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.
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.
Output :
👁 Image
Now find the covariance among the columns of the data frame
Output :
👁 Image
Example #2: Use
cov() function to find the covariance between the columns of the dataframe which are having
NaN value.
