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

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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