Use .corr to get the correlation between two columns

I ran into the same issue.
It appeared Citable Documents per Person was a float, and python skips it somehow by default. All the other columns of my dataframe were in numpy-formats, so I solved it by converting the columnt to np.float64

Top15['Citable Documents per Person']=np.float64(Top15['Citable Documents per Person'])

Remember it’s exactly the column you calculated yourself

If you want the correlations between all pairs of columns, you could do something like this:

import pandas as pd
import numpy as np

def get_corrs(df):
    col_correlations = df.corr()
    col_correlations.loc[:, :] = np.tril(col_correlations, k=-1)
    cor_pairs = col_correlations.stack()
    return cor_pairs.to_dict()

my_corrs = get_corrs(df)
# and the following line to retrieve the single correlation
print(my_corrs[('Citable docs per Capita','Energy Supply per Capita')])

I solved this problem by changing the data type. If you see the ‘Energy Supply per Capita’ is a numerical type while the ‘Citable docs per Capita’ is an object type. I converted the column to float using astype. I had the same problem with some np functions: count_nonzero and sum worked while mean and std didn’t.

When you call this:

data = Top15[['Citable docs per Capita','Energy Supply per Capita']]
correlation = data.corr(method='pearson')

Since, DataFrame.corr() function performs pair-wise correlations, you have four pair from two variables. So, basically you are getting diagonal values as auto correlation (correlation with itself, two values since you have two variables), and other two values as cross correlations of one vs another and vice versa.

Either perform correlation between two series to get a single value:

from scipy.stats.stats import pearsonr
docs_col = Top15['Citable docs per Capita'].values
energy_col = Top15['Energy Supply per Capita'].values
corr , _ = pearsonr(docs_col, energy_col)

or,
if you want a single value from the same function (DataFrame’s corr):

single_value = correlation[0][1] 

Hope this helps.

My solution would be after converting data to numerical type:

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

It works like this:

Top15['Citable docs per Capita']=np.float64(Top15['Citable docs per Capita'])

Top15['Energy Supply per Capita']=np.float64(Top15['Energy Supply per Capita'])

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

changing ‘Citable docs per Capita’ to numeric before correlation will solve the problem.

    Top15['Citable docs per Capita'] = pd.to_numeric(Top15['Citable docs per Capita'])
    data = Top15[['Citable docs per Capita','Energy Supply per Capita']]
    correlation = data.corr(method='pearson')

The following works for me. Taking the correlation matrix, then filter based on variable names:

cor_df = df.corr()  # take the correlation from the data
cor_df.loc['Citable docs per Capita','Energy Supply per Capita'] # only single value

if you put variables in [], it return variable names as well:

cor_df.loc[['Citable docs per Capita'],['Energy Supply per Capita']]

If you want a quick way of finding and plotting the correlation between all the columns with respect to a single column (‘Energy Supply per Capita’, in this case):

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
cor = []
plt.figure()
for i in data.columns:
    try:
        cor +=  [np.corrcoef(data[i], data['Energy Supply per Capita'])[0][1]]
        plt.scatter(i, np.corrcoef(data[i], data['Energy Supply per Capita'])[0][1])
        plt.axvline(x = i, color='k', linestyle='-',alpha=0.2)
    except:
        pass
plt.ylabel('correlation')
plt.xticks(rotation = 90)