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Python でやってみる『"データサイエンティスト養成読本" 特集1 第1章 Rで統計解析を始めよう』
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tutorial.ipynb
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"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python \u3067\u3084\u3063\u3066\u307f\u308b\u300e\"\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u990a\u6210\u8aad\u672c\" \u7279\u96c61 \u7b2c1\u7ae0 R\u3067\u7d71\u8a08\u89e3\u6790\u3092\u59cb\u3081\u3088\u3046\u300f"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list1\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list2\n",
"## \u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf\n",
"## http://gihyo.jp/book/2013/978-4-7741-5896-9/support\n",
"body_data = pd.read_csv(\"body_sample.csv\")\n",
"body_data.info()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 400 entries, 0 to 399\n",
"Data columns (total 4 columns):\n",
"id 400 non-null int64\n",
"gender 400 non-null object\n",
"height 400 non-null float64\n",
"weight 400 non-null float64\n",
"dtypes: float64(2), int64(1), object(1)"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list3 \u30c7\u30fc\u30bf\u306e\u57fa\u672c\u64cd\u4f5c"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"body_data[[1]] # \u5217\u756a\u53f7\u3092\u6307\u5b9a\u3057\u3066\u53d6\u5f97"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>373</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>382</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>383</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>384</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>385</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>387</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>388</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td> F</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td> F</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>400 rows \u00d7 1 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" gender\n",
"0 M\n",
"1 M\n",
"2 M\n",
"3 M\n",
"4 M\n",
"5 M\n",
"6 M\n",
"7 M\n",
"8 M\n",
"9 M\n",
"10 M\n",
"11 M\n",
"12 M\n",
"13 M\n",
"14 M\n",
"15 M\n",
"16 M\n",
"17 M\n",
"18 M\n",
"19 M\n",
"20 M\n",
"21 M\n",
"22 M\n",
"23 M\n",
"24 M\n",
"25 M\n",
"26 M\n",
"27 M\n",
"28 M\n",
"29 M\n",
".. ...\n",
"370 F\n",
"371 F\n",
"372 F\n",
"373 F\n",
"374 F\n",
"375 F\n",
"376 F\n",
"377 F\n",
"378 F\n",
"379 F\n",
"380 F\n",
"381 F\n",
"382 F\n",
"383 F\n",
"384 F\n",
"385 F\n",
"386 F\n",
"387 F\n",
"388 F\n",
"389 F\n",
"390 F\n",
"391 F\n",
"392 F\n",
"393 F\n",
"394 F\n",
"395 F\n",
"396 F\n",
"397 F\n",
"398 F\n",
"399 F\n",
"\n",
"[400 rows x 1 columns]"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"body_data[[0, 2]] # \u8907\u6570\u306e\u5217\u756a\u53f7\u3092\u6307\u5b9a\u3057\u3066\u53d6\u5f97"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>height</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 1</td>\n",
" <td> 157.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2</td>\n",
" <td> 178.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 3</td>\n",
" <td> 161.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 4</td>\n",
" <td> 162.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 5</td>\n",
" <td> 167.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 6</td>\n",
" <td> 165.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 7</td>\n",
" <td> 163.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 8</td>\n",
" <td> 171.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 9</td>\n",
" <td> 161.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 10</td>\n",
" <td> 160.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> 11</td>\n",
" <td> 163.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> 12</td>\n",
" <td> 152.74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> 13</td>\n",
" <td> 157.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> 14</td>\n",
" <td> 167.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> 15</td>\n",
" <td> 170.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> 16</td>\n",
" <td> 171.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> 17</td>\n",
" <td> 166.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> 18</td>\n",
" <td> 167.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> 19</td>\n",
" <td> 170.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> 20</td>\n",
" <td> 172.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> 21</td>\n",
" <td> 157.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> 22</td>\n",
" <td> 165.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> 23</td>\n",
" <td> 161.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> 24</td>\n",
" <td> 153.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> 25</td>\n",
" <td> 162.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> 26</td>\n",
" <td> 168.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> 27</td>\n",
" <td> 163.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 28</td>\n",
" <td> 157.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> 29</td>\n",
" <td> 154.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> 30</td>\n",
" <td> 155.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370</th>\n",
" <td> 371</td>\n",
" <td> 154.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371</th>\n",
" <td> 372</td>\n",
" <td> 155.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td> 373</td>\n",
" <td> 160.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>373</th>\n",
" <td> 374</td>\n",
" <td> 151.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td> 375</td>\n",
" <td> 151.46</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td> 376</td>\n",
" <td> 154.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\n",
" <td> 377</td>\n",
" <td> 150.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <td> 378</td>\n",
" <td> 153.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td> 379</td>\n",
" <td> 154.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379</th>\n",
" <td> 380</td>\n",
" <td> 154.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td> 381</td>\n",
" <td> 158.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td> 382</td>\n",
" <td> 149.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>382</th>\n",
" <td> 383</td>\n",
" <td> 167.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>383</th>\n",
" <td> 384</td>\n",
" <td> 159.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>384</th>\n",
" <td> 385</td>\n",
" <td> 151.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>385</th>\n",
" <td> 386</td>\n",
" <td> 148.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td> 387</td>\n",
" <td> 158.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>387</th>\n",
" <td> 388</td>\n",
" <td> 160.72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>388</th>\n",
" <td> 389</td>\n",
" <td> 151.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td> 390</td>\n",
" <td> 151.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td> 391</td>\n",
" <td> 141.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td> 392</td>\n",
" <td> 167.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td> 393</td>\n",
" <td> 156.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td> 394</td>\n",
" <td> 145.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td> 395</td>\n",
" <td> 160.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td> 396</td>\n",
" <td> 163.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td> 397</td>\n",
" <td> 161.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td> 398</td>\n",
" <td> 161.86</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td> 399</td>\n",
" <td> 150.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td> 400</td>\n",
" <td> 146.28</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>400 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
" id height\n",
"0 1 157.67\n",
"1 2 178.76\n",
"2 3 161.95\n",
"3 4 162.26\n",
"4 5 167.95\n",
"5 6 165.59\n",
"6 7 163.66\n",
"7 8 171.78\n",
"8 9 161.11\n",
"9 10 160.97\n",
"10 11 163.69\n",
"11 12 152.74\n",
"12 13 157.58\n",
"13 14 167.95\n",
"14 15 170.35\n",
"15 16 171.33\n",
"16 17 166.38\n",
"17 18 167.69\n",
"18 19 170.19\n",
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".. ... ...\n",
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"[400 rows x 2 columns]"
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},
{
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"...\n",
"385 47.84\n",
"386 53.94\n",
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{
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{
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}
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"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"body_data[[\"id\", \"height\"]] # \u8907\u6570\u306e\u5217\u540d\u3067\u53d6\u5f97"
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{
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" <td> 161.91</td>\n",
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" </tr>\n",
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" <td> 168.42</td>\n",
" </tr>\n",
" <tr>\n",
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" <td> 163.93</td>\n",
" </tr>\n",
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" <td> 157.76</td>\n",
" </tr>\n",
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" <td> 154.93</td>\n",
" </tr>\n",
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" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <td> 373</td>\n",
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" <td> 376</td>\n",
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" <td> 159.67</td>\n",
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"27 28 157.76\n",
"28 29 154.93\n",
"29 30 155.84\n",
".. ... ...\n",
"370 371 154.89\n",
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"372 373 160.31\n",
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"374 375 151.46\n",
"375 376 154.98\n",
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"397 398 161.86\n",
"398 399 150.88\n",
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}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"body_data[body_data.gender == \"F\"] # \u6761\u4ef6\u306b\u3042\u3063\u305f\u884c\u3060\u3051\u53d6\u308a\u51fa\u3059"
],
"language": "python",
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{
"html": [
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" <thead>\n",
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" <th>id</th>\n",
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" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
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" <td> 203</td>\n",
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" <td> F</td>\n",
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" <td> 209</td>\n",
" <td> F</td>\n",
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" <td> 43.73</td>\n",
" </tr>\n",
" <tr>\n",
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" <td> 210</td>\n",
" <td> F</td>\n",
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" <td> F</td>\n",
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" <th>216</th>\n",
" <td> 217</td>\n",
" <td> F</td>\n",
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" <th>217</th>\n",
" <td> 218</td>\n",
" <td> F</td>\n",
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" <td> 55.11</td>\n",
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" <th>218</th>\n",
" <td> 219</td>\n",
" <td> F</td>\n",
" <td> 165.63</td>\n",
" <td> 65.16</td>\n",
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" <tr>\n",
" <th>219</th>\n",
" <td> 220</td>\n",
" <td> F</td>\n",
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" <tr>\n",
" <th>220</th>\n",
" <td> 221</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>221</th>\n",
" <td> 222</td>\n",
" <td> F</td>\n",
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" <tr>\n",
" <th>222</th>\n",
" <td> 223</td>\n",
" <td> F</td>\n",
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" <td> 53.43</td>\n",
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" <tr>\n",
" <th>223</th>\n",
" <td> 224</td>\n",
" <td> F</td>\n",
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" <td> 57.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>224</th>\n",
" <td> 225</td>\n",
" <td> F</td>\n",
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" <tr>\n",
" <th>225</th>\n",
" <td> 226</td>\n",
" <td> F</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>226</th>\n",
" <td> 227</td>\n",
" <td> F</td>\n",
" <td> 149.45</td>\n",
" <td> 48.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>227</th>\n",
" <td> 228</td>\n",
" <td> F</td>\n",
" <td> 143.75</td>\n",
" <td> 43.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>228</th>\n",
" <td> 229</td>\n",
" <td> F</td>\n",
" <td> 160.09</td>\n",
" <td> 60.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>229</th>\n",
" <td> 230</td>\n",
" <td> F</td>\n",
" <td> 149.42</td>\n",
" <td> 50.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370</th>\n",
" <td> 371</td>\n",
" <td> F</td>\n",
" <td> 154.89</td>\n",
" <td> 53.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371</th>\n",
" <td> 372</td>\n",
" <td> F</td>\n",
" <td> 155.31</td>\n",
" <td> 49.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372</th>\n",
" <td> 373</td>\n",
" <td> F</td>\n",
" <td> 160.31</td>\n",
" <td> 55.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>373</th>\n",
" <td> 374</td>\n",
" <td> F</td>\n",
" <td> 151.83</td>\n",
" <td> 50.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>374</th>\n",
" <td> 375</td>\n",
" <td> F</td>\n",
" <td> 151.46</td>\n",
" <td> 51.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375</th>\n",
" <td> 376</td>\n",
" <td> F</td>\n",
" <td> 154.98</td>\n",
" <td> 55.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\n",
" <td> 377</td>\n",
" <td> F</td>\n",
" <td> 150.91</td>\n",
" <td> 49.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377</th>\n",
" <td> 378</td>\n",
" <td> F</td>\n",
" <td> 153.04</td>\n",
" <td> 56.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td> 379</td>\n",
" <td> F</td>\n",
" <td> 154.76</td>\n",
" <td> 50.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379</th>\n",
" <td> 380</td>\n",
" <td> F</td>\n",
" <td> 154.67</td>\n",
" <td> 52.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td> 381</td>\n",
" <td> F</td>\n",
" <td> 158.71</td>\n",
" <td> 57.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>381</th>\n",
" <td> 382</td>\n",
" <td> F</td>\n",
" <td> 149.25</td>\n",
" <td> 50.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>382</th>\n",
" <td> 383</td>\n",
" <td> F</td>\n",
" <td> 167.48</td>\n",
" <td> 65.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>383</th>\n",
" <td> 384</td>\n",
" <td> F</td>\n",
" <td> 159.67</td>\n",
" <td> 58.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>384</th>\n",
" <td> 385</td>\n",
" <td> F</td>\n",
" <td> 151.79</td>\n",
" <td> 45.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>385</th>\n",
" <td> 386</td>\n",
" <td> F</td>\n",
" <td> 148.39</td>\n",
" <td> 47.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386</th>\n",
" <td> 387</td>\n",
" <td> F</td>\n",
" <td> 158.86</td>\n",
" <td> 53.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>387</th>\n",
" <td> 388</td>\n",
" <td> F</td>\n",
" <td> 160.72</td>\n",
" <td> 60.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>388</th>\n",
" <td> 389</td>\n",
" <td> F</td>\n",
" <td> 151.20</td>\n",
" <td> 47.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389</th>\n",
" <td> 390</td>\n",
" <td> F</td>\n",
" <td> 151.27</td>\n",
" <td> 44.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td> 391</td>\n",
" <td> F</td>\n",
" <td> 141.49</td>\n",
" <td> 41.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391</th>\n",
" <td> 392</td>\n",
" <td> F</td>\n",
" <td> 167.83</td>\n",
" <td> 67.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392</th>\n",
" <td> 393</td>\n",
" <td> F</td>\n",
" <td> 156.41</td>\n",
" <td> 52.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td> 394</td>\n",
" <td> F</td>\n",
" <td> 145.48</td>\n",
" <td> 45.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>394</th>\n",
" <td> 395</td>\n",
" <td> F</td>\n",
" <td> 160.13</td>\n",
" <td> 57.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>395</th>\n",
" <td> 396</td>\n",
" <td> F</td>\n",
" <td> 163.84</td>\n",
" <td> 59.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>396</th>\n",
" <td> 397</td>\n",
" <td> F</td>\n",
" <td> 161.23</td>\n",
" <td> 56.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397</th>\n",
" <td> 398</td>\n",
" <td> F</td>\n",
" <td> 161.86</td>\n",
" <td> 57.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>398</th>\n",
" <td> 399</td>\n",
" <td> F</td>\n",
" <td> 150.88</td>\n",
" <td> 50.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td> 400</td>\n",
" <td> F</td>\n",
" <td> 146.28</td>\n",
" <td> 42.26</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>200 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
" id gender height weight\n",
"200 201 F 157.64 51.16\n",
"201 202 F 146.67 43.98\n",
"202 203 F 154.72 56.15\n",
"203 204 F 158.33 53.37\n",
"204 205 F 153.25 49.22\n",
"205 206 F 165.35 62.92\n",
"206 207 F 147.87 47.59\n",
"207 208 F 150.70 43.52\n",
"208 209 F 146.92 43.73\n",
"209 210 F 137.89 40.85\n",
"210 211 F 150.98 49.94\n",
"211 212 F 153.57 53.25\n",
"212 213 F 152.95 48.96\n",
"213 214 F 157.06 49.27\n",
"214 215 F 153.07 51.64\n",
"215 216 F 160.87 57.23\n",
"216 217 F 149.80 51.29\n",
"217 218 F 161.15 55.11\n",
"218 219 F 165.63 65.16\n",
"219 220 F 147.34 44.93\n",
"220 221 F 159.46 59.56\n",
"221 222 F 143.69 45.75\n",
"222 223 F 155.91 53.43\n",
"223 224 F 161.78 57.97\n",
"224 225 F 151.21 50.32\n",
"225 226 F 165.19 63.59\n",
"226 227 F 149.45 48.54\n",
"227 228 F 143.75 43.67\n",
"228 229 F 160.09 60.29\n",
"229 230 F 149.42 50.35\n",
".. ... ... ... ...\n",
"370 371 F 154.89 53.77\n",
"371 372 F 155.31 49.11\n",
"372 373 F 160.31 55.68\n",
"373 374 F 151.83 50.36\n",
"374 375 F 151.46 51.21\n",
"375 376 F 154.98 55.09\n",
"376 377 F 150.91 49.67\n",
"377 378 F 153.04 56.13\n",
"378 379 F 154.76 50.25\n",
"379 380 F 154.67 52.39\n",
"380 381 F 158.71 57.63\n",
"381 382 F 149.25 50.68\n",
"382 383 F 167.48 65.62\n",
"383 384 F 159.67 58.95\n",
"384 385 F 151.79 45.20\n",
"385 386 F 148.39 47.84\n",
"386 387 F 158.86 53.94\n",
"387 388 F 160.72 60.88\n",
"388 389 F 151.20 47.62\n",
"389 390 F 151.27 44.98\n",
"390 391 F 141.49 41.07\n",
"391 392 F 167.83 67.78\n",
"392 393 F 156.41 52.70\n",
"393 394 F 145.48 45.13\n",
"394 395 F 160.13 57.55\n",
"395 396 F 163.84 59.15\n",
"396 397 F 161.23 56.51\n",
"397 398 F 161.86 57.02\n",
"398 399 F 150.88 50.60\n",
"399 400 F 146.28 42.26\n",
"\n",
"[200 rows x 4 columns]"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"body_data.sort(\"height\") # \u6607\u9806\u3067\u30bd\u30fc\u30c8"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>gender</th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>323</th>\n",
" <td> 324</td>\n",
" <td> F</td>\n",
" <td> 135.51</td>\n",
" <td> 33.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td> 270</td>\n",
" <td> F</td>\n",
" <td> 136.59</td>\n",
" <td> 36.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td> 283</td>\n",
" <td> F</td>\n",
" <td> 136.85</td>\n",
" <td> 31.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>209</th>\n",
" <td> 210</td>\n",
" <td> F</td>\n",
" <td> 137.89</td>\n",
" <td> 40.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td> 282</td>\n",
" <td> F</td>\n",
" <td> 140.53</td>\n",
" <td> 41.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246</th>\n",
" <td> 247</td>\n",
" <td> F</td>\n",
" <td> 141.10</td>\n",
" <td> 35.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td> 391</td>\n",
" <td> F</td>\n",
" <td> 141.49</td>\n",
" <td> 41.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>324</th>\n",
" <td> 325</td>\n",
" <td> F</td>\n",
" <td> 141.62</td>\n",
" <td> 39.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td> 243</td>\n",
" <td> F</td>\n",
" <td> 142.09</td>\n",
" <td> 43.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>257</th>\n",
" <td> 258</td>\n",
" <td> F</td>\n",
" <td> 143.00</td>\n",
" <td> 38.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>251</th>\n",
" <td> 252</td>\n",
" <td> F</td>\n",
" <td> 143.17</td>\n",
" <td> 39.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td> 267</td>\n",
" <td> F</td>\n",
" <td> 143.25</td>\n",
" <td> 45.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>221</th>\n",
" <td> 222</td>\n",
" <td> F</td>\n",
" <td> 143.69</td>\n",
" <td> 45.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>227</th>\n",
" <td> 228</td>\n",
" <td> F</td>\n",
" <td> 143.75</td>\n",
" <td> 43.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>364</th>\n",
" <td> 365</td>\n",
" <td> F</td>\n",
" <td> 143.79</td>\n",
" <td> 43.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td> 287</td>\n",
" <td> F</td>\n",
" <td> 144.07</td>\n",
" <td> 42.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>244</th>\n",
" <td> 245</td>\n",
" <td> F</td>\n",
" <td> 144.47</td>\n",
" <td> 44.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>237</th>\n",
" <td> 238</td>\n",
" <td> F</td>\n",
" <td> 145.02</td>\n",
" <td> 40.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90 </th>\n",
" <td> 91</td>\n",
" <td> M</td>\n",
" <td> 145.10</td>\n",
" <td> 50.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
" <td> 261</td>\n",
" <td> F</td>\n",
" <td> 145.48</td>\n",
" <td> 47.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td> 394</td>\n",
" <td> F</td>\n",
" <td> 145.48</td>\n",
" <td> 45.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td> 143</td>\n",
" <td> M</td>\n",
" <td> 145.63</td>\n",
" <td> 55.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>247</th>\n",
" <td> 248</td>\n",
" <td> F</td>\n",
" <td> 145.80</td>\n",
" <td> 45.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td> 120</td>\n",
" <td> M</td>\n",
" <td> 145.97</td>\n",
" <td> 55.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53 </th>\n",
" <td> 54</td>\n",
" <td> M</td>\n",
" <td> 146.00</td>\n",
" <td> 50.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td> 400</td>\n",
" <td> F</td>\n",
" <td> 146.28</td>\n",
" <td> 42.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td> 189</td>\n",
" <td> M</td>\n",
" <td> 146.48</td>\n",
" <td> 56.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201</th>\n",
" <td> 202</td>\n",
" <td> F</td>\n",
" <td> 146.67</td>\n",
" <td> 43.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>208</th>\n",
" <td> 209</td>\n",
" <td> F</td>\n",
" <td> 146.92</td>\n",
" <td> 43.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>283</th>\n",
" <td> 284</td>\n",
" <td> F</td>\n",
" <td> 147.27</td>\n",
" <td> 41.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93 </th>\n",
" <td> 94</td>\n",
" <td> M</td>\n",
" <td> 171.05</td>\n",
" <td> 70.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td> 114</td>\n",
" <td> M</td>\n",
" <td> 171.11</td>\n",
" <td> 77.74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59 </th>\n",
" <td> 60</td>\n",
" <td> M</td>\n",
" <td> 171.29</td>\n",
" <td> 70.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> 16</td>\n",
" <td> M</td>\n",
" <td> 171.33</td>\n",
" <td> 76.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td> 127</td>\n",
" <td> M</td>\n",
" <td> 171.40</td>\n",
" <td> 68.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 8</td>\n",
" <td> M</td>\n",
" <td> 171.78</td>\n",
" <td> 67.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60 </th>\n",
" <td> 61</td>\n",
" <td> M</td>\n",
" <td> 171.81</td>\n",
" <td> 68.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70 </th>\n",
" <td> 71</td>\n",
" <td> M</td>\n",
" <td> 171.86</td>\n",
" <td> 77.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td> 129</td>\n",
" <td> M</td>\n",
" <td> 171.86</td>\n",
" <td> 76.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td> 162</td>\n",
" <td> M</td>\n",
" <td> 171.95</td>\n",
" <td> 69.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97 </th>\n",
" <td> 98</td>\n",
" <td> M</td>\n",
" <td> 172.19</td>\n",
" <td> 68.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td> 198</td>\n",
" <td> M</td>\n",
" <td> 172.24</td>\n",
" <td> 69.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td> 134</td>\n",
" <td> M</td>\n",
" <td> 172.33</td>\n",
" <td> 66.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82 </th>\n",
" <td> 83</td>\n",
" <td> M</td>\n",
" <td> 172.43</td>\n",
" <td> 69.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td> 135</td>\n",
" <td> M</td>\n",
" <td> 172.57</td>\n",
" <td> 77.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> 20</td>\n",
" <td> M</td>\n",
" <td> 172.97</td>\n",
" <td> 70.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>328</th>\n",
" <td> 329</td>\n",
" <td> F</td>\n",
" <td> 173.76</td>\n",
" <td> 66.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td> 188</td>\n",
" <td> M</td>\n",
" <td> 173.79</td>\n",
" <td> 75.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td> 168</td>\n",
" <td> M</td>\n",
" <td> 174.07</td>\n",
" <td> 72.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td> 110</td>\n",
" <td> M</td>\n",
" <td> 174.10</td>\n",
" <td> 67.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td> 191</td>\n",
" <td> M</td>\n",
" <td> 175.74</td>\n",
" <td> 75.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td> 184</td>\n",
" <td> M</td>\n",
" <td> 176.07</td>\n",
" <td> 74.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38 </th>\n",
" <td> 39</td>\n",
" <td> M</td>\n",
" <td> 176.24</td>\n",
" <td> 73.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44 </th>\n",
" <td> 45</td>\n",
" <td> M</td>\n",
" <td> 176.34</td>\n",
" <td> 75.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154</th>\n",
" <td> 155</td>\n",
" <td> M</td>\n",
" <td> 177.06</td>\n",
" <td> 74.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td> 117</td>\n",
" <td> M</td>\n",
" <td> 177.17</td>\n",
" <td> 78.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2</td>\n",
" <td> M</td>\n",
" <td> 178.76</td>\n",
" <td> 72.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td> 113</td>\n",
" <td> M</td>\n",
" <td> 178.80</td>\n",
" <td> 69.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> 125</td>\n",
" <td> M</td>\n",
" <td> 179.17</td>\n",
" <td> 78.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76 </th>\n",
" <td> 77</td>\n",
" <td> M</td>\n",
" <td> 181.53</td>\n",
" <td> 77.60</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>400 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
" id gender height weight\n",
"323 324 F 135.51 33.07\n",
"269 270 F 136.59 36.07\n",
"282 283 F 136.85 31.44\n",
"209 210 F 137.89 40.85\n",
"281 282 F 140.53 41.28\n",
"246 247 F 141.10 35.02\n",
"390 391 F 141.49 41.07\n",
"324 325 F 141.62 39.77\n",
"242 243 F 142.09 43.59\n",
"257 258 F 143.00 38.47\n",
"251 252 F 143.17 39.01\n",
"266 267 F 143.25 45.40\n",
"221 222 F 143.69 45.75\n",
"227 228 F 143.75 43.67\n",
"364 365 F 143.79 43.30\n",
"286 287 F 144.07 42.68\n",
"244 245 F 144.47 44.17\n",
"237 238 F 145.02 40.22\n",
"90 91 M 145.10 50.49\n",
"260 261 F 145.48 47.71\n",
"393 394 F 145.48 45.13\n",
"142 143 M 145.63 55.43\n",
"247 248 F 145.80 45.37\n",
"119 120 M 145.97 55.62\n",
"53 54 M 146.00 50.69\n",
"399 400 F 146.28 42.26\n",
"188 189 M 146.48 56.07\n",
"201 202 F 146.67 43.98\n",
"208 209 F 146.92 43.73\n",
"283 284 F 147.27 41.88\n",
".. ... ... ... ...\n",
"93 94 M 171.05 70.21\n",
"113 114 M 171.11 77.74\n",
"59 60 M 171.29 70.13\n",
"15 16 M 171.33 76.02\n",
"126 127 M 171.40 68.27\n",
"7 8 M 171.78 67.76\n",
"60 61 M 171.81 68.40\n",
"70 71 M 171.86 77.23\n",
"128 129 M 171.86 76.90\n",
"161 162 M 171.95 69.59\n",
"97 98 M 172.19 68.83\n",
"197 198 M 172.24 69.80\n",
"133 134 M 172.33 66.38\n",
"82 83 M 172.43 69.58\n",
"134 135 M 172.57 77.34\n",
"19 20 M 172.97 70.98\n",
"328 329 F 173.76 66.17\n",
"187 188 M 173.79 75.48\n",
"167 168 M 174.07 72.92\n",
"109 110 M 174.10 67.76\n",
"190 191 M 175.74 75.60\n",
"183 184 M 176.07 74.48\n",
"38 39 M 176.24 73.56\n",
"44 45 M 176.34 75.02\n",
"154 155 M 177.06 74.68\n",
"116 117 M 177.17 78.99\n",
"1 2 M 178.76 72.38\n",
"112 113 M 178.80 69.24\n",
"124 125 M 179.17 78.56\n",
"76 77 M 181.53 77.60\n",
"\n",
"[400 rows x 4 columns]"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort.html\n",
"body_data.sort(\"height\", ascending=False) # \u964d\u9806\u3067\u30bd\u30fc\u30c8"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>gender</th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>76 </th>\n",
" <td> 77</td>\n",
" <td> M</td>\n",
" <td> 181.53</td>\n",
" <td> 77.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> 125</td>\n",
" <td> M</td>\n",
" <td> 179.17</td>\n",
" <td> 78.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td> 113</td>\n",
" <td> M</td>\n",
" <td> 178.80</td>\n",
" <td> 69.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2</td>\n",
" <td> M</td>\n",
" <td> 178.76</td>\n",
" <td> 72.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td> 117</td>\n",
" <td> M</td>\n",
" <td> 177.17</td>\n",
" <td> 78.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>154</th>\n",
" <td> 155</td>\n",
" <td> M</td>\n",
" <td> 177.06</td>\n",
" <td> 74.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44 </th>\n",
" <td> 45</td>\n",
" <td> M</td>\n",
" <td> 176.34</td>\n",
" <td> 75.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38 </th>\n",
" <td> 39</td>\n",
" <td> M</td>\n",
" <td> 176.24</td>\n",
" <td> 73.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td> 184</td>\n",
" <td> M</td>\n",
" <td> 176.07</td>\n",
" <td> 74.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>190</th>\n",
" <td> 191</td>\n",
" <td> M</td>\n",
" <td> 175.74</td>\n",
" <td> 75.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td> 110</td>\n",
" <td> M</td>\n",
" <td> 174.10</td>\n",
" <td> 67.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>167</th>\n",
" <td> 168</td>\n",
" <td> M</td>\n",
" <td> 174.07</td>\n",
" <td> 72.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>187</th>\n",
" <td> 188</td>\n",
" <td> M</td>\n",
" <td> 173.79</td>\n",
" <td> 75.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>328</th>\n",
" <td> 329</td>\n",
" <td> F</td>\n",
" <td> 173.76</td>\n",
" <td> 66.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> 20</td>\n",
" <td> M</td>\n",
" <td> 172.97</td>\n",
" <td> 70.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td> 135</td>\n",
" <td> M</td>\n",
" <td> 172.57</td>\n",
" <td> 77.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82 </th>\n",
" <td> 83</td>\n",
" <td> M</td>\n",
" <td> 172.43</td>\n",
" <td> 69.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td> 134</td>\n",
" <td> M</td>\n",
" <td> 172.33</td>\n",
" <td> 66.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>197</th>\n",
" <td> 198</td>\n",
" <td> M</td>\n",
" <td> 172.24</td>\n",
" <td> 69.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97 </th>\n",
" <td> 98</td>\n",
" <td> M</td>\n",
" <td> 172.19</td>\n",
" <td> 68.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>161</th>\n",
" <td> 162</td>\n",
" <td> M</td>\n",
" <td> 171.95</td>\n",
" <td> 69.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70 </th>\n",
" <td> 71</td>\n",
" <td> M</td>\n",
" <td> 171.86</td>\n",
" <td> 77.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td> 129</td>\n",
" <td> M</td>\n",
" <td> 171.86</td>\n",
" <td> 76.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60 </th>\n",
" <td> 61</td>\n",
" <td> M</td>\n",
" <td> 171.81</td>\n",
" <td> 68.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 8</td>\n",
" <td> M</td>\n",
" <td> 171.78</td>\n",
" <td> 67.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td> 127</td>\n",
" <td> M</td>\n",
" <td> 171.40</td>\n",
" <td> 68.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> 16</td>\n",
" <td> M</td>\n",
" <td> 171.33</td>\n",
" <td> 76.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59 </th>\n",
" <td> 60</td>\n",
" <td> M</td>\n",
" <td> 171.29</td>\n",
" <td> 70.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td> 114</td>\n",
" <td> M</td>\n",
" <td> 171.11</td>\n",
" <td> 77.74</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93 </th>\n",
" <td> 94</td>\n",
" <td> M</td>\n",
" <td> 171.05</td>\n",
" <td> 70.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>283</th>\n",
" <td> 284</td>\n",
" <td> F</td>\n",
" <td> 147.27</td>\n",
" <td> 41.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>208</th>\n",
" <td> 209</td>\n",
" <td> F</td>\n",
" <td> 146.92</td>\n",
" <td> 43.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>201</th>\n",
" <td> 202</td>\n",
" <td> F</td>\n",
" <td> 146.67</td>\n",
" <td> 43.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188</th>\n",
" <td> 189</td>\n",
" <td> M</td>\n",
" <td> 146.48</td>\n",
" <td> 56.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>399</th>\n",
" <td> 400</td>\n",
" <td> F</td>\n",
" <td> 146.28</td>\n",
" <td> 42.26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53 </th>\n",
" <td> 54</td>\n",
" <td> M</td>\n",
" <td> 146.00</td>\n",
" <td> 50.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td> 120</td>\n",
" <td> M</td>\n",
" <td> 145.97</td>\n",
" <td> 55.62</td>\n",
" </tr>\n",
" <tr>\n",
" <th>247</th>\n",
" <td> 248</td>\n",
" <td> F</td>\n",
" <td> 145.80</td>\n",
" <td> 45.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>142</th>\n",
" <td> 143</td>\n",
" <td> M</td>\n",
" <td> 145.63</td>\n",
" <td> 55.43</td>\n",
" </tr>\n",
" <tr>\n",
" <th>260</th>\n",
" <td> 261</td>\n",
" <td> F</td>\n",
" <td> 145.48</td>\n",
" <td> 47.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>393</th>\n",
" <td> 394</td>\n",
" <td> F</td>\n",
" <td> 145.48</td>\n",
" <td> 45.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90 </th>\n",
" <td> 91</td>\n",
" <td> M</td>\n",
" <td> 145.10</td>\n",
" <td> 50.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>237</th>\n",
" <td> 238</td>\n",
" <td> F</td>\n",
" <td> 145.02</td>\n",
" <td> 40.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>244</th>\n",
" <td> 245</td>\n",
" <td> F</td>\n",
" <td> 144.47</td>\n",
" <td> 44.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>286</th>\n",
" <td> 287</td>\n",
" <td> F</td>\n",
" <td> 144.07</td>\n",
" <td> 42.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>364</th>\n",
" <td> 365</td>\n",
" <td> F</td>\n",
" <td> 143.79</td>\n",
" <td> 43.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>227</th>\n",
" <td> 228</td>\n",
" <td> F</td>\n",
" <td> 143.75</td>\n",
" <td> 43.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>221</th>\n",
" <td> 222</td>\n",
" <td> F</td>\n",
" <td> 143.69</td>\n",
" <td> 45.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>266</th>\n",
" <td> 267</td>\n",
" <td> F</td>\n",
" <td> 143.25</td>\n",
" <td> 45.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>251</th>\n",
" <td> 252</td>\n",
" <td> F</td>\n",
" <td> 143.17</td>\n",
" <td> 39.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>257</th>\n",
" <td> 258</td>\n",
" <td> F</td>\n",
" <td> 143.00</td>\n",
" <td> 38.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>242</th>\n",
" <td> 243</td>\n",
" <td> F</td>\n",
" <td> 142.09</td>\n",
" <td> 43.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>324</th>\n",
" <td> 325</td>\n",
" <td> F</td>\n",
" <td> 141.62</td>\n",
" <td> 39.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390</th>\n",
" <td> 391</td>\n",
" <td> F</td>\n",
" <td> 141.49</td>\n",
" <td> 41.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>246</th>\n",
" <td> 247</td>\n",
" <td> F</td>\n",
" <td> 141.10</td>\n",
" <td> 35.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>281</th>\n",
" <td> 282</td>\n",
" <td> F</td>\n",
" <td> 140.53</td>\n",
" <td> 41.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>209</th>\n",
" <td> 210</td>\n",
" <td> F</td>\n",
" <td> 137.89</td>\n",
" <td> 40.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>282</th>\n",
" <td> 283</td>\n",
" <td> F</td>\n",
" <td> 136.85</td>\n",
" <td> 31.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>269</th>\n",
" <td> 270</td>\n",
" <td> F</td>\n",
" <td> 136.59</td>\n",
" <td> 36.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>323</th>\n",
" <td> 324</td>\n",
" <td> F</td>\n",
" <td> 135.51</td>\n",
" <td> 33.07</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>400 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
" id gender height weight\n",
"76 77 M 181.53 77.60\n",
"124 125 M 179.17 78.56\n",
"112 113 M 178.80 69.24\n",
"1 2 M 178.76 72.38\n",
"116 117 M 177.17 78.99\n",
"154 155 M 177.06 74.68\n",
"44 45 M 176.34 75.02\n",
"38 39 M 176.24 73.56\n",
"183 184 M 176.07 74.48\n",
"190 191 M 175.74 75.60\n",
"109 110 M 174.10 67.76\n",
"167 168 M 174.07 72.92\n",
"187 188 M 173.79 75.48\n",
"328 329 F 173.76 66.17\n",
"19 20 M 172.97 70.98\n",
"134 135 M 172.57 77.34\n",
"82 83 M 172.43 69.58\n",
"133 134 M 172.33 66.38\n",
"197 198 M 172.24 69.80\n",
"97 98 M 172.19 68.83\n",
"161 162 M 171.95 69.59\n",
"70 71 M 171.86 77.23\n",
"128 129 M 171.86 76.90\n",
"60 61 M 171.81 68.40\n",
"7 8 M 171.78 67.76\n",
"126 127 M 171.40 68.27\n",
"15 16 M 171.33 76.02\n",
"59 60 M 171.29 70.13\n",
"113 114 M 171.11 77.74\n",
"93 94 M 171.05 70.21\n",
".. ... ... ... ...\n",
"283 284 F 147.27 41.88\n",
"208 209 F 146.92 43.73\n",
"201 202 F 146.67 43.98\n",
"188 189 M 146.48 56.07\n",
"399 400 F 146.28 42.26\n",
"53 54 M 146.00 50.69\n",
"119 120 M 145.97 55.62\n",
"247 248 F 145.80 45.37\n",
"142 143 M 145.63 55.43\n",
"260 261 F 145.48 47.71\n",
"393 394 F 145.48 45.13\n",
"90 91 M 145.10 50.49\n",
"237 238 F 145.02 40.22\n",
"244 245 F 144.47 44.17\n",
"286 287 F 144.07 42.68\n",
"364 365 F 143.79 43.30\n",
"227 228 F 143.75 43.67\n",
"221 222 F 143.69 45.75\n",
"266 267 F 143.25 45.40\n",
"251 252 F 143.17 39.01\n",
"257 258 F 143.00 38.47\n",
"242 243 F 142.09 43.59\n",
"324 325 F 141.62 39.77\n",
"390 391 F 141.49 41.07\n",
"246 247 F 141.10 35.02\n",
"281 282 F 140.53 41.28\n",
"209 210 F 137.89 40.85\n",
"282 283 F 136.85 31.44\n",
"269 270 F 136.59 36.07\n",
"323 324 F 135.51 33.07\n",
"\n",
"[400 rows x 4 columns]"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#list4 \u6570\u5024\u8981\u7d04\uff08R\u306esummary\uff09\n",
"body_data.describe ()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>height</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td> 400.000000</td>\n",
" <td> 400.000000</td>\n",
" <td> 400.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td> 200.500000</td>\n",
" <td> 158.368625</td>\n",
" <td> 58.161525</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td> 115.614301</td>\n",
" <td> 8.210723</td>\n",
" <td> 9.168719</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td> 1.000000</td>\n",
" <td> 135.510000</td>\n",
" <td> 31.440000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td> 100.750000</td>\n",
" <td> 152.397500</td>\n",
" <td> 50.930000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td> 200.500000</td>\n",
" <td> 158.240000</td>\n",
" <td> 57.785000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td> 300.250000</td>\n",
" <td> 163.862500</td>\n",
" <td> 65.525000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td> 400.000000</td>\n",
" <td> 181.530000</td>\n",
" <td> 78.990000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
" id height weight\n",
"count 400.000000 400.000000 400.000000\n",
"mean 200.500000 158.368625 58.161525\n",
"std 115.614301 8.210723 9.168719\n",
"min 1.000000 135.510000 31.440000\n",
"25% 100.750000 152.397500 50.930000\n",
"50% 200.500000 158.240000 57.785000\n",
"75% 300.250000 163.862500 65.525000\n",
"max 400.000000 181.530000 78.990000"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list5"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"body_data.height.std() # \u6a19\u6e96\u504f\u5dee"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"8.2107232507360379"
]
}
],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"body_data.weight.var() # \u4e0d\u504f\u5206\u6563"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"84.065404936723198"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list6 \u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\n",
"plt.hist(body_data.height, bins=30)\n",
"plt.xlabel(\"height\")\n",
"plt.ylabel(\"count\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEPCAYAAABCyrPIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1128057d0>"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list7 \u7537\u5973\u5225\u306b\u8272\u5206\u3051\u3057\u305f\u30d2\u30b9\u30c8\u30b0\u30e9\u30e0\n",
"body_data_f = body_data[body_data.gender == \"F\"]\n",
"plt.hist(body_data.height.values, bins=30, color=\"cyan\")\n",
"plt.hist(body_data_f.height.values, bins=30, color=\"magenta\")\n",
"plt.xlabel(\"height\")\n",
"plt.ylabel(\"count\")\n",
"plt.legend([\"M\", \"F\"], title=\"gender\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x112945550>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# sample \u82e5\u5e72\u30aa\u30d7\u30b7\u30e7\u30f3\u3092\u3044\u3058\u3063\u3066\u307f\u308b\n",
"# http://matplotlib.org/api/pyplot_api.html?highlight=hist#matplotlib.pyplot.hist\n",
"body_data_m = body_data[body_data.gender == \"M\"]\n",
"body_data_f = body_data[body_data.gender == \"F\"]\n",
"plt.hist(body_data.height.values, bins=30, color=\"yellow\", alpha=.2)\n",
"plt.hist(body_data_m.height.values, bins=30, color=\"cyan\", alpha=.5)\n",
"plt.hist(body_data_f.height.values, bins=30, color=\"magenta\", alpha=.5)\n",
"plt.xlabel(\"height\")\n",
"plt.ylabel(\"count\")\n",
"plt.legend([\"all\", \"M\", \"F\"], title=\"gender\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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tewT4HNgLxP5xx/6LVL6AX2C2l4wM25YM5fNi9hbbYb2uDduXDOUDeAD4DNhN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\nrkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1128974d0>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list8 \u8eab\u9577\u30c7\u30fc\u30bf\u306e\u7bb1\u3072\u3052\u56f3\n",
"body_data_f = body_data[body_data.gender == \"F\"]\n",
"body_data_m = body_data[body_data.gender == \"M\"]\n",
"plt.xlabel(\"gender\")\n",
"plt.boxplot([body_data_f.height, body_data_m.height])\n",
"plt.xticks([1, 2], ['F', 'M'])\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fy5PAJ3Jtfl4lSZIkSZIkSZIkSZIkSZJGx6vK7oA04rYT/p08V3I/pHl5QpDUXq8nszg4\n0tAZ5KqSPyRcZnAfsAP4IPB64FHgKeArwBvivtuBvwS+Sri+7C/G9hrhjMQjwBcIlzKcXYL1zUA9\nvtce4Ediex34c+BJ4APF/1qSNB5WE5b0XQRcAfwHIcj3AjfEfW4Cvhi3txOWWwX4MeBo3H4P8HlC\neL+OsF7Ie4DLgK8BV8b9bgPuj9v/wtzp6NLQuWiWquKngUcIFwo/S7i02OXATwH/kNtvUbxvxP0h\n1L9/OG6/lTCabwDfBr4U299AuPLN3vj4VYQLk8/6DFJJDHJVRYNXXoXmEuBF5l+3/Wxue/a1rd5n\n1rOEPwytfK+LPkoDYY1cVfFV4OcJV6W5Avg5wnVNnwd+Ke5TA97Y4X2+QiibXEIordwS279BWFf7\n5vj4MmCyoL5LC2KQqyqeIlzM4GnCNSMPEUbjvwq8HzgAPAO8O/eaRovtfyTUyw8DDxLq4gDnCH8Q\nPhbfaz/wlgH8HpI01r4/3r+GMINkVYl9kYbGGrmq5NOEcsflhFkpB0rtjSRJkiRJkiRJkiRJkiRJ\nkjSa/g9gi/6vkuNhZQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x11283e5d0>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list9 \u8eab\u9577\u3068\u4f53\u91cd\u306e\u6563\u5e03\u56f3\n",
"plt.scatter(body_data.height, body_data.weight, s=1)\n",
"plt.xlabel(\"height\")\n",
"plt.ylabel(\"weight\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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3lLlzv8ow35Aho8XdvXCWZmQ9fPiwuLl5yBtv/EsKFy6W4eR158+fz3DE0Ftv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\nAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1127cd590>"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list10 \u8eab\u9577\u3068\u4f53\u91cd\u306e\u6563\u5e03\u56f3+\u56de\u5e30\u76f4\u7dda\n",
"from sklearn import linear_model\n",
"LinerRegr = linear_model.LinearRegression()\n",
"x = body_data[[\"height\"]]\n",
"y = body_data[[\"weight\"]]\n",
"LinerRegr.fit(x, y)\n",
"plt.scatter(x, y, s=1)\n",
"px = np.arange(x.min(), x.max(), .01)[:,np.newaxis]\n",
"py = LinerRegr.predict(px)\n",
"plt.plot(px, py, color=\"blue\", linewidth=2)\n",
"plt.xlabel(\"height\")\n",
"plt.ylabel(\"weight\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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JHMjD4KCUTSpXrkDp0s9RrFixJzrfzc2Nvn37Wrdfe60zUVHRPPvss3HS+fr6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\nZmZmZmZmZmZmZmZmZm3zH0Vdx18vBzq2AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x112af40d0>"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list11 \u7537\u5973\u5225\u306e\u8eab\u9577\u3068\u4f53\u91cd\u306e\u6563\u5e03\u56f3+\u56de\u5e30\u76f4\u7dda\n",
"from sklearn import linear_model\n",
"body_data_f = body_data[body_data.gender == \"F\"]\n",
"body_data_m = body_data[body_data.gender == \"M\"]\n",
"\n",
"x = body_data[[\"height\"]]\n",
"y = body_data[[\"weight\"]]\n",
"plt.scatter(x, y, s=1)\n",
"\n",
"def plot_linear_regression(data, x_variable=\"height\", y_variable=\"weight\", color=\"black\", linewidth=2):\n",
" x = data[[x_variable]]\n",
" y = data[[y_variable]]\n",
" liner_regr = linear_model.LinearRegression()\n",
" liner_regr.fit(x, y)\n",
" px = np.arange(x.min(), x.max(), .01)[:,np.newaxis]\n",
" py = liner_regr.predict(px)\n",
" plt.plot(px, py, color=color, linewidth=linewidth)\n",
"\n",
"plot_linear_regression(data=body_data_m, color=\"cyan\")\n",
"plot_linear_regression(data=body_data_f, color=\"pink\")\n",
"\n",
"plt.legend([\"M\", \"F\"], title=\"gender\", loc='lower right')\n",
"plt.xlabel(\"height\")\n",
"plt.ylabel(\"weight\")\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Lxd/fX4xGo3z++RdFLirv5dVZANm1a5eIiBiz9OL765+S6eOXM7s44IxIYpJ2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\niIiIiIiIiIiIiIiIiIiIiIiIBMz/AXp/zWhVavKIAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x11283ec90>"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# list12 \u76f8\u95a2\u4fc2\u6570\u306e\u7b97\u51fa"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 24
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## \u5168\u4f53\n",
"corr_of_all = np.corrcoef(body_data.height, body_data.weight)\n",
"corr_of_all[0, 1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 25,
"text": [
"0.8928747678201534"
]
}
],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## \u7537\u6027\n",
"body_data_m = body_data[body_data.gender == \"M\"]\n",
"corr_of_m = np.corrcoef(body_data_m.height, body_data_m.weight)\n",
"corr_of_m[0, 1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 26,
"text": [
"0.86345697571511137"
]
}
],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"## \u5973\u6027\n",
"body_data_f = body_data[body_data.gender == \"F\"]\n",
"corr_of_f = np.corrcoef(body_data_f.height, body_data_f.weight)\n",
"corr_of_f[0, 1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 27,
"text": [
"0.91735993883788636"
]
}
],
"prompt_number": 27
}
],
"metadata": {}
}
]
}
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