pyspark.pandas.window.Expanding.std#

Expanding.std()[source]#

Calculate expanding standard deviation.

Note

the current implementation of this API uses Spark’s Window without specifying partition specification. This leads to move all data into single partition in single machine and could cause serious performance degradation. Avoid this method against very large dataset.

Returns:
Series or DataFrame

Returns the same object type as the caller of the expanding calculation.

See also

pyspark.pandas.Series.expanding

Calling object with Series data.

pyspark.pandas.DataFrame.expanding

Calling object with DataFrames.

pyspark.pandas.Series.std

Equivalent method for Series.

pyspark.pandas.DataFrame.std

Equivalent method for DataFrame.

numpy.std

Equivalent method for Numpy array.

Examples

>>> s = ps.Series([5, 5, 6, 7, 5, 5, 5])
>>> s.expanding(3).std()
0         NaN
1         NaN
2    0.577350
3    0.957427
4    0.894427
5    0.836660
6    0.786796
dtype: float64

For DataFrame, each expanding standard deviation variance is computed column-wise.

>>> df = ps.DataFrame({"A": s.to_numpy(), "B": s.to_numpy() ** 2})
>>> df.expanding(2).std()
          A          B
0       NaN        NaN
1  0.000000   0.000000
2  0.577350   6.350853
3  0.957427  11.412712
4  0.894427  10.630146
5  0.836660   9.928075
6  0.786796   9.327379