没有实际数据，很难回答这个问题，但是我想您正在
寻找这样的东西：

Top15['Citable docs per Capita'].corr(Top15['Energy Supply per Capita'])

That calculates the correlation between your two
columns 'Citable docs per Capita'
and 'Energy Supply per Capita'.

To give an example:

import pandas as pd

df = pd.DataFrame({'A': range(4), 'B': [2*i for i in range(4)]})

   A  B
0  0  0
1  1  2
2  2  4
3  3  6

Then

df['A'].corr(df['B'])

gives 1 as expected.

Now, if you change a value, e.g.

df.loc[2, 'B'] = 4.5

   A    B
0  0  0.0
1  1  2.0
2  2  4.5
3  3  6.0

the command

df['A'].corr(df['B'])

returns

0.99586

which is still close to 1, as expected.

If you apply .corr directly to your dataframe, it will return all pairwise
correlations between your columns; that’s why you then
observe 1s at the diagonal of your matrix (each column is perfectly
correlated with itself).

df.corr()

will therefore return

          A         B
A  1.000000  0.995862
B  0.995862  1.000000

在您显示的图形中，仅表示相关矩阵的左上角（我假设）。

有可能的情况下，你在哪里得到NaN您的解决方案的S -检查这个职位的一个例子。

如果要过滤高于或低于某个阈值的条目，可以检查此问题。如果要绘制相关
系数的热图，则可以检查该答案，如果然后遇到轴标签重叠的问题，请检查以下文章。