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in Python by (16.4k points)

Look at the below pandas dataframe:

I make a column that appraises the number of citable documents per individual: 

Top15['PopEst'] = Top15['Energy Supply'] / Top15['Energy Supply per Capita']

Top15['Citable docs per Capita'] = Top15['Citable documents'] / Top15['PopEst']

I need to know the correlation between's the quantity of citable documents per capita and also the energy supply per capita. So I utilize the .corr() technique (Pearson's relationship): 

data = Top15[['Citable docs per Capita','Energy Supply per Capita']]

correlation = data.corr(method='pearson')

I need to restore a single number, yet the outcome is: enter picture depiction here

1 Answer

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by (26.4k points)

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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