What is corrwith function in Pandas?

The corrwith function in Pandas computes pair-wise correlations between rows and columns of a dataframe with rows and columns of a series or dataframe. Rows and columns of the dataframe and the other object are first matched before computing the correlations.

Correlation matrix

A correlation matrix shows the degree of the linear relationship between variables in a dataset. It indicates the correlation using the correlation coefficient.

The correlation coefficient shows how strongly or weakly any two variables are related. Scores range between 1 and -1. 1 indicates a perfect positive correlation, whereas -1 indicates a perfect negative correlation. Scores closer to 0 indicate a weak correlation.

Syntax

The syntax of the corrwith function is as follows:

DataFrame.corrwith(other, axis=0, drop=False, method='pearson')

Parameters

The corrwith functions require at least one parameter: other. The rest are optional.

The table below describes the parameters of the corrwith function:

Parameters	Description
`other`	Refers to a series or a dataframe. It is the object with which a correlation is computed.
`axis`	The axis to be used. `0` refers to column-wise computation. `1` refers to row-wise. Bu default, it is `0`.
`drop`	Used to drop missing indices from the result. Takes a `bool` value. By default, it is `False`.
`method`	The method to use for computing correlation. Can be `pearson`, `kendall`, `spearman` or `callable`

Methods of computing correlations.

There are three main methods of computing correlations:

Pearson: standard correlation coefficient
Kendall: Kendall Tau correlation coefficient
Spearman: Spearman rank correlation

callable refers to inputting two one-dimensional arrays and returning a float.

Return value

The corrwith function returns a matrix with pairwise correlations.

Example

The code snippet below shows how the corrwith function can be used in Pandas:

import pandas as pd # for creating a dataframe
# Data for matrix
data = {'A': [45,37,42,35,39],
        'B': [38,31,26,28,33],
        'C': [10,15,17,21,12]
        }
df = pd.DataFrame(data,columns=['A','B','C'])
print("Original dataframe")
print(df) # original df
print("\n")
corrMatrix = df.corrwith(df["B"]) # finding correlations
print("Between column B and the rest of the dataframe")
print("Correlation Coefficients Matrix")
print(corrMatrix) # printing correlations
print('\n')
corrMatrix = df.corrwith(df["C"]) # finding correlations
print("Between column C and the rest of the dataframe")
print("Correlation Coefficients Matrix")
print(corrMatrix) # printing correlations
print('\n')
corrMatrix = df.corrwith(df["C"]) # finding correlations
print("Between column C and the rest of the dataframe")
print("Correlation Coefficients Matrix")
print(corrMatrix) # printing correlations

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