Pandas Series corr() Function:
.corr() pandas: The corr() function of the Pandas Series calculates the correlation of a Series with other Series while ignoring missing values. The computation automatically excludes NA and null values.
Syntax:
Series.corr(other, method='pearson', min_periods=None)
Parameters
other: This is required. It indicates a Series with which the correlation has to be computed.
method: This is optional. It is the correlation method. The default value is ‘pearson.’ Possible values include:
 Pearson – Coefficient of standard correlation
 kendall – Coefficient of Kendall Tau correlation
 Spearman Spearman rank correlation
 callable – callable with two 1d ndarrays as input and returns a float. Note that regardless of how the callable behaves, the returned correlation matrix will have 1 along the diagonals and will be symmetric.
min_periods: This is optional. It is an int indicating the minimum number of observations required for a valid result.
Return Value:
The correlation of a Series with other Series is returned by the corr() function of the Pandas Series.
 Python Pandas Series cov() Function
 Python Pandas Series ge() Function
 Python Pandas Series eq() Function
Python Pandas Series corr() Function
Example1
Approach:
 Import pandas module using the import keyword.
 Pass some random list as an argument to the Series() function of the pandas module to create a series.
 Store it in a variable.
Pass some random list as an argument to the Series() function of the pandas module to create another series.  Store it in another variable.
 Print the above given first series
 Print the above given second series
 Apply corr() function on the given first and second series to get the correlation of the first series with the second and print the result.
 The Exit of the Program.
Below is the implementation:
# Import pandas module using the import keyword. import pandas as pd # Pass some random list as an argument to the Series() function # of the pandas module to create a series. # Store it in a variable. gvn_series1 = pd.Series([3, 3.2, 10.1, 5.3, 4, 2, 3, 6]) # Pass some random list as an argument to the Series() function # of the pandas module to create another series. # Store it in another variable. gvn_series2 = pd.Series([3.1, 3, 7.2, 6, 8, 2.5, 3, 1.1]) # Print the above given first series print("The given first series is:") print(gvn_series1) print() # Print the above given second series print("The given second series is:") print(gvn_series2) print() # Apply corr() function on the given first and second series to get the # correlation of first series with the second # and print the result. print("The correlation of first series with the second:") print(gvn_series1.corr(gvn_series2))
Output:
he given first series is: 0 3.0 1 3.2 2 10.1 3 5.3 4 4.0 5 2.0 6 3.0 7 6.0 dtype: float64 The given second series is: 0 3.1 1 3.0 2 7.2 3 6.0 4 8.0 5 2.5 6 3.0 7 1.1 dtype: float64 The correlation of first series with the second: 0.46464053797455707
Example2
Corr python: Here, the corr() function is used on a specific series/column in a DataFrame.
Approach:
 Import pandas module using the import keyword.
 Pass some random keyvalue pair(dictionary), index list as arguments to the DataFrame() function of the pandas module to create a dataframe.
 Store it in a variable.
 Print the given dataframe.
 Apply corr() function on the student_rollno and student_marks column of the dataframe to get the correlation of student_rollno column of the dataframe with the student_marks and print the result.

The Exit of the Program.
Below is the implementation:
# Import pandas module using the import keyword. import pandas as pd # Pass some random keyvalue pair(dictionary), index list as arguments to the # DataFrame() function of the pandas module to create a dataframe # Store it in a variable. data_frme = pd.DataFrame({ "student_rollno": [1, 2, 3, 4], "student_marks": [80, 35, 25, 90]}, index= ["virat", "nick" , "jessy", "sindhu"] ) # Print the given dataframe print("The given Dataframe:") print(data_frme) print() # Apply corr() function on the student_rollno and student_marks column of the # dataframe to get the correlation of student_rollno column of the dataframe # with the student_marks and print the result. print("The correlation of student_rollno column of the dataframe with the student_marks:") print(data_frme['student_rollno'].corr(data_frme['student_marks']))
Output:
The given Dataframe: student_rollno student_marks virat 1 80 nick 2 35 jessy 3 25 sindhu 4 90 The correlation of student_rollno column of the dataframe with the student_marks: 0.08