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snowflake.snowpark.modin.plugin.extensions.resample_overrides.Resampler.count¶

Resampler.count() → Union[DataFrame, Series][source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/resample_overrides.py#L337-L349)¶

Compute count of resample bins, exclude missing values.

Returns:: Computed count of values within each resample bin.
Return type:: Series or DataFrame

Examples

For Series:

>>> lst1 = pd.date_range('2020-01-01', periods=4, freq='1D')
>>> ser1 = pd.Series([1, 2, 3, 4], index=lst1)
>>> ser1
2020-01-01    1
2020-01-02    2
2020-01-03    3
2020-01-04    4
Freq: None, dtype: int64

>>> ser1.resample('2D').count()
2020-01-01    2
2020-01-03    2
Freq: None, dtype: int64

>>> lst2 = pd.date_range('2020-01-01', periods=4, freq='S')
>>> ser2 = pd.Series([1, 2, np.nan, 4], index=lst2)
>>> ser2
2020-01-01 00:00:00    1.0
2020-01-01 00:00:01    2.0
2020-01-01 00:00:02    NaN
2020-01-01 00:00:03    4.0
Freq: None, dtype: float64

>>> ser2.resample('2S').count()
2020-01-01 00:00:00    2
2020-01-01 00:00:02    1
Freq: None, dtype: int64

For DataFrame:

>>> data = [[1, 8, 2], [1, 2, 5], [2, 5, 8], [2, 6, 9]]
>>> df = pd.DataFrame(data,
...      columns=["a", "b", "c"],
...      index=pd.date_range('2020-01-01', periods=4, freq='1D'))
>>> df
            a  b  c
2020-01-01  1  8  2
2020-01-02  1  2  5
2020-01-03  2  5  8
2020-01-04  2  6  9

>>> df.resample('2D').count()
            a  b  c
2020-01-01  2  2  2
2020-01-03  2  2  2