【Kaggle数据分析实战练习】World University Rankings

素颜马尾好姑娘i 2023-01-21 10:19 19阅读 0赞

# 数据集介绍 #

本次数据分析的数据集来自Kaggle的[World University Rankings][]的`cwurData.csv`。数据集共包含2201行14列（含标题行），官方给出的每列的描述如下：

*  world\_rank: world rank for university
 *  institution: name of university
 *  country: country of each university
 *  national\_rank: rank of university within its country
 *  quality\_of\_education: rank for quality of education
 *  alumni\_employment: rank for alumni employment
 *  quality\_of\_faculty: rank for quality of faculty
 *  publications: rank for publications
 *  influence: rank for influence
 *  citations: number of students at the university
 *  broad\_impact: rank for broad impact (only available for 2014 and 2015)
 *  patents: rank for patents
 *  score: total score, used for determining world rank
 *  year: year of ranking (2012 to 2015)

# 初步探索数据集 #

import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    import seaborn as sns
    %matplotlib inline

df = pd.read_csv('./data/cwurData.csv', encoding='utf-8')

df.describe()

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
   <th>score</th> 
   <th>year</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>count</th> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
   <td>2000.000000</td> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
   <td>2200.000000</td> 
  </tr> 
  <tr> 
   <th>mean</th> 
   <td>459.590909</td> 
   <td>40.278182</td> 
   <td>275.100455</td> 
   <td>357.116818</td> 
   <td>178.888182</td> 
   <td>459.908636</td> 
   <td>459.797727</td> 
   <td>413.417273</td> 
   <td>496.699500</td> 
   <td>433.346364</td> 
   <td>47.798395</td> 
   <td>2014.318182</td> 
  </tr> 
  <tr> 
   <th>std</th> 
   <td>304.320363</td> 
   <td>51.740870</td> 
   <td>121.935100</td> 
   <td>186.779252</td> 
   <td>64.050885</td> 
   <td>303.760352</td> 
   <td>303.331822</td> 
   <td>264.366549</td> 
   <td>286.919755</td> 
   <td>273.996525</td> 
   <td>7.760806</td> 
   <td>0.762130</td> 
  </tr> 
  <tr> 
   <th>min</th> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>1.000000</td> 
   <td>43.360000</td> 
   <td>2012.000000</td> 
  </tr> 
  <tr> 
   <th>25%</th> 
   <td>175.750000</td> 
   <td>6.000000</td> 
   <td>175.750000</td> 
   <td>175.750000</td> 
   <td>175.750000</td> 
   <td>175.750000</td> 
   <td>175.750000</td> 
   <td>161.000000</td> 
   <td>250.500000</td> 
   <td>170.750000</td> 
   <td>44.460000</td> 
   <td>2014.000000</td> 
  </tr> 
  <tr> 
   <th>50%</th> 
   <td>450.500000</td> 
   <td>21.000000</td> 
   <td>355.000000</td> 
   <td>450.500000</td> 
   <td>210.000000</td> 
   <td>450.500000</td> 
   <td>450.500000</td> 
   <td>406.000000</td> 
   <td>496.000000</td> 
   <td>426.000000</td> 
   <td>45.100000</td> 
   <td>2014.000000</td> 
  </tr> 
  <tr> 
   <th>75%</th> 
   <td>725.250000</td> 
   <td>49.000000</td> 
   <td>367.000000</td> 
   <td>478.000000</td> 
   <td>218.000000</td> 
   <td>725.000000</td> 
   <td>725.250000</td> 
   <td>645.000000</td> 
   <td>741.000000</td> 
   <td>714.250000</td> 
   <td>47.545000</td> 
   <td>2015.000000</td> 
  </tr> 
  <tr> 
   <th>max</th> 
   <td>1000.000000</td> 
   <td>229.000000</td> 
   <td>367.000000</td> 
   <td>567.000000</td> 
   <td>218.000000</td> 
   <td>1000.000000</td> 
   <td>991.000000</td> 
   <td>812.000000</td> 
   <td>1000.000000</td> 
   <td>871.000000</td> 
   <td>100.000000</td> 
   <td>2015.000000</td> 
  </tr> 
 </tbody> 
</table>

df.head()

df.info()

<class 'pandas.core.frame.DataFrame'>
    RangeIndex: 2200 entries, 0 to 2199
    Data columns (total 14 columns):
     #   Column                Non-Null Count  Dtype  
    ---  ------                --------------  -----  
     0   world_rank            2200 non-null   int64  
     1   institution           2200 non-null   object 
     2   country               2200 non-null   object 
     3   national_rank         2200 non-null   int64  
     4   quality_of_education  2200 non-null   int64  
     5   alumni_employment     2200 non-null   int64  
     6   quality_of_faculty    2200 non-null   int64  
     7   publications          2200 non-null   int64  
     8   influence             2200 non-null   int64  
     9   citations             2200 non-null   int64  
     10  broad_impact          2000 non-null   float64
     11  patents               2200 non-null   int64  
     12  score                 2200 non-null   float64
     13  year                  2200 non-null   int64  
    dtypes: float64(2), int64(10), object(2)
    memory usage: 240.8+ KB

set(df['year'])

{2012, 2013, 2014, 2015}

我们可以看到，这份数据集只有`broad_impact`一列有缺失值，但缺失不是很多；`year`一列的范围是`2012-2015`，接下来我们可以依据年份分别对这些数据进行分析。

# 2012年数据分析 #

首先筛选出`year`为`2012`的数据`data_2012`。

data_2012 = df[df['year'] == 2012].drop('year', axis=1)
    data_2012

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
   <th>score</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>7</td> 
   <td>9</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>NaN</td> 
   <td>5</td> 
   <td>100.00</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>2</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>2</td> 
   <td>9</td> 
   <td>17</td> 
   <td>3</td> 
   <td>12</td> 
   <td>4</td> 
   <td>4</td> 
   <td>NaN</td> 
   <td>1</td> 
   <td>91.67</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>3</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>3</td> 
   <td>17</td> 
   <td>11</td> 
   <td>5</td> 
   <td>4</td> 
   <td>2</td> 
   <td>2</td> 
   <td>NaN</td> 
   <td>15</td> 
   <td>89.50</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>4</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>1</td> 
   <td>10</td> 
   <td>24</td> 
   <td>4</td> 
   <td>16</td> 
   <td>16</td> 
   <td>11</td> 
   <td>NaN</td> 
   <td>50</td> 
   <td>86.17</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>5</td> 
   <td>California Institute of Technology</td> 
   <td>USA</td> 
   <td>4</td> 
   <td>2</td> 
   <td>29</td> 
   <td>7</td> 
   <td>37</td> 
   <td>22</td> 
   <td>22</td> 
   <td>NaN</td> 
   <td>18</td> 
   <td>85.21</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>95</th> 
   <td>96</td> 
   <td>University of Texas MD Anderson Cancer Center</td> 
   <td>USA</td> 
   <td>58</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>95</td> 
   <td>46</td> 
   <td>66</td> 
   <td>NaN</td> 
   <td>100</td> 
   <td>43.88</td> 
  </tr> 
  <tr> 
   <th>96</th> 
   <td>97</td> 
   <td>University of Nottingham</td> 
   <td>United Kingdom</td> 
   <td>6</td> 
   <td>101</td> 
   <td>101</td> 
   <td>87</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>NaN</td> 
   <td>92</td> 
   <td>43.79</td> 
  </tr> 
  <tr> 
   <th>97</th> 
   <td>98</td> 
   <td>University of Bristol</td> 
   <td>United Kingdom</td> 
   <td>7</td> 
   <td>101</td> 
   <td>101</td> 
   <td>78</td> 
   <td>75</td> 
   <td>81</td> 
   <td>86</td> 
   <td>NaN</td> 
   <td>101</td> 
   <td>43.77</td> 
  </tr> 
  <tr> 
   <th>98</th> 
   <td>99</td> 
   <td>Utrecht University</td> 
   <td>Netherlands</td> 
   <td>2</td> 
   <td>100</td> 
   <td>101</td> 
   <td>101</td> 
   <td>65</td> 
   <td>101</td> 
   <td>60</td> 
   <td>NaN</td> 
   <td>101</td> 
   <td>43.47</td> 
  </tr> 
  <tr> 
   <th>99</th> 
   <td>100</td> 
   <td>Mines ParisTech</td> 
   <td>France</td> 
   <td>5</td> 
   <td>44</td> 
   <td>4</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>NaN</td> 
   <td>101</td> 
   <td>43.36</td> 
  </tr> 
 </tbody> 
</table>

100 rows × 13 columns

data_2012.info()

<class 'pandas.core.frame.DataFrame'>
    Int64Index: 100 entries, 0 to 99
    Data columns (total 13 columns):
     #   Column                Non-Null Count  Dtype  
    ---  ------                --------------  -----  
     0   world_rank            100 non-null    int64  
     1   institution           100 non-null    object 
     2   country               100 non-null    object 
     3   national_rank         100 non-null    int64  
     4   quality_of_education  100 non-null    int64  
     5   alumni_employment     100 non-null    int64  
     6   quality_of_faculty    100 non-null    int64  
     7   publications          100 non-null    int64  
     8   influence             100 non-null    int64  
     9   citations             100 non-null    int64  
     10  broad_impact          0 non-null      float64
     11  patents               100 non-null    int64  
     12  score                 100 non-null    float64
    dtypes: float64(2), int64(9), object(2)
    memory usage: 10.9+ KB

从上面的结果我们可以看出，`broad_impact`一列全部缺失，在后面的分析中可以删除此列；其余数据列没有缺失值。

接下来查看2012年前100所世界名校的国家分布。

data_2012['country'].value_counts()

USA               58
    United Kingdom     8
    Japan              5
    France             5
    Israel             4
    Switzerland        4
    Canada             3
    Germany            3
    Australia          2
    Netherlands        2
    Italy              1
    Norway             1
    Denmark            1
    South Korea        1
    Finland            1
    Sweden             1
    Name: country, dtype: int64

可以看出，世界排名前100的高校榜单中，美国高校占据了其中58个席位，远远超过排名第二的英国（8所）。中国没有高校入选该榜单，不清楚是不是因为没有参与该榜单的排名。

删除`broad_impact`一列，便于后续分析。

data_2012_copy = data_2012.copy()
    data_2012_copy.drop('broad_impact', axis=1, inplace=True)
    data_2012 = data_2012_copy
    data_2012

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>patents</th> 
   <th>score</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>7</td> 
   <td>9</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>5</td> 
   <td>100.00</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>2</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>2</td> 
   <td>9</td> 
   <td>17</td> 
   <td>3</td> 
   <td>12</td> 
   <td>4</td> 
   <td>4</td> 
   <td>1</td> 
   <td>91.67</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>3</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>3</td> 
   <td>17</td> 
   <td>11</td> 
   <td>5</td> 
   <td>4</td> 
   <td>2</td> 
   <td>2</td> 
   <td>15</td> 
   <td>89.50</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>4</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>1</td> 
   <td>10</td> 
   <td>24</td> 
   <td>4</td> 
   <td>16</td> 
   <td>16</td> 
   <td>11</td> 
   <td>50</td> 
   <td>86.17</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>5</td> 
   <td>California Institute of Technology</td> 
   <td>USA</td> 
   <td>4</td> 
   <td>2</td> 
   <td>29</td> 
   <td>7</td> 
   <td>37</td> 
   <td>22</td> 
   <td>22</td> 
   <td>18</td> 
   <td>85.21</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>95</th> 
   <td>96</td> 
   <td>University of Texas MD Anderson Cancer Center</td> 
   <td>USA</td> 
   <td>58</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>95</td> 
   <td>46</td> 
   <td>66</td> 
   <td>100</td> 
   <td>43.88</td> 
  </tr> 
  <tr> 
   <th>96</th> 
   <td>97</td> 
   <td>University of Nottingham</td> 
   <td>United Kingdom</td> 
   <td>6</td> 
   <td>101</td> 
   <td>101</td> 
   <td>87</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>92</td> 
   <td>43.79</td> 
  </tr> 
  <tr> 
   <th>97</th> 
   <td>98</td> 
   <td>University of Bristol</td> 
   <td>United Kingdom</td> 
   <td>7</td> 
   <td>101</td> 
   <td>101</td> 
   <td>78</td> 
   <td>75</td> 
   <td>81</td> 
   <td>86</td> 
   <td>101</td> 
   <td>43.77</td> 
  </tr> 
  <tr> 
   <th>98</th> 
   <td>99</td> 
   <td>Utrecht University</td> 
   <td>Netherlands</td> 
   <td>2</td> 
   <td>100</td> 
   <td>101</td> 
   <td>101</td> 
   <td>65</td> 
   <td>101</td> 
   <td>60</td> 
   <td>101</td> 
   <td>43.47</td> 
  </tr> 
  <tr> 
   <th>99</th> 
   <td>100</td> 
   <td>Mines ParisTech</td> 
   <td>France</td> 
   <td>5</td> 
   <td>44</td> 
   <td>4</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>43.36</td> 
  </tr> 
 </tbody> 
</table>

100 rows × 12 columns

因为分析`national_rank`和`score`对`world_rank`的影响意义不大，所以删除这两列数据。

data_2012_world_rank = data_2012.copy()
    data_2012_world_rank.drop(['national_rank', 'score'], axis=1, inplace=True)
    data_2012_world_rank

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>patents</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>7</td> 
   <td>9</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>5</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>2</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>9</td> 
   <td>17</td> 
   <td>3</td> 
   <td>12</td> 
   <td>4</td> 
   <td>4</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>3</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>17</td> 
   <td>11</td> 
   <td>5</td> 
   <td>4</td> 
   <td>2</td> 
   <td>2</td> 
   <td>15</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>4</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>10</td> 
   <td>24</td> 
   <td>4</td> 
   <td>16</td> 
   <td>16</td> 
   <td>11</td> 
   <td>50</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>5</td> 
   <td>California Institute of Technology</td> 
   <td>USA</td> 
   <td>2</td> 
   <td>29</td> 
   <td>7</td> 
   <td>37</td> 
   <td>22</td> 
   <td>22</td> 
   <td>18</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>95</th> 
   <td>96</td> 
   <td>University of Texas MD Anderson Cancer Center</td> 
   <td>USA</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>95</td> 
   <td>46</td> 
   <td>66</td> 
   <td>100</td> 
  </tr> 
  <tr> 
   <th>96</th> 
   <td>97</td> 
   <td>University of Nottingham</td> 
   <td>United Kingdom</td> 
   <td>101</td> 
   <td>101</td> 
   <td>87</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>92</td> 
  </tr> 
  <tr> 
   <th>97</th> 
   <td>98</td> 
   <td>University of Bristol</td> 
   <td>United Kingdom</td> 
   <td>101</td> 
   <td>101</td> 
   <td>78</td> 
   <td>75</td> 
   <td>81</td> 
   <td>86</td> 
   <td>101</td> 
  </tr> 
  <tr> 
   <th>98</th> 
   <td>99</td> 
   <td>Utrecht University</td> 
   <td>Netherlands</td> 
   <td>100</td> 
   <td>101</td> 
   <td>101</td> 
   <td>65</td> 
   <td>101</td> 
   <td>60</td> 
   <td>101</td> 
  </tr> 
  <tr> 
   <th>99</th> 
   <td>100</td> 
   <td>Mines ParisTech</td> 
   <td>France</td> 
   <td>44</td> 
   <td>4</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
  </tr> 
 </tbody> 
</table>

100 rows × 10 columns

利用`matplotlib`和`seaborn`绘制热力图，查看各个变量间的相关度。

plt.figure(figsize=(10, 10))
    sns.heatmap(data_2012_world_rank.corr(), annot=True)

<AxesSubplot:>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70]

可以看出，在2012年的榜单中，决定世界排名 (World Rank) 的几个主要因素为教师质量 (Quality of Faculty)、影响力 (Influence)、被引用次数 (Citations) 等。

# 2013年数据分析 #

2013年的榜单与2012年结构相差不大，分析流程也与上面的流程一致，分析过程不再赘述。

data_2013 = df[df['year'] == 2013].drop('year', axis=1)
    data_2013

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
   <th>score</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>100</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>NaN</td> 
   <td>7</td> 
   <td>100.00</td> 
  </tr> 
  <tr> 
   <th>101</th> 
   <td>2</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>2</td> 
   <td>11</td> 
   <td>2</td> 
   <td>4</td> 
   <td>6</td> 
   <td>2</td> 
   <td>2</td> 
   <td>NaN</td> 
   <td>11</td> 
   <td>93.94</td> 
  </tr> 
  <tr> 
   <th>102</th> 
   <td>3</td> 
   <td>University of Oxford</td> 
   <td>United Kingdom</td> 
   <td>1</td> 
   <td>7</td> 
   <td>12</td> 
   <td>10</td> 
   <td>11</td> 
   <td>7</td> 
   <td>13</td> 
   <td>NaN</td> 
   <td>15</td> 
   <td>92.54</td> 
  </tr> 
  <tr> 
   <th>103</th> 
   <td>4</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>3</td> 
   <td>2</td> 
   <td>16</td> 
   <td>2</td> 
   <td>16</td> 
   <td>3</td> 
   <td>3</td> 
   <td>NaN</td> 
   <td>1</td> 
   <td>91.45</td> 
  </tr> 
  <tr> 
   <th>104</th> 
   <td>5</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>2</td> 
   <td>3</td> 
   <td>15</td> 
   <td>5</td> 
   <td>9</td> 
   <td>11</td> 
   <td>10</td> 
   <td>NaN</td> 
   <td>39</td> 
   <td>90.24</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>195</th> 
   <td>96</td> 
   <td>Australian National University</td> 
   <td>Australia</td> 
   <td>2</td> 
   <td>101</td> 
   <td>101</td> 
   <td>43</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>NaN</td> 
   <td>101</td> 
   <td>44.50</td> 
  </tr> 
  <tr> 
   <th>196</th> 
   <td>97</td> 
   <td>University of Alberta</td> 
   <td>Canada</td> 
   <td>4</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>68</td> 
   <td>101</td> 
   <td>92</td> 
   <td>NaN</td> 
   <td>81</td> 
   <td>44.50</td> 
  </tr> 
  <tr> 
   <th>197</th> 
   <td>98</td> 
   <td>University of Helsinki</td> 
   <td>Finland</td> 
   <td>1</td> 
   <td>69</td> 
   <td>101</td> 
   <td>81</td> 
   <td>74</td> 
   <td>79</td> 
   <td>71</td> 
   <td>NaN</td> 
   <td>101</td> 
   <td>44.39</td> 
  </tr> 
  <tr> 
   <th>198</th> 
   <td>99</td> 
   <td>Paris Diderot University - Paris 7</td> 
   <td>France</td> 
   <td>5</td> 
   <td>28</td> 
   <td>101</td> 
   <td>72</td> 
   <td>101</td> 
   <td>87</td> 
   <td>101</td> 
   <td>NaN</td> 
   <td>101</td> 
   <td>44.36</td> 
  </tr> 
  <tr> 
   <th>199</th> 
   <td>100</td> 
   <td>Georgia Institute of Technology</td> 
   <td>USA</td> 
   <td>57</td> 
   <td>101</td> 
   <td>85</td> 
   <td>101</td> 
   <td>97</td> 
   <td>101</td> 
   <td>43</td> 
   <td>NaN</td> 
   <td>32</td> 
   <td>44.26</td> 
  </tr> 
 </tbody> 
</table>

100 rows × 13 columns

data_2013.info()

<class 'pandas.core.frame.DataFrame'>
    Int64Index: 100 entries, 100 to 199
    Data columns (total 13 columns):
     #   Column                Non-Null Count  Dtype  
    ---  ------                --------------  -----  
     0   world_rank            100 non-null    int64  
     1   institution           100 non-null    object 
     2   country               100 non-null    object 
     3   national_rank         100 non-null    int64  
     4   quality_of_education  100 non-null    int64  
     5   alumni_employment     100 non-null    int64  
     6   quality_of_faculty    100 non-null    int64  
     7   publications          100 non-null    int64  
     8   influence             100 non-null    int64  
     9   citations             100 non-null    int64  
     10  broad_impact          0 non-null      float64
     11  patents               100 non-null    int64  
     12  score                 100 non-null    float64
    dtypes: float64(2), int64(9), object(2)
    memory usage: 10.9+ KB

data_2013['country'].value_counts()

USA               57
    United Kingdom     7
    Japan              6
    France             5
    Switzerland        4
    Canada             4
    Israel             4
    Australia          2
    Germany            2
    South Korea        1
    Russia             1
    Denmark            1
    Singapore          1
    Netherlands        1
    Finland            1
    Norway             1
    Sweden             1
    Italy              1
    Name: country, dtype: int64

2013年世界排名前100的高校来自18个国家和地区，在2012年未入选前100名的来自新加坡和俄罗斯的各一所高校入选2013年榜单。此外，仍然没有来自中国的高校入选该榜单。

data_2013_copy = data_2013.copy()
    data_2013_copy.drop('broad_impact', axis=1, inplace=True)
    data_2013 = data_2013_copy
    data_2013

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>patents</th> 
   <th>score</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>100</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>7</td> 
   <td>100.00</td> 
  </tr> 
  <tr> 
   <th>101</th> 
   <td>2</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>2</td> 
   <td>11</td> 
   <td>2</td> 
   <td>4</td> 
   <td>6</td> 
   <td>2</td> 
   <td>2</td> 
   <td>11</td> 
   <td>93.94</td> 
  </tr> 
  <tr> 
   <th>102</th> 
   <td>3</td> 
   <td>University of Oxford</td> 
   <td>United Kingdom</td> 
   <td>1</td> 
   <td>7</td> 
   <td>12</td> 
   <td>10</td> 
   <td>11</td> 
   <td>7</td> 
   <td>13</td> 
   <td>15</td> 
   <td>92.54</td> 
  </tr> 
  <tr> 
   <th>103</th> 
   <td>4</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>3</td> 
   <td>2</td> 
   <td>16</td> 
   <td>2</td> 
   <td>16</td> 
   <td>3</td> 
   <td>3</td> 
   <td>1</td> 
   <td>91.45</td> 
  </tr> 
  <tr> 
   <th>104</th> 
   <td>5</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>2</td> 
   <td>3</td> 
   <td>15</td> 
   <td>5</td> 
   <td>9</td> 
   <td>11</td> 
   <td>10</td> 
   <td>39</td> 
   <td>90.24</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>195</th> 
   <td>96</td> 
   <td>Australian National University</td> 
   <td>Australia</td> 
   <td>2</td> 
   <td>101</td> 
   <td>101</td> 
   <td>43</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>44.50</td> 
  </tr> 
  <tr> 
   <th>196</th> 
   <td>97</td> 
   <td>University of Alberta</td> 
   <td>Canada</td> 
   <td>4</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>68</td> 
   <td>101</td> 
   <td>92</td> 
   <td>81</td> 
   <td>44.50</td> 
  </tr> 
  <tr> 
   <th>197</th> 
   <td>98</td> 
   <td>University of Helsinki</td> 
   <td>Finland</td> 
   <td>1</td> 
   <td>69</td> 
   <td>101</td> 
   <td>81</td> 
   <td>74</td> 
   <td>79</td> 
   <td>71</td> 
   <td>101</td> 
   <td>44.39</td> 
  </tr> 
  <tr> 
   <th>198</th> 
   <td>99</td> 
   <td>Paris Diderot University - Paris 7</td> 
   <td>France</td> 
   <td>5</td> 
   <td>28</td> 
   <td>101</td> 
   <td>72</td> 
   <td>101</td> 
   <td>87</td> 
   <td>101</td> 
   <td>101</td> 
   <td>44.36</td> 
  </tr> 
  <tr> 
   <th>199</th> 
   <td>100</td> 
   <td>Georgia Institute of Technology</td> 
   <td>USA</td> 
   <td>57</td> 
   <td>101</td> 
   <td>85</td> 
   <td>101</td> 
   <td>97</td> 
   <td>101</td> 
   <td>43</td> 
   <td>32</td> 
   <td>44.26</td> 
  </tr> 
 </tbody> 
</table>

100 rows × 12 columns

data_2013_world_rank = data_2013.copy()
    data_2013_world_rank.drop(['national_rank', 'score'], axis=1, inplace=True)
    data_2013_world_rank

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>patents</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>100</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>7</td> 
  </tr> 
  <tr> 
   <th>101</th> 
   <td>2</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>11</td> 
   <td>2</td> 
   <td>4</td> 
   <td>6</td> 
   <td>2</td> 
   <td>2</td> 
   <td>11</td> 
  </tr> 
  <tr> 
   <th>102</th> 
   <td>3</td> 
   <td>University of Oxford</td> 
   <td>United Kingdom</td> 
   <td>7</td> 
   <td>12</td> 
   <td>10</td> 
   <td>11</td> 
   <td>7</td> 
   <td>13</td> 
   <td>15</td> 
  </tr> 
  <tr> 
   <th>103</th> 
   <td>4</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>2</td> 
   <td>16</td> 
   <td>2</td> 
   <td>16</td> 
   <td>3</td> 
   <td>3</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>104</th> 
   <td>5</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>3</td> 
   <td>15</td> 
   <td>5</td> 
   <td>9</td> 
   <td>11</td> 
   <td>10</td> 
   <td>39</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>195</th> 
   <td>96</td> 
   <td>Australian National University</td> 
   <td>Australia</td> 
   <td>101</td> 
   <td>101</td> 
   <td>43</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
  </tr> 
  <tr> 
   <th>196</th> 
   <td>97</td> 
   <td>University of Alberta</td> 
   <td>Canada</td> 
   <td>101</td> 
   <td>101</td> 
   <td>101</td> 
   <td>68</td> 
   <td>101</td> 
   <td>92</td> 
   <td>81</td> 
  </tr> 
  <tr> 
   <th>197</th> 
   <td>98</td> 
   <td>University of Helsinki</td> 
   <td>Finland</td> 
   <td>69</td> 
   <td>101</td> 
   <td>81</td> 
   <td>74</td> 
   <td>79</td> 
   <td>71</td> 
   <td>101</td> 
  </tr> 
  <tr> 
   <th>198</th> 
   <td>99</td> 
   <td>Paris Diderot University - Paris 7</td> 
   <td>France</td> 
   <td>28</td> 
   <td>101</td> 
   <td>72</td> 
   <td>101</td> 
   <td>87</td> 
   <td>101</td> 
   <td>101</td> 
  </tr> 
  <tr> 
   <th>199</th> 
   <td>100</td> 
   <td>Georgia Institute of Technology</td> 
   <td>USA</td> 
   <td>101</td> 
   <td>85</td> 
   <td>101</td> 
   <td>97</td> 
   <td>101</td> 
   <td>43</td> 
   <td>32</td> 
  </tr> 
 </tbody> 
</table>

100 rows × 10 columns

plt.figure(figsize=(10, 10))
    sns.heatmap(data_2013_world_rank.corr(), annot=True)

<AxesSubplot:>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 1]  
在2013年的榜单中，决定世界排名 (World Rank) 的几个主要因素为教师质量 (Quality of Faculty)、影响力 (Influence)、被引用次数 (Citations)、出版物 (Publications) 等，整体与2012年榜单一致。

# 2014年数据分析 #

首先依旧是筛选2014年的数据。

data_2014 = df[df['year'] == 2014].drop('year', axis=1)
    data_2014

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
   <th>score</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>200</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1.0</td> 
   <td>2</td> 
   <td>100.00</td> 
  </tr> 
  <tr> 
   <th>201</th> 
   <td>2</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>2</td> 
   <td>11</td> 
   <td>2</td> 
   <td>4</td> 
   <td>5</td> 
   <td>3</td> 
   <td>3</td> 
   <td>4.0</td> 
   <td>6</td> 
   <td>99.09</td> 
  </tr> 
  <tr> 
   <th>202</th> 
   <td>3</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>3</td> 
   <td>3</td> 
   <td>11</td> 
   <td>2</td> 
   <td>15</td> 
   <td>2</td> 
   <td>2</td> 
   <td>2.0</td> 
   <td>1</td> 
   <td>98.69</td> 
  </tr> 
  <tr> 
   <th>203</th> 
   <td>4</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>1</td> 
   <td>2</td> 
   <td>10</td> 
   <td>5</td> 
   <td>10</td> 
   <td>9</td> 
   <td>12</td> 
   <td>13.0</td> 
   <td>48</td> 
   <td>97.64</td> 
  </tr> 
  <tr> 
   <th>204</th> 
   <td>5</td> 
   <td>University of Oxford</td> 
   <td>United Kingdom</td> 
   <td>2</td> 
   <td>7</td> 
   <td>12</td> 
   <td>10</td> 
   <td>11</td> 
   <td>12</td> 
   <td>11</td> 
   <td>12.0</td> 
   <td>16</td> 
   <td>97.51</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>1195</th> 
   <td>996</td> 
   <td>National Dong Hwa University</td> 
   <td>Taiwan</td> 
   <td>24</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>901</td> 
   <td>934</td> 
   <td>800</td> 
   <td>989.0</td> 
   <td>737</td> 
   <td>44.24</td> 
  </tr> 
  <tr> 
   <th>1196</th> 
   <td>997</td> 
   <td>National Taipei University of Technology</td> 
   <td>Taiwan</td> 
   <td>25</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>867</td> 
   <td>987</td> 
   <td>800</td> 
   <td>994.0</td> 
   <td>737</td> 
   <td>44.24</td> 
  </tr> 
  <tr> 
   <th>1197</th> 
   <td>998</td> 
   <td>Shaanxi Normal University</td> 
   <td>China</td> 
   <td>82</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>956</td> 
   <td>965</td> 
   <td>800</td> 
   <td>994.0</td> 
   <td>737</td> 
   <td>44.23</td> 
  </tr> 
  <tr> 
   <th>1198</th> 
   <td>999</td> 
   <td>National University of Defense Technology</td> 
   <td>China</td> 
   <td>83</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>860</td> 
   <td>973</td> 
   <td>800</td> 
   <td>999.0</td> 
   <td>637</td> 
   <td>44.21</td> 
  </tr> 
  <tr> 
   <th>1199</th> 
   <td>1000</td> 
   <td>Yanbian University</td> 
   <td>China</td> 
   <td>84</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>890</td> 
   <td>790</td> 
   <td>800</td> 
   <td>1000.0</td> 
   <td>737</td> 
   <td>44.18</td> 
  </tr> 
 </tbody> 
</table>

1000 rows × 13 columns

data_2014.info()

<class 'pandas.core.frame.DataFrame'>
    Int64Index: 1000 entries, 200 to 1199
    Data columns (total 13 columns):
     #   Column                Non-Null Count  Dtype  
    ---  ------                --------------  -----  
     0   world_rank            1000 non-null   int64  
     1   institution           1000 non-null   object 
     2   country               1000 non-null   object 
     3   national_rank         1000 non-null   int64  
     4   quality_of_education  1000 non-null   int64  
     5   alumni_employment     1000 non-null   int64  
     6   quality_of_faculty    1000 non-null   int64  
     7   publications          1000 non-null   int64  
     8   influence             1000 non-null   int64  
     9   citations             1000 non-null   int64  
     10  broad_impact          1000 non-null   float64
     11  patents               1000 non-null   int64  
     12  score                 1000 non-null   float64
    dtypes: float64(2), int64(9), object(2)
    memory usage: 109.4+ KB

2014年的榜单相较于前两年，世界排名从100增加到了1000，另外`broad_impact`一列也不再有缺失值，不知道这一列的加入是否会对决定世界排名的因素有影响。

data_2014['country'].value_counts()

USA                     229
    China                    84
    Japan                    74
    United Kingdom           64
    Germany                  55
    France                   50
    Italy                    47
    Spain                    41
    South Korea              34
    Canada                   32
    Australia                27
    Taiwan                   25
    Brazil                   18
    India                    15
    Netherlands              13
    Austria                  12
    Sweden                   11
    Belgium                  10
    Turkey                   10
    Finland                   9
    Poland                    9
    Switzerland               9
    Ireland                   8
    Iran                      8
    Greece                    7
    Portugal                  7
    Israel                    7
    New Zealand               6
    Hong Kong                 6
    Hungary                   6
    Denmark                   5
    South Africa              5
    Norway                    5
    Czech Republic            5
    Chile                     4
    Argentina                 4
    Egypt                     4
    Saudi Arabia              4
    Thailand                  3
    Russia                    3
    Malaysia                  3
    Slovenia                  2
    Singapore                 2
    Mexico                    2
    Colombia                  2
    Estonia                   1
    Cyprus                    1
    United Arab Emirates      1
    Uganda                    1
    Lebanon                   1
    Romania                   1
    Croatia                   1
    Serbia                    1
    Lithuania                 1
    Bulgaria                  1
    Uruguay                   1
    Slovak Republic           1
    Iceland                   1
    Puerto Rico               1
    Name: country, dtype: int64

2014年，世界排名前1000的高校中，美国仍然占了相当大的比例 (22.9%)。排名第二的是中国（大陆地区），有84所高校入选榜单。排名第三至第五的国家分别是日本、英国、德国。中国香港也有6所高校入选。

下面我们来查看一下有哪些中国（大陆地区）高校入选了2014年榜单，并且查看这些高校的世界排名。

data_2014_China = data_2014[data_2014['country'] == 'China']
    data_2014_China

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
   <th>score</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>254</th> 
   <td>55</td> 
   <td>Peking University</td> 
   <td>China</td> 
   <td>1</td> 
   <td>355</td> 
   <td>35</td> 
   <td>210</td> 
   <td>65</td> 
   <td>155</td> 
   <td>250</td> 
   <td>155.0</td> 
   <td>7</td> 
   <td>55.30</td> 
  </tr> 
  <tr> 
   <th>286</th> 
   <td>87</td> 
   <td>Tsinghua University</td> 
   <td>China</td> 
   <td>2</td> 
   <td>294</td> 
   <td>63</td> 
   <td>210</td> 
   <td>79</td> 
   <td>192</td> 
   <td>134</td> 
   <td>162.0</td> 
   <td>16</td> 
   <td>52.60</td> 
  </tr> 
  <tr> 
   <th>388</th> 
   <td>189</td> 
   <td>Fudan University</td> 
   <td>China</td> 
   <td>3</td> 
   <td>355</td> 
   <td>126</td> 
   <td>210</td> 
   <td>120</td> 
   <td>264</td> 
   <td>310</td> 
   <td>230.0</td> 
   <td>100</td> 
   <td>48.14</td> 
  </tr> 
  <tr> 
   <th>394</th> 
   <td>195</td> 
   <td>Shanghai Jiao Tong University</td> 
   <td>China</td> 
   <td>4</td> 
   <td>325</td> 
   <td>149</td> 
   <td>210</td> 
   <td>102</td> 
   <td>250</td> 
   <td>250</td> 
   <td>234.0</td> 
   <td>138</td> 
   <td>48.02</td> 
  </tr> 
  <tr> 
   <th>405</th> 
   <td>206</td> 
   <td>Zhejiang University</td> 
   <td>China</td> 
   <td>5</td> 
   <td>355</td> 
   <td>293</td> 
   <td>210</td> 
   <td>86</td> 
   <td>318</td> 
   <td>493</td> 
   <td>290.0</td> 
   <td>94</td> 
   <td>47.76</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>1189</th> 
   <td>990</td> 
   <td>Harbin Medical University</td> 
   <td>China</td> 
   <td>80</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>900</td> 
   <td>862</td> 
   <td>800</td> 
   <td>979.0</td> 
   <td>737</td> 
   <td>44.26</td> 
  </tr> 
  <tr> 
   <th>1190</th> 
   <td>991</td> 
   <td>Zhejiang Normal University</td> 
   <td>China</td> 
   <td>81</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>905</td> 
   <td>932</td> 
   <td>800</td> 
   <td>979.0</td> 
   <td>737</td> 
   <td>44.26</td> 
  </tr> 
  <tr> 
   <th>1197</th> 
   <td>998</td> 
   <td>Shaanxi Normal University</td> 
   <td>China</td> 
   <td>82</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>956</td> 
   <td>965</td> 
   <td>800</td> 
   <td>994.0</td> 
   <td>737</td> 
   <td>44.23</td> 
  </tr> 
  <tr> 
   <th>1198</th> 
   <td>999</td> 
   <td>National University of Defense Technology</td> 
   <td>China</td> 
   <td>83</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>860</td> 
   <td>973</td> 
   <td>800</td> 
   <td>999.0</td> 
   <td>637</td> 
   <td>44.21</td> 
  </tr> 
  <tr> 
   <th>1199</th> 
   <td>1000</td> 
   <td>Yanbian University</td> 
   <td>China</td> 
   <td>84</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>890</td> 
   <td>790</td> 
   <td>800</td> 
   <td>1000.0</td> 
   <td>737</td> 
   <td>44.18</td> 
  </tr> 
 </tbody> 
</table>

84 rows × 13 columns

for i in range(84):
        print('{:4d}: {}'.format(data_2014_China['world_rank'].tolist()[i], data_2014_China['institution'].tolist()[i]))

55: Peking University
      87: Tsinghua University
     189: Fudan University
     195: Shanghai Jiao Tong University
     206: Zhejiang University
     217: Nanjing University
     242: Dalian University of Technology
     270: University of Science and Technology of China
     292: Sun Yat-sen University
     349: Nankai University
     354: Xiamen University
     370: Tongji University
     385: Beijing Normal University
     394: Tianjin University
     399: Xi'an Jiaotong University
     408: Huazhong University of Science and Technology
     412: Southeast University
     435: Jilin University
     438: Wuhan University
     457: Shandong University
     465: Central South University
     475: Harbin Institute of Technology
     498: South China University of Technology
     515: Sichuan University
     528: East China University of Science and Technology
     560: Beihang University
     571: Lanzhou University
     588: Hunan University
     599: University of Science and Technology Beijing
     610: Central China Normal University
     669: Northeast Normal University
     672: East China Normal University
     685: Beijing University of Chemical Technology
     700: China Agricultural University
     710: Nanjing University of Science and Technology
     713: Soochow University (Suzhou)
     715: Fuzhou University
     722: Shanghai University
     731: Donghua University
     751: Wuhan University of Technology
     761: Peking Union Medical College
     782: Second Military Medical University
     790: University of Electronic Science and Technology of China
     815: Chongqing University
     816: Beijing Institute of Technology
     818: Zhengzhou University
     824: Capital Medical University
     842: Nanjing Normal University
     846: Jinan University
     865: Nanjing University of Technology
     870: Ocean University of China
     877: Jiangnan University
     883: Huazhong Agricultural University
     886: Shanghai Normal University
     896: Nanjing Agricultural University
     907: Northeastern University (China)
     912: Northwest University (China)
     917: Nanjing Medical University
     921: Southwest University
     932: Fourth Military Medical University
     935: Nanjing University of Aeronautics and Astronautics
     941: Jiangsu University
     942: Third Military Medical University
     943: South China Normal University
     945: Xiangtan University
     946: Yangzhou University
     947: Northwestern Polytechnical University
     955: Southern Medical University
     960: Beijing Jiaotong University
     968: Hunan Normal University
     973: South China Agricultural University
     975: China Pharmaceutical University
     980: Xidian University
     981: Zhejiang University of Technology
     982: China Medical University (PRC)
     985: Beijing University of Technology
     986: Guangxi University
     987: Northwest A&F University
     988: Shanxi University
     990: Harbin Medical University
     991: Zhejiang Normal University
     998: Shaanxi Normal University
     999: National University of Defense Technology
    1000: Yanbian University

plt.figure(figsize=(10, 10))
    plt.hist(data_2014_China['world_rank'].tolist(), 10)
    plt.xlabel('World Rank (2014)')
    plt.ylabel('Count')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 2]

从上图可以看出，入选榜单的84所中国（大陆地区）高校大多集中在800-1000名的位置，位于前200名的高校数量并不多。

data_2014_world_rank = data_2014.copy()
    data_2014_world_rank.drop(['national_rank', 'score'], axis=1, inplace=True)
    data_2014_world_rank

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>200</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1.0</td> 
   <td>2</td> 
  </tr> 
  <tr> 
   <th>201</th> 
   <td>2</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>11</td> 
   <td>2</td> 
   <td>4</td> 
   <td>5</td> 
   <td>3</td> 
   <td>3</td> 
   <td>4.0</td> 
   <td>6</td> 
  </tr> 
  <tr> 
   <th>202</th> 
   <td>3</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>3</td> 
   <td>11</td> 
   <td>2</td> 
   <td>15</td> 
   <td>2</td> 
   <td>2</td> 
   <td>2.0</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>203</th> 
   <td>4</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>2</td> 
   <td>10</td> 
   <td>5</td> 
   <td>10</td> 
   <td>9</td> 
   <td>12</td> 
   <td>13.0</td> 
   <td>48</td> 
  </tr> 
  <tr> 
   <th>204</th> 
   <td>5</td> 
   <td>University of Oxford</td> 
   <td>United Kingdom</td> 
   <td>7</td> 
   <td>12</td> 
   <td>10</td> 
   <td>11</td> 
   <td>12</td> 
   <td>11</td> 
   <td>12.0</td> 
   <td>16</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>1195</th> 
   <td>996</td> 
   <td>National Dong Hwa University</td> 
   <td>Taiwan</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>901</td> 
   <td>934</td> 
   <td>800</td> 
   <td>989.0</td> 
   <td>737</td> 
  </tr> 
  <tr> 
   <th>1196</th> 
   <td>997</td> 
   <td>National Taipei University of Technology</td> 
   <td>Taiwan</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>867</td> 
   <td>987</td> 
   <td>800</td> 
   <td>994.0</td> 
   <td>737</td> 
  </tr> 
  <tr> 
   <th>1197</th> 
   <td>998</td> 
   <td>Shaanxi Normal University</td> 
   <td>China</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>956</td> 
   <td>965</td> 
   <td>800</td> 
   <td>994.0</td> 
   <td>737</td> 
  </tr> 
  <tr> 
   <th>1198</th> 
   <td>999</td> 
   <td>National University of Defense Technology</td> 
   <td>China</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>860</td> 
   <td>973</td> 
   <td>800</td> 
   <td>999.0</td> 
   <td>637</td> 
  </tr> 
  <tr> 
   <th>1199</th> 
   <td>1000</td> 
   <td>Yanbian University</td> 
   <td>China</td> 
   <td>355</td> 
   <td>478</td> 
   <td>210</td> 
   <td>890</td> 
   <td>790</td> 
   <td>800</td> 
   <td>1000.0</td> 
   <td>737</td> 
  </tr> 
 </tbody> 
</table>

1000 rows × 11 columns

plt.figure(figsize=(10, 10))
    sns.heatmap(data_2014_world_rank.corr(), annot=True)

<AxesSubplot:>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 3]

在2014年的榜单中，决定世界排名 (World Rank) 的几个主要因素为广泛影响力 (Broad Impact)、出版物 (Publications)、影响力 (Influence) 等。`broad_impact`一列数据的加入使得决定性因素发生了变化，而其他几项因素的相关度相较于前两年也有所改变。可能这也和数据量从100增加到了1000有关。

# 2015年数据分析 #

2015年的数据与2014年结构类似，共有1000条数据，并且没有缺失值，分析流程与2014年数据基本一致，下面不再赘述。

data_2015 = df[df['year'] == 2015].drop('year', axis=1)
    data_2015

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
   <th>score</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>1200</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1.0</td> 
   <td>3</td> 
   <td>100.00</td> 
  </tr> 
  <tr> 
   <th>1201</th> 
   <td>2</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>2</td> 
   <td>9</td> 
   <td>2</td> 
   <td>4</td> 
   <td>5</td> 
   <td>3</td> 
   <td>3</td> 
   <td>4.0</td> 
   <td>10</td> 
   <td>98.66</td> 
  </tr> 
  <tr> 
   <th>1202</th> 
   <td>3</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>3</td> 
   <td>3</td> 
   <td>11</td> 
   <td>2</td> 
   <td>15</td> 
   <td>2</td> 
   <td>2</td> 
   <td>2.0</td> 
   <td>1</td> 
   <td>97.54</td> 
  </tr> 
  <tr> 
   <th>1203</th> 
   <td>4</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>1</td> 
   <td>2</td> 
   <td>10</td> 
   <td>5</td> 
   <td>11</td> 
   <td>6</td> 
   <td>12</td> 
   <td>13.0</td> 
   <td>48</td> 
   <td>96.81</td> 
  </tr> 
  <tr> 
   <th>1204</th> 
   <td>5</td> 
   <td>University of Oxford</td> 
   <td>United Kingdom</td> 
   <td>2</td> 
   <td>7</td> 
   <td>13</td> 
   <td>10</td> 
   <td>7</td> 
   <td>12</td> 
   <td>7</td> 
   <td>9.0</td> 
   <td>15</td> 
   <td>96.46</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>2195</th> 
   <td>996</td> 
   <td>University of the Algarve</td> 
   <td>Portugal</td> 
   <td>7</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>926</td> 
   <td>845</td> 
   <td>812</td> 
   <td>969.0</td> 
   <td>816</td> 
   <td>44.03</td> 
  </tr> 
  <tr> 
   <th>2196</th> 
   <td>997</td> 
   <td>Alexandria University</td> 
   <td>Egypt</td> 
   <td>4</td> 
   <td>236</td> 
   <td>566</td> 
   <td>218</td> 
   <td>997</td> 
   <td>908</td> 
   <td>645</td> 
   <td>981.0</td> 
   <td>871</td> 
   <td>44.03</td> 
  </tr> 
  <tr> 
   <th>2197</th> 
   <td>998</td> 
   <td>Federal University of Ceará</td> 
   <td>Brazil</td> 
   <td>18</td> 
   <td>367</td> 
   <td>549</td> 
   <td>218</td> 
   <td>830</td> 
   <td>823</td> 
   <td>812</td> 
   <td>975.0</td> 
   <td>824</td> 
   <td>44.03</td> 
  </tr> 
  <tr> 
   <th>2198</th> 
   <td>999</td> 
   <td>University of A Coruña</td> 
   <td>Spain</td> 
   <td>40</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>886</td> 
   <td>974</td> 
   <td>812</td> 
   <td>975.0</td> 
   <td>651</td> 
   <td>44.02</td> 
  </tr> 
  <tr> 
   <th>2199</th> 
   <td>1000</td> 
   <td>China Pharmaceutical University</td> 
   <td>China</td> 
   <td>83</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>861</td> 
   <td>991</td> 
   <td>812</td> 
   <td>981.0</td> 
   <td>547</td> 
   <td>44.02</td> 
  </tr> 
 </tbody> 
</table>

1000 rows × 13 columns

data_2015.info()

<class 'pandas.core.frame.DataFrame'>
    Int64Index: 1000 entries, 1200 to 2199
    Data columns (total 13 columns):
     #   Column                Non-Null Count  Dtype  
    ---  ------                --------------  -----  
     0   world_rank            1000 non-null   int64  
     1   institution           1000 non-null   object 
     2   country               1000 non-null   object 
     3   national_rank         1000 non-null   int64  
     4   quality_of_education  1000 non-null   int64  
     5   alumni_employment     1000 non-null   int64  
     6   quality_of_faculty    1000 non-null   int64  
     7   publications          1000 non-null   int64  
     8   influence             1000 non-null   int64  
     9   citations             1000 non-null   int64  
     10  broad_impact          1000 non-null   float64
     11  patents               1000 non-null   int64  
     12  score                 1000 non-null   float64
    dtypes: float64(2), int64(9), object(2)
    memory usage: 109.4+ KB

data_2015['country'].value_counts()

USA                     229
    China                    83
    Japan                    74
    United Kingdom           65
    Germany                  55
    France                   49
    Italy                    47
    Spain                    40
    South Korea              36
    Canada                   33
    Australia                27
    Taiwan                   21
    Brazil                   18
    India                    16
    Netherlands              13
    Austria                  12
    Sweden                   11
    Belgium                  10
    Turkey                   10
    Poland                    9
    Switzerland               9
    Finland                   9
    Iran                      8
    Ireland                   8
    Portugal                  7
    Greece                    7
    Israel                    7
    Hong Kong                 6
    New Zealand               6
    Hungary                   6
    South Africa              5
    Czech Republic            5
    Denmark                   5
    Russia                    5
    Norway                    5
    Egypt                     4
    Saudi Arabia              4
    Chile                     4
    Thailand                  3
    Argentina                 3
    Malaysia                  3
    Romania                   2
    Slovenia                  2
    Mexico                    2
    Colombia                  2
    Singapore                 2
    Cyprus                    1
    Estonia                   1
    United Arab Emirates      1
    Uganda                    1
    Lebanon                   1
    Croatia                   1
    Serbia                    1
    Lithuania                 1
    Bulgaria                  1
    Uruguay                   1
    Slovak Republic           1
    Iceland                   1
    Puerto Rico               1
    Name: country, dtype: int64

前五名依然是美国、中国（大陆地区）、日本、英国、德国，与2014年一致。

data_2015_China = data_2015[data_2015['country'] == 'China']
    data_2015_China

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>national_rank</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
   <th>score</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>1255</th> 
   <td>56</td> 
   <td>Peking University</td> 
   <td>China</td> 
   <td>1</td> 
   <td>182</td> 
   <td>38</td> 
   <td>218</td> 
   <td>52</td> 
   <td>131</td> 
   <td>182</td> 
   <td>125.0</td> 
   <td>20</td> 
   <td>54.26</td> 
  </tr> 
  <tr> 
   <th>1277</th> 
   <td>78</td> 
   <td>Tsinghua University</td> 
   <td>China</td> 
   <td>2</td> 
   <td>309</td> 
   <td>73</td> 
   <td>218</td> 
   <td>63</td> 
   <td>158</td> 
   <td>65</td> 
   <td>156.0</td> 
   <td>30</td> 
   <td>52.21</td> 
  </tr> 
  <tr> 
   <th>1379</th> 
   <td>180</td> 
   <td>Shanghai Jiao Tong University</td> 
   <td>China</td> 
   <td>3</td> 
   <td>335</td> 
   <td>143</td> 
   <td>218</td> 
   <td>81</td> 
   <td>267</td> 
   <td>212</td> 
   <td>209.0</td> 
   <td>198</td> 
   <td>47.96</td> 
  </tr> 
  <tr> 
   <th>1390</th> 
   <td>191</td> 
   <td>Zhejiang University</td> 
   <td>China</td> 
   <td>4</td> 
   <td>367</td> 
   <td>309</td> 
   <td>218</td> 
   <td>71</td> 
   <td>290</td> 
   <td>368</td> 
   <td>265.0</td> 
   <td>106</td> 
   <td>47.66</td> 
  </tr> 
  <tr> 
   <th>1394</th> 
   <td>195</td> 
   <td>Fudan University</td> 
   <td>China</td> 
   <td>5</td> 
   <td>367</td> 
   <td>203</td> 
   <td>218</td> 
   <td>112</td> 
   <td>252</td> 
   <td>368</td> 
   <td>204.0</td> 
   <td>123</td> 
   <td>47.56</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>2183</th> 
   <td>984</td> 
   <td>Zhejiang University of Technology</td> 
   <td>China</td> 
   <td>79</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>858</td> 
   <td>991</td> 
   <td>812</td> 
   <td>958.0</td> 
   <td>672</td> 
   <td>44.04</td> 
  </tr> 
  <tr> 
   <th>2189</th> 
   <td>990</td> 
   <td>Henan Normal University</td> 
   <td>China</td> 
   <td>80</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>959</td> 
   <td>991</td> 
   <td>812</td> 
   <td>958.0</td> 
   <td>871</td> 
   <td>44.04</td> 
  </tr> 
  <tr> 
   <th>2190</th> 
   <td>991</td> 
   <td>Xidian University</td> 
   <td>China</td> 
   <td>81</td> 
   <td>367</td> 
   <td>542</td> 
   <td>218</td> 
   <td>830</td> 
   <td>974</td> 
   <td>812</td> 
   <td>984.0</td> 
   <td>434</td> 
   <td>44.03</td> 
  </tr> 
  <tr> 
   <th>2192</th> 
   <td>993</td> 
   <td>Southwest Jiaotong University</td> 
   <td>China</td> 
   <td>82</td> 
   <td>367</td> 
   <td>327</td> 
   <td>218</td> 
   <td>937</td> 
   <td>962</td> 
   <td>812</td> 
   <td>998.0</td> 
   <td>861</td> 
   <td>44.03</td> 
  </tr> 
  <tr> 
   <th>2199</th> 
   <td>1000</td> 
   <td>China Pharmaceutical University</td> 
   <td>China</td> 
   <td>83</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>861</td> 
   <td>991</td> 
   <td>812</td> 
   <td>981.0</td> 
   <td>547</td> 
   <td>44.02</td> 
  </tr> 
 </tbody> 
</table>

83 rows × 13 columns

for i in range(83):
        print('{:4d}: {}'.format(data_2015_China['world_rank'].tolist()[i], data_2015_China['institution'].tolist()[i]))

56: Peking University
      78: Tsinghua University
     180: Shanghai Jiao Tong University
     191: Zhejiang University
     195: Fudan University
     239: University of Science and Technology of China
     244: Nanjing University
     277: Sun Yat-sen University
     305: Dalian University of Technology
     313: Xiamen University
     315: Nankai University
     331: Tianjin University
     389: Xi'an Jiaotong University
     391: Jilin University
     396: Beijing Normal University
     400: Huazhong University of Science and Technology
     410: Harbin Institute of Technology
     413: Shandong University
     415: Wuhan University
     420: Tongji University
     423: Peking Union Medical College
     438: Central South University
     439: South China University of Technology
     443: Southeast University
     477: East China University of Science and Technology
     479: Sichuan University
     496: Renmin University of China
     514: Lanzhou University
     530: University of Science and Technology Beijing
     553: Hunan University
     572: Central China Normal University
     586: East China Normal University
     615: Soochow University (Suzhou)
     627: China Agricultural University
     640: Northeast Normal University
     642: Beijing University of Chemical Technology
     659: Donghua University
     677: Wuhan University of Technology
     684: Shanghai University
     705: Beihang University
     706: Fuzhou University
     749: Beijing Institute of Technology
     774: Chongqing University
     776: Second Military Medical University
     783: University of Electronic Science and Technology of China
     788: Capital Medical University
     795: Zhengzhou University
     804: Nanjing University of Science and Technology
     810: Nanjing University of Technology
     828: Jiangnan University
     846: Ocean University of China
     858: Huazhong Agricultural University
     864: Nanjing Normal University
     866: Nanjing Agricultural University
     871: Northeastern University (China)
     877: Northwest University (China)
     883: Hefei University of Technology
     886: Nanjing Medical University
     888: Shanghai Normal University
     889: Southwest University
     895: Nanjing University of Aeronautics and Astronautics
     899: Fourth Military Medical University
     904: China University of Geosciences (Wuhan)
     924: South China Normal University
     943: Yangzhou University
     947: Jiangsu University
     948: Northwestern Polytechnical University
     949: Xiangtan University
     953: Harbin Engineering University
     955: Jinan University
     957: Third Military Medical University
     963: Southern Medical University
     969: South China Agricultural University
     972: Hunan Normal University
     975: Shenzhen University
     976: Tianjin Medical University
     977: Beijing University of Technology
     982: China Medical University (PRC)
     984: Zhejiang University of Technology
     990: Henan Normal University
     991: Xidian University
     993: Southwest Jiaotong University
    1000: China Pharmaceutical University

plt.figure(figsize=(10, 10))
    plt.hist(data_2015_China['world_rank'].tolist(), 10)
    plt.xlabel('World Rank (2015)')
    plt.ylabel('Count')
    plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 4]

plt.figure(figsize=(10, 10))
    sns.histplot(data_2014_China['world_rank'].tolist(), color='blue', label='2014')
    sns.histplot(data_2015_China['world_rank'].tolist(), color='green', label='2015')
    plt.legend()
    plt.show()

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 5][]

2015年榜单共有83所来自中国（大陆地区）的高校入选，相较于2014年减少了1所。从上面的图表来看，大多数仍然分布于800-1000名，但相较于2014年，中国（大陆地区）高校的排名总体上呈现前进趋势。

data_2015_world_rank = data_2015.copy()
    data_2015_world_rank.drop(['national_rank', 'score'], axis=1, inplace=True)
    data_2015_world_rank

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>world_rank</th> 
   <th>institution</th> 
   <th>country</th> 
   <th>quality_of_education</th> 
   <th>alumni_employment</th> 
   <th>quality_of_faculty</th> 
   <th>publications</th> 
   <th>influence</th> 
   <th>citations</th> 
   <th>broad_impact</th> 
   <th>patents</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>1200</th> 
   <td>1</td> 
   <td>Harvard University</td> 
   <td>USA</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1</td> 
   <td>1.0</td> 
   <td>3</td> 
  </tr> 
  <tr> 
   <th>1201</th> 
   <td>2</td> 
   <td>Stanford University</td> 
   <td>USA</td> 
   <td>9</td> 
   <td>2</td> 
   <td>4</td> 
   <td>5</td> 
   <td>3</td> 
   <td>3</td> 
   <td>4.0</td> 
   <td>10</td> 
  </tr> 
  <tr> 
   <th>1202</th> 
   <td>3</td> 
   <td>Massachusetts Institute of Technology</td> 
   <td>USA</td> 
   <td>3</td> 
   <td>11</td> 
   <td>2</td> 
   <td>15</td> 
   <td>2</td> 
   <td>2</td> 
   <td>2.0</td> 
   <td>1</td> 
  </tr> 
  <tr> 
   <th>1203</th> 
   <td>4</td> 
   <td>University of Cambridge</td> 
   <td>United Kingdom</td> 
   <td>2</td> 
   <td>10</td> 
   <td>5</td> 
   <td>11</td> 
   <td>6</td> 
   <td>12</td> 
   <td>13.0</td> 
   <td>48</td> 
  </tr> 
  <tr> 
   <th>1204</th> 
   <td>5</td> 
   <td>University of Oxford</td> 
   <td>United Kingdom</td> 
   <td>7</td> 
   <td>13</td> 
   <td>10</td> 
   <td>7</td> 
   <td>12</td> 
   <td>7</td> 
   <td>9.0</td> 
   <td>15</td> 
  </tr> 
  <tr> 
   <th>...</th> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
   <td>...</td> 
  </tr> 
  <tr> 
   <th>2195</th> 
   <td>996</td> 
   <td>University of the Algarve</td> 
   <td>Portugal</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>926</td> 
   <td>845</td> 
   <td>812</td> 
   <td>969.0</td> 
   <td>816</td> 
  </tr> 
  <tr> 
   <th>2196</th> 
   <td>997</td> 
   <td>Alexandria University</td> 
   <td>Egypt</td> 
   <td>236</td> 
   <td>566</td> 
   <td>218</td> 
   <td>997</td> 
   <td>908</td> 
   <td>645</td> 
   <td>981.0</td> 
   <td>871</td> 
  </tr> 
  <tr> 
   <th>2197</th> 
   <td>998</td> 
   <td>Federal University of Ceará</td> 
   <td>Brazil</td> 
   <td>367</td> 
   <td>549</td> 
   <td>218</td> 
   <td>830</td> 
   <td>823</td> 
   <td>812</td> 
   <td>975.0</td> 
   <td>824</td> 
  </tr> 
  <tr> 
   <th>2198</th> 
   <td>999</td> 
   <td>University of A Coruña</td> 
   <td>Spain</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>886</td> 
   <td>974</td> 
   <td>812</td> 
   <td>975.0</td> 
   <td>651</td> 
  </tr> 
  <tr> 
   <th>2199</th> 
   <td>1000</td> 
   <td>China Pharmaceutical University</td> 
   <td>China</td> 
   <td>367</td> 
   <td>567</td> 
   <td>218</td> 
   <td>861</td> 
   <td>991</td> 
   <td>812</td> 
   <td>981.0</td> 
   <td>547</td> 
  </tr> 
 </tbody> 
</table>

1000 rows × 11 columns

plt.figure(figsize=(10, 10))
    sns.heatmap(data_2015_world_rank.corr(), annot=True)

<AxesSubplot:>

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 6]  
在2015年的榜单中，决定世界排名 (World Rank) 的几个主要因素为广泛影响力 (Broad Impact)、出版物 (Publications)、影响力 (Influence) 等，与2014年一致。

[World University Rankings]: https://www.kaggle.com/mylesoneill/world-university-rankings/
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70]: /images/20221020/51f86761d09545ecafed8abe0d1c6ab1.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 1]: /images/20221020/911dbb44e1224f4aba55a6337bb13dd7.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 3]: /images/20221020/5dab61b57fc244319155625e2aa36a96.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 4]: /images/20221020/4a21e50f2c3b4c63920e7c861ecc788c.png
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ1NTU0MDEw_size_16_color_FFFFFF_t_70 6]: /images/20221020/2d7dbeb81cf14e7d9ef55fec20b96814.png