pyspark.pandas.window.Rolling.count¶

Rolling.count() → FrameLike¶

The rolling count of any non-NaN observations inside the window.

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.expandingCalling object with Series data.
DataFrame.expandingCalling object with DataFrames.
Series.countCount of the full Series.
DataFrame.countCount of the full DataFrame.

Examples

>>> s = ps.Series([2, 3, float("nan"), 10])
>>> s.rolling(1).count()
0    1.0
1    1.0
2    0.0
3    1.0
dtype: float64

>>> s.rolling(3).count()
0    1.0
1    2.0
2    2.0
3    2.0
dtype: float64

>>> s.to_frame().rolling(1).count()
     0
0  1.0
1  1.0
2  0.0
3  1.0

>>> s.to_frame().rolling(3).count()
     0
0  1.0
1  2.0
2  2.0
3  2.0

Window

pyspark.pandas.window.Rolling.sum