Pandas Series cov() Function:
The cov() function of the Pandas Series calculates the covariance of a Series with other Series, ignoring missing values. The computation automatically excludes both NA and null values.
Syntax:
Series.cov(other, min_periods=None, ddof=1)
Parameters
other: This is required. It indicates a Series for which the covariance has to be computed.
min_periods: This is optional. It is an int that indicates the minimum number of observations required in order for a valid result to be obtained.
ddof: This is optional. Delta Degrees of Freedom is indicated by this argument. N – ddof is the divisor used in calculations, where N is the number of elements.
Return Value:
The covariance of a Series with other Series is returned by the cov() function of the Pandas Series.
 Python Pandas Series var() Function
 Python Pandas Series std() Function
 Python Pandas Series corr() Function
Pandas Series cov() Function in Python
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
 Pass the given second series as an argument to the cov() function and apply it on the given first series to get the covariance 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() # Pass the given second series as an argument to the cov() function and apply it # on the given first series to get the covariance of the first series with the second # and print the result. print("The covariance of first series with the second:") print(gvn_series1.cov(gvn_series2))
Output:
The 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 covariance of first series with the second: 2.989642857142857
Example2
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 cov() function on the student_rollno and student_marks column of the dataframe to get the covariance 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 cov() function on the student_rollno and student_marks column of the # dataframe to get the covariance of student_rollno column of the dataframe # with the student_marks and print the result. print("The covariance of student_rollno column of the dataframe with the student_marks:") print(data_frme['student_rollno'].cov(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 covariance of student_rollno column of the dataframe with the student_marks: 3.333333333333333