dask_expr._collection.Index.corr
dask_expr._collection.Index.corr¶
- Index.corr(other, method='pearson', min_periods=None, split_every=False)¶
Compute correlation with other Series, excluding missing values.
This docstring was copied from pandas.core.series.Series.corr.
Some inconsistencies with the Dask version may exist.
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([.2, .0, .6, .2]) >>> s2 = pd.Series([.3, .6, .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