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entropy_summer_school
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "code", | |
| "execution_count": 1, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "%matplotlib inline\n", | |
| "import matplotlib.pyplot as plt\n", | |
| "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n", | |
| "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 2, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "from scipy.stats import entropy" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 3, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "import pandas as pd" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df = pd.read_excel('学生信息1.xlsx', header=1) # 读取表格数据" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 5, | |
| "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>姓名</th>\n", | |
| " <th>性别</th>\n", | |
| " <th>院系</th>\n", | |
| " <th>在学年级</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>周娜</td>\n", | |
| " <td>女</td>\n", | |
| " <td>心理与认知科学学院</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>周行健</td>\n", | |
| " <td>男</td>\n", | |
| " <td>数学科学学院</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>孙怡宁</td>\n", | |
| " <td>女</td>\n", | |
| " <td>哲学系</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>岑仕鹏</td>\n", | |
| " <td>男</td>\n", | |
| " <td>信息科学技术学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>朱元虎</td>\n", | |
| " <td>男</td>\n", | |
| " <td>中国语言文学系</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " 姓名 性别 院系 在学年级\n", | |
| "0 周娜 女 心理与认知科学学院 2017\n", | |
| "1 周行健 男 数学科学学院 2017\n", | |
| "2 孙怡宁 女 哲学系 2017\n", | |
| "3 岑仕鹏 男 信息科学技术学院 2018\n", | |
| "4 朱元虎 男 中国语言文学系 2017" | |
| ] | |
| }, | |
| "execution_count": 5, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df.head()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "## 将其它学校归为一类，判断条件是院系列包含大学" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 6, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "df['院系'] = df['院系'].apply(lambda x: '其它大学' if '大学' in x else x) " | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 7, | |
| "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>姓名</th>\n", | |
| " <th>性别</th>\n", | |
| " <th>院系</th>\n", | |
| " <th>在学年级</th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>0</th>\n", | |
| " <td>周娜</td>\n", | |
| " <td>女</td>\n", | |
| " <td>心理与认知科学学院</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>1</th>\n", | |
| " <td>周行健</td>\n", | |
| " <td>男</td>\n", | |
| " <td>数学科学学院</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2</th>\n", | |
| " <td>孙怡宁</td>\n", | |
| " <td>女</td>\n", | |
| " <td>哲学系</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>3</th>\n", | |
| " <td>岑仕鹏</td>\n", | |
| " <td>男</td>\n", | |
| " <td>信息科学技术学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>4</th>\n", | |
| " <td>朱元虎</td>\n", | |
| " <td>男</td>\n", | |
| " <td>中国语言文学系</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>5</th>\n", | |
| " <td>严牧心</td>\n", | |
| " <td>女</td>\n", | |
| " <td>外国语学院</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>6</th>\n", | |
| " <td>匡然</td>\n", | |
| " <td>女</td>\n", | |
| " <td>外国语学院</td>\n", | |
| " <td>2017</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>7</th>\n", | |
| " <td>李一笑</td>\n", | |
| " <td>男</td>\n", | |
| " <td>数学科学学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>8</th>\n", | |
| " <td>薛睿</td>\n", | |
| " <td>男</td>\n", | |
| " <td>工学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>9</th>\n", | |
| " <td>马泽宇</td>\n", | |
| " <td>男</td>\n", | |
| " <td>物理学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>10</th>\n", | |
| " <td>杨秀金</td>\n", | |
| " <td>男</td>\n", | |
| " <td>生命科学学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>11</th>\n", | |
| " <td>周妍</td>\n", | |
| " <td>女</td>\n", | |
| " <td>历史学系</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>12</th>\n", | |
| " <td>王伊姗</td>\n", | |
| " <td>女</td>\n", | |
| " <td>哲学系</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>13</th>\n", | |
| " <td>张馨语</td>\n", | |
| " <td>女</td>\n", | |
| " <td>光华管理学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>14</th>\n", | |
| " <td>谭畅</td>\n", | |
| " <td>男</td>\n", | |
| " <td>工学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>15</th>\n", | |
| " <td>丁月迪</td>\n", | |
| " <td>女</td>\n", | |
| " <td>政府管理学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>16</th>\n", | |
| " <td>王金晶</td>\n", | |
| " <td>女</td>\n", | |
| " <td>外国语学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>17</th>\n", | |
| " <td>王韵涵</td>\n", | |
| " <td>女</td>\n", | |
| " <td>外国语学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>18</th>\n", | |
| " <td>李武陶文</td>\n", | |
| " <td>男</td>\n", | |
| " <td>外国语学院</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>19</th>\n", | |
| " <td>薄瑞</td>\n", | |
| " <td>男</td>\n", | |
| " <td>工学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>20</th>\n", | |
| " <td>张润祺</td>\n", | |
| " <td>女</td>\n", | |
| " <td>工学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>21</th>\n", | |
| " <td>马小凯</td>\n", | |
| " <td>男</td>\n", | |
| " <td>生命科学学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>22</th>\n", | |
| " <td>汪家震</td>\n", | |
| " <td>男</td>\n", | |
| " <td>生命科学学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>23</th>\n", | |
| " <td>吴军</td>\n", | |
| " <td>男</td>\n", | |
| " <td>生命科学学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>24</th>\n", | |
| " <td>刘家铭</td>\n", | |
| " <td>男</td>\n", | |
| " <td>生命科学学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>25</th>\n", | |
| " <td>谢奥林</td>\n", | |
| " <td>男</td>\n", | |
| " <td>生命科学学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>26</th>\n", | |
| " <td>李泊宁</td>\n", | |
| " <td>男</td>\n", | |
| " <td>生命科学学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>27</th>\n", | |
| " <td>侯东林</td>\n", | |
| " <td>男</td>\n", | |
| " <td>信息科学技术学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>28</th>\n", | |
| " <td>赵海波</td>\n", | |
| " <td>男</td>\n", | |
| " <td>信息科学技术学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>29</th>\n", | |
| " <td>陈福康</td>\n", | |
| " <td>男</td>\n", | |
| " <td>信息科学技术学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>30</th>\n", | |
| " <td>贺伟桓</td>\n", | |
| " <td>男</td>\n", | |
| " <td>城市与环境学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>31</th>\n", | |
| " <td>刘泽熙</td>\n", | |
| " <td>男</td>\n", | |
| " <td>心理与认知科学学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>32</th>\n", | |
| " <td>邱春水</td>\n", | |
| " <td>男</td>\n", | |
| " <td>历史学系</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>33</th>\n", | |
| " <td>杜心怡</td>\n", | |
| " <td>女</td>\n", | |
| " <td>考古文博学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>34</th>\n", | |
| " <td>吴添</td>\n", | |
| " <td>男</td>\n", | |
| " <td>经济学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>35</th>\n", | |
| " <td>钟艳琦</td>\n", | |
| " <td>女</td>\n", | |
| " <td>光华管理学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>36</th>\n", | |
| " <td>于灏轩</td>\n", | |
| " <td>男</td>\n", | |
| " <td>社会学系</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>37</th>\n", | |
| " <td>宋汶航</td>\n", | |
| " <td>男</td>\n", | |
| " <td>元培学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>38</th>\n", | |
| " <td>乐江立</td>\n", | |
| " <td>男</td>\n", | |
| " <td>元培学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>39</th>\n", | |
| " <td>孙世元</td>\n", | |
| " <td>男</td>\n", | |
| " <td>元培学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>40</th>\n", | |
| " <td>叶风灿</td>\n", | |
| " <td>男</td>\n", | |
| " <td>元培学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>41</th>\n", | |
| " <td>鄭曉琳</td>\n", | |
| " <td>女</td>\n", | |
| " <td>外国语学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>42</th>\n", | |
| " <td>周心宁</td>\n", | |
| " <td>女</td>\n", | |
| " <td>外国语学院</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>43</th>\n", | |
| " <td>刘俊智</td>\n", | |
| " <td>男</td>\n", | |
| " <td>医学部教学办</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>44</th>\n", | |
| " <td>邢一泓</td>\n", | |
| " <td>男</td>\n", | |
| " <td>医学部教学办</td>\n", | |
| " <td>2019</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>45</th>\n", | |
| " <td>罗京</td>\n", | |
| " <td>男</td>\n", | |
| " <td>其它大学</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>46</th>\n", | |
| " <td>郭瑞元</td>\n", | |
| " <td>女</td>\n", | |
| " <td>其它大学</td>\n", | |
| " <td>2018</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " 姓名 性别 院系 在学年级\n", | |
| "0 周娜 女 心理与认知科学学院 2017\n", | |
| "1 周行健 男 数学科学学院 2017\n", | |
| "2 孙怡宁 女 哲学系 2017\n", | |
| "3 岑仕鹏 男 信息科学技术学院 2018\n", | |
| "4 朱元虎 男 中国语言文学系 2017\n", | |
| "5 严牧心 女 外国语学院 2017\n", | |
| "6 匡然 女 外国语学院 2017\n", | |
| "7 李一笑 男 数学科学学院 2018\n", | |
| "8 薛睿 男 工学院 2018\n", | |
| "9 马泽宇 男 物理学院 2018\n", | |
| "10 杨秀金 男 生命科学学院 2018\n", | |
| "11 周妍 女 历史学系 2018\n", | |
| "12 王伊姗 女 哲学系 2018\n", | |
| "13 张馨语 女 光华管理学院 2018\n", | |
| "14 谭畅 男 工学院 2018\n", | |
| "15 丁月迪 女 政府管理学院 2018\n", | |
| "16 王金晶 女 外国语学院 2019\n", | |
| "17 王韵涵 女 外国语学院 2018\n", | |
| "18 李武陶文 男 外国语学院 2018\n", | |
| "19 薄瑞 男 工学院 2019\n", | |
| "20 张润祺 女 工学院 2019\n", | |
| "21 马小凯 男 生命科学学院 2019\n", | |
| "22 汪家震 男 生命科学学院 2019\n", | |
| "23 吴军 男 生命科学学院 2019\n", | |
| "24 刘家铭 男 生命科学学院 2019\n", | |
| "25 谢奥林 男 生命科学学院 2019\n", | |
| "26 李泊宁 男 生命科学学院 2019\n", | |
| "27 侯东林 男 信息科学技术学院 2019\n", | |
| "28 赵海波 男 信息科学技术学院 2019\n", | |
| "29 陈福康 男 信息科学技术学院 2019\n", | |
| "30 贺伟桓 男 城市与环境学院 2019\n", | |
| "31 刘泽熙 男 心理与认知科学学院 2019\n", | |
| "32 邱春水 男 历史学系 2019\n", | |
| "33 杜心怡 女 考古文博学院 2019\n", | |
| "34 吴添 男 经济学院 2019\n", | |
| "35 钟艳琦 女 光华管理学院 2019\n", | |
| "36 于灏轩 男 社会学系 2019\n", | |
| "37 宋汶航 男 元培学院 2019\n", | |
| "38 乐江立 男 元培学院 2019\n", | |
| "39 孙世元 男 元培学院 2019\n", | |
| "40 叶风灿 男 元培学院 2019\n", | |
| "41 鄭曉琳 女 外国语学院 2019\n", | |
| "42 周心宁 女 外国语学院 2019\n", | |
| "43 刘俊智 男 医学部教学办 2019\n", | |
| "44 邢一泓 男 医学部教学办 2019\n", | |
| "45 罗京 男 其它大学 2018\n", | |
| "46 郭瑞元 女 其它大学 2018" | |
| ] | |
| }, | |
| "execution_count": 7, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "df" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "\n", | |
| "依据公式$S=-whereum F_ilnF_i$，其中取$K_B=1$\n", | |
| "\n", | |
| "因此计算熵可采用 entropy 函数，其定义如下\n", | |
| "\n", | |
| "scipy.stats.entropy(pk, qk=None, base=None, axis=0)，base 默认值是 e\n", | |
| "\n", | |
| "If only probabilities pk are given, the entropy is calculated as S = -sum(pk * log(pk), axis=axis).\n" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# 按学院(校)分" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 8, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "dept_col = df.loc[:, '院系'].value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "外国语学院 7\n", | |
| "生命科学学院 7\n", | |
| "元培学院 4\n", | |
| "工学院 4\n", | |
| "信息科学技术学院 4\n", | |
| "历史学系 2\n", | |
| "心理与认知科学学院 2\n", | |
| "其它大学 2\n", | |
| "光华管理学院 2\n", | |
| "医学部教学办 2\n", | |
| "数学科学学院 2\n", | |
| "哲学系 2\n", | |
| "物理学院 1\n", | |
| "考古文博学院 1\n", | |
| "政府管理学院 1\n", | |
| "经济学院 1\n", | |
| "中国语言文学系 1\n", | |
| "城市与环境学院 1\n", | |
| "社会学系 1\n", | |
| "Name: 院系, dtype: int64" | |
| ] | |
| }, | |
| "execution_count": 9, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "dept_col" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 10, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f54d29fd850>" | |
| ] | |
| }, | |
| "execution_count": 10, | |
| "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": [ | |
| "dept_col.plot.barh()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 11, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "dept_col.p = dept_col.values / dept_col.values.sum()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 12, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "院系(校):外国语学院, 人数:7,占比:0.14893617021276595\n", | |
| "院系(校):生命科学学院, 人数:7,占比:0.14893617021276595\n", | |
| "院系(校):元培学院, 人数:4,占比:0.0851063829787234\n", | |
| "院系(校):工学院, 人数:4,占比:0.0851063829787234\n", | |
| "院系(校):信息科学技术学院, 人数:4,占比:0.0851063829787234\n", | |
| "院系(校):历史学系, 人数:2,占比:0.0425531914893617\n", | |
| "院系(校):心理与认知科学学院, 人数:2,占比:0.0425531914893617\n", | |
| "院系(校):其它大学, 人数:2,占比:0.0425531914893617\n", | |
| "院系(校):光华管理学院, 人数:2,占比:0.0425531914893617\n", | |
| "院系(校):医学部教学办, 人数:2,占比:0.0425531914893617\n", | |
| "院系(校):数学科学学院, 人数:2,占比:0.0425531914893617\n", | |
| "院系(校):哲学系, 人数:2,占比:0.0425531914893617\n", | |
| "院系(校):物理学院, 人数:1,占比:0.02127659574468085\n", | |
| "院系(校):考古文博学院, 人数:1,占比:0.02127659574468085\n", | |
| "院系(校):政府管理学院, 人数:1,占比:0.02127659574468085\n", | |
| "院系(校):经济学院, 人数:1,占比:0.02127659574468085\n", | |
| "院系(校):中国语言文学系, 人数:1,占比:0.02127659574468085\n", | |
| "院系(校):城市与环境学院, 人数:1,占比:0.02127659574468085\n", | |
| "院系(校):社会学系, 人数:1,占比:0.02127659574468085\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for i in range(len(dept_col)):\n", | |
| " print('院系(校):{}, 人数:{},占比:{}'.format(dept_col.index[i], dept_col.values[i], dept_col.p[i]))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 13, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "按院系(校)分熵:2.7100979219642647\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('按院系(校)分熵:{}'.format(entropy(dept_col)))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# 按性别分" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 14, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "sex_col = df.loc[:,'性别'].value_counts()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "data": { | |
| "text/plain": [ | |
| "<matplotlib.axes._subplots.AxesSubplot at 0x7f54d04ff950>" | |
| ] | |
| }, | |
| "execution_count": 15, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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| "text/plain": [ | |
| "<Figure size 432x288 with 1 Axes>" | |
| ] | |
| }, | |
| "metadata": { | |
| "needs_background": "light" | |
| }, | |
| "output_type": "display_data" | |
| } | |
| ], | |
| "source": [ | |
| "sex_col.plot.barh()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 16, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "sex_col.p = sex_col.values/sex_col.values.sum()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 17, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "性别:男, 人数:31,占比:0.6595744680851063\n", | |
| "性别:女, 人数:16,占比:0.3404255319148936\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "for i in range(len(sex_col)):\n", | |
| " print('性别:{}, 人数:{},占比:{}'.format(sex_col.index[i], sex_col.values[i], sex_col.p[i]))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 18, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "性别分的熵:0.641317327350994\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('性别分的熵:{}'.format(entropy(sex_col)))" | |
| ] | |
| }, | |
| { | |
| "cell_type": "markdown", | |
| "metadata": {}, | |
| "source": [ | |
| "# 按学院(校)，性别，年级三个参数分" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 19, | |
| "metadata": {}, | |
| "outputs": [], | |
| "source": [ | |
| "mix = df.groupby(['院系','性别','在学年级']).count()" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 20, | |
| "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></th>\n", | |
| " <th></th>\n", | |
| " <th>姓名</th>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>院系</th>\n", | |
| " <th>性别</th>\n", | |
| " <th>在学年级</th>\n", | |
| " <th></th>\n", | |
| " </tr>\n", | |
| " </thead>\n", | |
| " <tbody>\n", | |
| " <tr>\n", | |
| " <th>中国语言文学系</th>\n", | |
| " <th>男</th>\n", | |
| " <th>2017</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">信息科学技术学院</th>\n", | |
| " <th rowspan=\"2\" valign=\"top\">男</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019</th>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>元培学院</th>\n", | |
| " <th>男</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>4</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">光华管理学院</th>\n", | |
| " <th rowspan=\"2\" valign=\"top\">女</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">其它大学</th>\n", | |
| " <th>女</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>男</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>医学部教学办</th>\n", | |
| " <th>男</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">历史学系</th>\n", | |
| " <th>女</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>男</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">哲学系</th>\n", | |
| " <th rowspan=\"2\" valign=\"top\">女</th>\n", | |
| " <th>2017</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>城市与环境学院</th>\n", | |
| " <th>男</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"4\" valign=\"top\">外国语学院</th>\n", | |
| " <th rowspan=\"3\" valign=\"top\">女</th>\n", | |
| " <th>2017</th>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019</th>\n", | |
| " <td>3</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>男</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"3\" valign=\"top\">工学院</th>\n", | |
| " <th>女</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">男</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>2</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">心理与认知科学学院</th>\n", | |
| " <th>女</th>\n", | |
| " <th>2017</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>男</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>政府管理学院</th>\n", | |
| " <th>女</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">数学科学学院</th>\n", | |
| " <th rowspan=\"2\" valign=\"top\">男</th>\n", | |
| " <th>2017</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>物理学院</th>\n", | |
| " <th>男</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th rowspan=\"2\" valign=\"top\">生命科学学院</th>\n", | |
| " <th rowspan=\"2\" valign=\"top\">男</th>\n", | |
| " <th>2018</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>2019</th>\n", | |
| " <td>6</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>社会学系</th>\n", | |
| " <th>男</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>经济学院</th>\n", | |
| " <th>男</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " <tr>\n", | |
| " <th>考古文博学院</th>\n", | |
| " <th>女</th>\n", | |
| " <th>2019</th>\n", | |
| " <td>1</td>\n", | |
| " </tr>\n", | |
| " </tbody>\n", | |
| "</table>\n", | |
| "</div>" | |
| ], | |
| "text/plain": [ | |
| " 姓名\n", | |
| "院系 性别 在学年级 \n", | |
| "中国语言文学系 男 2017 1\n", | |
| "信息科学技术学院 男 2018 1\n", | |
| " 2019 3\n", | |
| "元培学院 男 2019 4\n", | |
| "光华管理学院 女 2018 1\n", | |
| " 2019 1\n", | |
| "其它大学 女 2018 1\n", | |
| " 男 2018 1\n", | |
| "医学部教学办 男 2019 2\n", | |
| "历史学系 女 2018 1\n", | |
| " 男 2019 1\n", | |
| "哲学系 女 2017 1\n", | |
| " 2018 1\n", | |
| "城市与环境学院 男 2019 1\n", | |
| "外国语学院 女 2017 2\n", | |
| " 2018 1\n", | |
| " 2019 3\n", | |
| " 男 2018 1\n", | |
| "工学院 女 2019 1\n", | |
| " 男 2018 2\n", | |
| " 2019 1\n", | |
| "心理与认知科学学院 女 2017 1\n", | |
| " 男 2019 1\n", | |
| "政府管理学院 女 2018 1\n", | |
| "数学科学学院 男 2017 1\n", | |
| " 2018 1\n", | |
| "物理学院 男 2018 1\n", | |
| "生命科学学院 男 2018 1\n", | |
| " 2019 6\n", | |
| "社会学系 男 2019 1\n", | |
| "经济学院 男 2019 1\n", | |
| "考古文博学院 女 2019 1" | |
| ] | |
| }, | |
| "execution_count": 20, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ], | |
| "source": [ | |
| "mix" | |
| ] | |
| }, | |
| { | |
| "cell_type": "code", | |
| "execution_count": 21, | |
| "metadata": {}, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": [ | |
| "按学院(校)，性别，年级三个参数分的熵:[3.2746946]\n" | |
| ] | |
| } | |
| ], | |
| "source": [ | |
| "print('按学院(校)，性别，年级三个参数分的熵:{}'.format(entropy(mix)))" | |
| ] | |
| } | |
| ], | |
| "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 | |
| } |
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