2.,  1.,  2.,  1.,  3.]),\n",
       " array([ 99.56490661, 106.56826723, 113.57162785, 120.57498847,\n",
       "        127.57834909, 134.58170971, 141.58507032, 148.58843094,\n",
       "        155.59179156, 162.59515218, 169.5985128 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sx9oPgE8Cvz5g/Ynyeiu9JDWqlVMokqQ1LHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUqP8DqTZLW6npH5kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(data2[\"SP\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 9.,  0.,  0., 22., 19., 18.,  7.,  5.,  0.,  1.]),\n",
       " array([15.71285853, 19.44134791, 23.1698373 , 26.89832668, 30.62681606,\n",
       "        34.35530544, 38.08379483, 41.81228421, 45.54077359, 49.26926298,\n",
       "        52.99775236]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 16,
     "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(data2[\"WT\"])"
   ]
  },
  {
   "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
}