Without actual information/data it is difficult to respond to the inquiry however I suppose you are searching for something like this:
Top15['Citable docs per Capita'].corr(Top15['Energy Supply per Capita'])
That figures the correlation between's your two sections 'Citable docs per Capita' and 'Energy Supply per Capita'.
For 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.
Just if you try to change the value, example
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 near 1, true to form.
In the event that you apply .corr straightforwardly to your dataframe, it will restore all pairwise correlations between's your columns; that is the reason you at that point notice 1s at the diagonal of the matrix (every column is completely connected with itself).
df.corr()
will therefore return
A B
A 1.000000 0.995862
B 0.995862 1.000000
In the realistic you show, just the upper left corner of correlation matrix is addressed (I expect).
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