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December 27, 2022 05:54
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Basics_stats_Q7_assignment
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| { | |
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| "start_time": "2022-12-27T05:17:57.210940Z", | |
| "end_time": "2022-12-27T05:17:58.089775Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import pandas as pd", | |
| "execution_count": 1, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:18:33.290066Z", | |
| "end_time": "2022-12-27T05:18:33.306623Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df = pd.read_csv('Q7.csv')", | |
| "execution_count": 2, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:18:37.332261Z", | |
| "end_time": "2022-12-27T05:18:37.364370Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df", | |
| "execution_count": 3, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 3, | |
| "data": { | |
| "text/plain": " Unnamed: 0 Points Score Weigh\n0 Mazda RX4 3.90 2.620 16.46\n1 Mazda RX4 Wag 3.90 2.875 17.02\n2 Datsun 710 3.85 2.320 18.61\n3 Hornet 4 Drive 3.08 3.215 19.44\n4 Hornet Sportabout 3.15 3.440 17.02\n5 Valiant 2.76 3.460 20.22\n6 Duster 360 3.21 3.570 15.84\n7 Merc 240D 3.69 3.190 20.00\n8 Merc 230 3.92 3.150 22.90\n9 Merc 280 3.92 3.440 18.30\n10 Merc 280C 3.92 3.440 18.90\n11 Merc 450SE 3.07 4.070 17.40\n12 Merc 450SL 3.07 3.730 17.60\n13 Merc 450SLC 3.07 3.780 18.00\n14 Cadillac Fleetwood 2.93 5.250 17.98\n15 Lincoln Continental 3.00 5.424 17.82\n16 Chrysler Imperial 3.23 5.345 17.42\n17 Fiat 128 4.08 2.200 19.47\n18 Honda Civic 4.93 1.615 18.52\n19 Toyota Corolla 4.22 1.835 19.90\n20 Toyota Corona 3.70 2.465 20.01\n21 Dodge Challenger 2.76 3.520 16.87\n22 AMC Javelin 3.15 3.435 17.30\n23 Camaro Z28 3.73 3.840 15.41\n24 Pontiac Firebird 3.08 3.845 17.05\n25 Fiat X1-9 4.08 1.935 18.90\n26 Porsche 914-2 4.43 2.140 16.70\n27 Lotus Europa 3.77 1.513 16.90\n28 Ford Pantera L 4.22 3.170 14.50\n29 Ferrari Dino 3.62 2.770 15.50\n30 Maserati Bora 3.54 3.570 14.60\n31 Volvo 142E 4.11 2.780 18.60", | |
| "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>Points</th>\n <th>Score</th>\n <th>Weigh</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Mazda RX4</td>\n <td>3.90</td>\n <td>2.620</td>\n <td>16.46</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Mazda RX4 Wag</td>\n <td>3.90</td>\n <td>2.875</td>\n <td>17.02</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Datsun 710</td>\n <td>3.85</td>\n <td>2.320</td>\n <td>18.61</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Hornet 4 Drive</td>\n <td>3.08</td>\n <td>3.215</td>\n <td>19.44</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Hornet Sportabout</td>\n <td>3.15</td>\n <td>3.440</td>\n <td>17.02</td>\n </tr>\n <tr>\n <th>5</th>\n <td>Valiant</td>\n <td>2.76</td>\n <td>3.460</td>\n <td>20.22</td>\n </tr>\n <tr>\n <th>6</th>\n <td>Duster 360</td>\n <td>3.21</td>\n <td>3.570</td>\n <td>15.84</td>\n </tr>\n <tr>\n <th>7</th>\n <td>Merc 240D</td>\n <td>3.69</td>\n <td>3.190</td>\n <td>20.00</td>\n </tr>\n <tr>\n <th>8</th>\n <td>Merc 230</td>\n <td>3.92</td>\n <td>3.150</td>\n <td>22.90</td>\n </tr>\n <tr>\n <th>9</th>\n <td>Merc 280</td>\n <td>3.92</td>\n <td>3.440</td>\n <td>18.30</td>\n </tr>\n <tr>\n <th>10</th>\n <td>Merc 280C</td>\n <td>3.92</td>\n <td>3.440</td>\n <td>18.90</td>\n </tr>\n <tr>\n <th>11</th>\n <td>Merc 450SE</td>\n <td>3.07</td>\n <td>4.070</td>\n <td>17.40</td>\n </tr>\n <tr>\n <th>12</th>\n <td>Merc 450SL</td>\n <td>3.07</td>\n <td>3.730</td>\n <td>17.60</td>\n </tr>\n <tr>\n <th>13</th>\n <td>Merc 450SLC</td>\n <td>3.07</td>\n <td>3.780</td>\n <td>18.00</td>\n </tr>\n <tr>\n <th>14</th>\n <td>Cadillac Fleetwood</td>\n <td>2.93</td>\n <td>5.250</td>\n <td>17.98</td>\n </tr>\n <tr>\n <th>15</th>\n <td>Lincoln Continental</td>\n <td>3.00</td>\n <td>5.424</td>\n <td>17.82</td>\n </tr>\n <tr>\n <th>16</th>\n <td>Chrysler Imperial</td>\n <td>3.23</td>\n <td>5.345</td>\n <td>17.42</td>\n </tr>\n <tr>\n <th>17</th>\n <td>Fiat 128</td>\n <td>4.08</td>\n <td>2.200</td>\n <td>19.47</td>\n </tr>\n <tr>\n <th>18</th>\n <td>Honda Civic</td>\n <td>4.93</td>\n <td>1.615</td>\n <td>18.52</td>\n </tr>\n <tr>\n <th>19</th>\n <td>Toyota Corolla</td>\n <td>4.22</td>\n <td>1.835</td>\n <td>19.90</td>\n </tr>\n <tr>\n <th>20</th>\n <td>Toyota Corona</td>\n <td>3.70</td>\n <td>2.465</td>\n <td>20.01</td>\n </tr>\n <tr>\n <th>21</th>\n <td>Dodge Challenger</td>\n <td>2.76</td>\n <td>3.520</td>\n <td>16.87</td>\n </tr>\n <tr>\n <th>22</th>\n <td>AMC Javelin</td>\n <td>3.15</td>\n <td>3.435</td>\n <td>17.30</td>\n </tr>\n <tr>\n <th>23</th>\n <td>Camaro Z28</td>\n <td>3.73</td>\n <td>3.840</td>\n <td>15.41</td>\n </tr>\n <tr>\n <th>24</th>\n <td>Pontiac Firebird</td>\n <td>3.08</td>\n <td>3.845</td>\n <td>17.05</td>\n </tr>\n <tr>\n <th>25</th>\n <td>Fiat X1-9</td>\n <td>4.08</td>\n <td>1.935</td>\n <td>18.90</td>\n </tr>\n <tr>\n <th>26</th>\n <td>Porsche 914-2</td>\n <td>4.43</td>\n <td>2.140</td>\n <td>16.70</td>\n </tr>\n <tr>\n <th>27</th>\n <td>Lotus Europa</td>\n <td>3.77</td>\n <td>1.513</td>\n <td>16.90</td>\n </tr>\n <tr>\n <th>28</th>\n <td>Ford Pantera L</td>\n <td>4.22</td>\n <td>3.170</td>\n <td>14.50</td>\n </tr>\n <tr>\n <th>29</th>\n <td>Ferrari Dino</td>\n <td>3.62</td>\n <td>2.770</td>\n <td>15.50</td>\n </tr>\n <tr>\n <th>30</th>\n <td>Maserati Bora</td>\n <td>3.54</td>\n <td>3.570</td>\n <td>14.60</td>\n </tr>\n <tr>\n <th>31</th>\n <td>Volvo 142E</td>\n <td>4.11</td>\n <td>2.780</td>\n <td>18.60</td>\n </tr>\n </tbody>\n</table>\n</div>" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:22:22.345571Z", | |
| "end_time": "2022-12-27T05:22:22.370422Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.mean()", | |
| "execution_count": 7, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "C:\\Users\\yogesh\\AppData\\Local\\Temp\\ipykernel_12352\\3698961737.py:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError. Select only valid columns before calling the reduction.\n df.mean()\n", | |
| "name": "stderr" | |
| }, | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 7, | |
| "data": { | |
| "text/plain": "Points 3.596563\nScore 3.217250\nWeigh 17.848750\ndtype: float64" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:23:33.235263Z", | |
| "end_time": "2022-12-27T05:23:33.251725Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import warnings\nwarnings.filterwarnings('ignore')", | |
| "execution_count": 8, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:23:46.647846Z", | |
| "end_time": "2022-12-27T05:23:46.659045Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# average of whole dataset\ndf.mean()", | |
| "execution_count": 9, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 9, | |
| "data": { | |
| "text/plain": "Points 3.596563\nScore 3.217250\nWeigh 17.848750\ndtype: float64" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# points" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:27:25.783780Z", | |
| "end_time": "2022-12-27T05:27:25.797982Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Points.mean()", | |
| "execution_count": 14, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 14, | |
| "data": { | |
| "text/plain": "3.5965625000000006" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:27:52.921678Z", | |
| "end_time": "2022-12-27T05:27:52.937871Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Points.median()", | |
| "execution_count": 15, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 15, | |
| "data": { | |
| "text/plain": "3.6950000000000003" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:28:16.983690Z", | |
| "end_time": "2022-12-27T05:28:17.007599Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Points.mode()", | |
| "execution_count": 16, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 16, | |
| "data": { | |
| "text/plain": "0 3.07\n1 3.92\nName: Points, dtype: float64" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:28:37.558621Z", | |
| "end_time": "2022-12-27T05:28:37.579511Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Points.std()", | |
| "execution_count": 17, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 17, | |
| "data": { | |
| "text/plain": "0.5346787360709716" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:28:48.123370Z", | |
| "end_time": "2022-12-27T05:28:48.133023Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Points.var()", | |
| "execution_count": 18, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 18, | |
| "data": { | |
| "text/plain": "0.28588135080645166" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:30:54.058379Z", | |
| "end_time": "2022-12-27T05:30:54.081961Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Points.min()", | |
| "execution_count": 20, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 20, | |
| "data": { | |
| "text/plain": "2.76" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:31:15.867779Z", | |
| "end_time": "2022-12-27T05:31:15.883782Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Points.max()", | |
| "execution_count": 21, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 21, | |
| "data": { | |
| "text/plain": "4.93" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:33:07.544796Z", | |
| "end_time": "2022-12-27T05:33:07.557790Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Points.min()-df.Points.max()", | |
| "execution_count": 25, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 25, | |
| "data": { | |
| "text/plain": "-2.17" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:34:29.850754Z", | |
| "end_time": "2022-12-27T05:34:29.857721Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "print(\"Range of points:\",df.Points.min()-df.Points.max())", | |
| "execution_count": 26, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "Range of points: -2.17\n", | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# score" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:39:46.308912Z", | |
| "end_time": "2022-12-27T05:39:46.332164Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Score.mean()", | |
| "execution_count": 28, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 28, | |
| "data": { | |
| "text/plain": "3.2172499999999995" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:40:18.776178Z", | |
| "end_time": "2022-12-27T05:40:18.792690Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Score.median()", | |
| "execution_count": 29, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 29, | |
| "data": { | |
| "text/plain": "3.325" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:41:01.822522Z", | |
| "end_time": "2022-12-27T05:41:01.838932Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Score.mode()", | |
| "execution_count": 30, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 30, | |
| "data": { | |
| "text/plain": "0 3.44\nName: Score, dtype: float64" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:41:29.419114Z", | |
| "end_time": "2022-12-27T05:41:29.435293Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Score.std()", | |
| "execution_count": 31, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 31, | |
| "data": { | |
| "text/plain": "0.9784574429896967" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:41:37.176410Z", | |
| "end_time": "2022-12-27T05:41:37.196047Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Score.var()", | |
| "execution_count": 32, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 32, | |
| "data": { | |
| "text/plain": "0.9573789677419356" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:41:51.685795Z", | |
| "end_time": "2022-12-27T05:41:51.701891Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Score.min()", | |
| "execution_count": 33, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 33, | |
| "data": { | |
| "text/plain": "1.513" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:41:59.108368Z", | |
| "end_time": "2022-12-27T05:41:59.132521Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Score.max()", | |
| "execution_count": 34, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 34, | |
| "data": { | |
| "text/plain": "5.424" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:42:26.569107Z", | |
| "end_time": "2022-12-27T05:42:26.584044Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Score.min()-df.Score.max()", | |
| "execution_count": 35, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 35, | |
| "data": { | |
| "text/plain": "-3.9110000000000005" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:43:15.623124Z", | |
| "end_time": "2022-12-27T05:43:15.639150Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "print(\"range of score:\",df.Score.min()-df.Score.max())", | |
| "execution_count": 36, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "range of score: -3.9110000000000005\n", | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": {}, | |
| "cell_type": "markdown", | |
| "source": "# Weigh" | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:44:38.598933Z", | |
| "end_time": "2022-12-27T05:44:38.620167Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Weigh.mean()", | |
| "execution_count": 38, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 38, | |
| "data": { | |
| "text/plain": "17.848750000000003" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:45:48.357243Z", | |
| "end_time": "2022-12-27T05:45:48.367776Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Weigh.median()", | |
| "execution_count": 39, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 39, | |
| "data": { | |
| "text/plain": "17.71" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:46:18.915708Z", | |
| "end_time": "2022-12-27T05:46:18.939909Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Weigh.mode()", | |
| "execution_count": 40, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 40, | |
| "data": { | |
| "text/plain": "0 17.02\n1 18.90\nName: Weigh, dtype: float64" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:46:46.043966Z", | |
| "end_time": "2022-12-27T05:46:46.068278Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Weigh.std()", | |
| "execution_count": 41, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 41, | |
| "data": { | |
| "text/plain": "1.7869432360968431" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:46:52.829110Z", | |
| "end_time": "2022-12-27T05:46:52.845419Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Weigh.var()", | |
| "execution_count": 42, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 42, | |
| "data": { | |
| "text/plain": "3.193166129032258" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:47:00.382595Z", | |
| "end_time": "2022-12-27T05:47:00.399018Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Weigh.max()", | |
| "execution_count": 43, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 43, | |
| "data": { | |
| "text/plain": "22.9" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:47:12.329897Z", | |
| "end_time": "2022-12-27T05:47:12.343624Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Weigh.min()", | |
| "execution_count": 44, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 44, | |
| "data": { | |
| "text/plain": "14.5" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:48:06.961434Z", | |
| "end_time": "2022-12-27T05:48:06.975271Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "df.Weigh.max()-df.Weigh.min()", | |
| "execution_count": 45, | |
| "outputs": [ | |
| { | |
| "output_type": "execute_result", | |
| "execution_count": 45, | |
| "data": { | |
| "text/plain": "8.399999999999999" | |
| }, | |
| "metadata": {} | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "start_time": "2022-12-27T05:49:02.048617Z", | |
| "end_time": "2022-12-27T05:49:02.065149Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "print(\"range of weigh:\",df.Weigh.max()-df.Weigh.min())", | |
| "execution_count": 46, | |
| "outputs": [ | |
| { | |
| "output_type": "stream", | |
| "text": "range of weigh: 8.399999999999999\n", | |
| "name": "stdout" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "", | |
| "execution_count": null, | |
| "outputs": [] | |
| } | |
| ], | |
| "metadata": { | |
| "kernelspec": { | |
| "name": "python3", | |
| "display_name": "Python 3 (ipykernel)", | |
| "language": "python" | |
| }, | |
| "language_info": { | |
| "name": "python", | |
| "version": "3.9.13", | |
| "mimetype": "text/x-python", | |
| "codemirror_mode": { | |
| "name": "ipython", | |
| "version": 3 | |
| }, | |
| "pygments_lexer": "ipython3", | |
| "nbconvert_exporter": "python", | |
| "file_extension": ".py" | |
| }, | |
| "gist": { | |
| "id": "", | |
| "data": { | |
| "description": "Basics_stats_Q7_assignment", | |
| "public": true | |
| } | |
| } | |
| }, | |
| "nbformat": 4, | |
| "nbformat_minor": 5 | |
| } |
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