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
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。
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