Related Articles

# Python | Pandas dataframe.cov()

• Last Updated : 16 Nov, 2018

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.cov()` is used to compute pairwise covariance of columns.
If some of the cells in a column contain `NaN` value, then it is ignored.

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

Syntax: DataFrame.cov(min_periods=None)

Parameters:
min_periods : Minimum number of observations required per pair of columns to have a valid result.

Returns: y : DataFrame

Example #1: Use `cov()` function to find the covariance between the columns of the dataframe.

Note : Any non-numeric columns will be ignored.

 `# importing pandas as pd``import` `pandas as pd`` ` `# Creating the dataframe``df ``=` `pd.DataFrame({``"A"``:[``5``, ``3``, ``6``, ``4``], ``                   ``"B"``:[``11``, ``2``, ``4``, ``3``],``                   ``"C"``:[``4``, ``3``, ``8``, ``5``],``                   ``"D"``:[``5``, ``4``, ``2``, ``8``]})`` ` `# Print the dataframe``df`

Output : Now find the covariance among the columns of the data frame

 `# To find the covariance ``df.cov()`

Output : Example #2: Use `cov()` function to find the covariance between the columns of the dataframe which are having `NaN` value.

 `# importing pandas as pd``import` `pandas as pd`` ` `# Creating the dataframe``df ``=` `pd.DataFrame({``"A"``:[``5``, ``3``, ``None``, ``4``],``                   ``"B"``:[``None``, ``2``, ``4``, ``3``],``                   ``"C"``:[``4``, ``3``, ``8``, ``5``], ``                   ``"D"``:[``5``, ``4``, ``2``, ``None``]})`` ` `# To find the covariance ``df.cov()`

Output : My Personal Notes arrow_drop_up