Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandasis one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.corrwith() is used to compute pairwise correlation between rows or columns of two DataFrame objects. If the shape of two dataframe object is not same then the corresponding correlation value will be a NaN value.
Syntax: DataFrame.count(axis=0, level=None, numeric_only=False)
Parameters:
other : DataFrame
axis : 0 or ‘index’ to compute column-wise, 1 or ‘columns’ for row-wise
drop : Drop missing indices from result, default returns union of allReturns: correls : Series
Note: The correlation of a variable with itself is 1.
Example #1: Use corrwith() function to find the correlation among two dataframe objects along the column axis
# importing pandas as pdimport pandas as pd # Creating the first dataframedf1 = pd.DataFrame({"A":[1, 5, 7, 8], "B":[5, 8, 4, 3], "C":[10, 4, 9, 3]}) # Creating the second dataframe df2 = pd.DataFrame({"A":[5, 3, 6, 4], "B":[11, 2, 4, 3], "C":[4, 3, 8, 5]}) # Print the first dataframeprint(df1, "\n") # Print the second dataframeprint(df2) |
Now find the correlation among the columns of the two data frames along the row axis.
# To find the correlation among the# columns of df1 and df2 along the column axisdf1.corrwith(df2, axis = 0) |
Output :
The output series contains the correlation between the three columns of two dataframe objects respectively.
Example #2: Use corrwith() function to find the correlation among two dataframe objects along the row axis
# importing pandas as pdimport pandas as pd # Creating the first dataframedf1 = pd.DataFrame({"A":[1, 5, 7, 8], "B":[5, 8, 4, 3], "C":[10, 4, 9, 3]}) # Creating the second dataframe df2 = pd.DataFrame({"A":[5, 3, 6, 4], "B":[11, 2, 4, 3], "C":[4, 3, 8, 5]}) # To find the correlation among the# columns of df1 and df2 along the row axisdf1.corrwith(df2, axis = 1) |
Output :
The output series contains the correlation between the four rows of two data frame objects respectively.
