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

• Difficulty Level : Easy
• Last Updated : 10 Oct, 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 `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 Covarience manually. Pandas `.cov()` is also applied and results from both ways are stored in variables and printed to compare the outputs.

 `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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