Python | Pandas dataframe.corr()
Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.
Pandas dataframe.corr() is used to find the pairwise correlation of all columns in the dataframe. Any na values are automatically excluded. For any non-numeric data type columns in the dataframe it is ignored.
Syntax: DataFrame.corr(self, method=’pearson’, min_periods=1)
Parameters:
method :pearson: standard correlation coefficientkendall: Kendall Tau correlation coefficientspearman: Spearman rank correlation
min_periods : Minimum number of observations required per pair of columns to have a valid result. Currently only available for pearson and spearman correlationReturns: count :y : DataFrame
Note: The correlation of a variable with itself is 1.
For link to CSV file Used in Code, click here
Example #1: Use corr() function to find the correlation among the columns in the dataframe using ‘Pearson’ method.
# importing pandas as pdimport pandas as pd # Making data frame from the csv filedf = pd.read_csv("nba.csv") # Printing the first 10 rows of the data frame for visualizationdf[:10] |
Now use corr() function to find the correlation among the columns. We are only having four numeric columns in the dataframe.
# To find the correlation among# the columns using pearson methoddf.corr(method ='pearson') |
Output :
The output dataframe can be interpreted as for any cell, row variable correlation with the column variable is the value of the cell. As mentioned earlier, that the correlation of a variable with itself is 1. For that reason all the diagonal values are 1.00
Example #2: Use corr() function to find the correlation among the columns in the dataframe using ‘kendall’ method.
# importing pandas as pdimport pandas as pd # Making data frame from the csv filedf = pd.read_csv("nba.csv") # To find the correlation among# the columns using kendall methoddf.corr(method ='kendall') |
Output :
The output dataframe can be interpreted as for any cell, row variable correlation with the column variable is the value of the cell. As mentioned earlier, that the correlation of a variable with itself is 1. For that reason all the diagonal values are 1.00.
Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.
To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course. And to begin with your Machine Learning Journey, join the Machine Learning – Basic Level Course
