# pandas.Series.corr¶

Compute correlation with other Series, excluding missing values.

- param other
- Series
Series with which to compute the correlation.

- param method
- {‘pearson’, ‘kendall’, ‘spearman’} or callable
pearson : standard correlation coefficient

kendall : Kendall Tau correlation coefficient

spearman : Spearman rank correlation

- callable: callable with input two 1d ndarrays
and returning a float. Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior .. versionadded:: 0.24.0

- param min_periods
- int, optional
Minimum number of observations needed to have a valid result.

- return
float Correlation with other.

## Limitations¶

Parameter

`method`

is supported only with default value ‘pearson’

## Examples¶

```
import numpy as np
import pandas as pd
from numba import njit
@njit
def series_corr():
s1 = pd.Series([3.2, -10, np.nan, 0.23, 9.2])
s2 = pd.Series([5., 0, 3.3, np.nan, 9.2])
return s1.corr(s2) # Expect value: 0.98673...
print(series_corr())
```

```
$ python ./series/series_corr.py
0.9867362434412106
```

See also

- Series.cov
Compute covariance with Series, excluding missing values.