pandas.core.groupby.SeriesGroupBy.corr#

SeriesGroupBy.corr(other, method='pearson', min_periods=None)[source]#

Compute correlation with other Series, excluding missing values.

The two Series objects are not required to be the same length and will be aligned internally before the correlation function is applied.

Parameters:

otherSeries

Series with which to compute the correlation.

method{‘pearson’, ‘kendall’, ‘spearman’} or callable

Method used to compute correlation:

pearson : Standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
callable: Callable with input two 1d ndarrays and returning a float.

Warning

Note that the returned matrix from corr will have 1 along the diagonals and will be symmetric regardless of the callable’s behavior.

min_periodsint, optional

Minimum number of observations needed to have a valid result.

Returns:

float: Correlation with other.

See also

DataFrame.corr: Compute pairwise correlation between columns.
DataFrame.corrwith: Compute pairwise correlation with another DataFrame or Series.

Notes

Pearson, Kendall and Spearman correlation are currently computed using pairwise complete observations.

Automatic data alignment: as with all pandas operations, automatic data alignment is performed for this method. corr() automatically considers values with matching indices.

Examples

>>> def histogram_intersection(a, b):
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> s1 = pd.Series([0.2, 0.0, 0.6, 0.2])
>>> s2 = pd.Series([0.3, 0.6, 0.0, 0.1])
>>> s1.corr(s2, method=histogram_intersection)
0.3

Pandas auto-aligns the values with matching indices

>>> s1 = pd.Series([1, 2, 3], index=[0, 1, 2])
>>> s2 = pd.Series([1, 2, 3], index=[2, 1, 0])
>>> s1.corr(s2)
-1.0

If the input is a constant array, the correlation is not defined in this case, and np.nan is returned.

>>> s1 = pd.Series([0.45, 0.45])
>>> s1.corr(s1)
nan

pandas.core.groupby.SeriesGroupBy.corr#

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