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# Python | Pandas Series.cov() to find Covariance

• Difficulty Level : Easy
• Last Updated : 08 Oct, 2021

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 Series.cov() is used to find covariance of two series. In the following example, covariance is found using both Pandas method and manually ways and the answers are then compared.

Syntax: Series.cov(other, min_periods=None)
Parameters:
other: Other series to be used in finding covariance
min_periods: Minimum number of observations to be taken to have a valid result
Return type: Float value, Returns covariance of caller series and passed series

Example :
In this example, two lists are made and converted to series using Pandas .Series() method. The average if both series is found and a function is created to find Covariance manually. Pandas .cov() is also applied and results from both ways are stored in variables and printed to compare the outputs.

## Python3

 `import` `pandas as pd` `# list  1``a ``=` `[``2``, ``3``, ``2.7``, ``3.2``, ``4.1``]` `# list 2``b ``=` `[``10``, ``14``, ``12``, ``15``, ``20``]` `# storing average of a``av_a ``=` `sum``(a)``/``len``(a)` `# storing average of b``av_b ``=` `sum``(b)``/``len``(b)` `# making series from list a``a ``=` `pd.Series(a)` `# making series from list b``b ``=` `pd.Series(b)``   ` `# covariance through pandas method``covar ``=` `a.cov(b)`  `# finding covariance manually``def` `covarfn(a, b, av_a, av_b):``    ``cov ``=` `0` `    ``for` `i ``in` `range``(``0``, ``len``(a)):``        ``cov ``+``=` `(a[i] ``-` `av_a) ``*` `(b[i] ``-` `av_b)``    ``return` `(cov ``/` `(``len``(a)``-``1``))` `# calling function``cov ``=` `covarfn(a, b, av_a, av_b)` `# printing results``print``(``"Results from Pandas method: "``, covar)``print``(``"Results from manual function method: "``, cov)`

Output:

As it can be seen in output, the output from both ways is same. Hence this method is useful when finding co variance for large series.

```Results from Pandas method:  2.8499999999999996
Results from manual function method:  2.8499999999999996```

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