modin.pandas.Resampler.std¶
- Resampler.std(ddof: int = 1, numeric_only: bool = False, *args: Any, **kwargs: Any) Union[DataFrame, Series] [source]¶
Compute standard deviation of resample bins.
- Parameters:
ddof (int, default 1) – Delta Degrees of Freedom. The divisor used in calculations is N - ddof, where N represents the number of elements.
numeric_only (bool, default False) – Include only float, int, boolean columns.
min_count (int, default 0) – The required number of valid values to perform the operation. If fewer than
min_count
non-NA values are present, the result will be NA.engine (str, default None) – This parameter is ignored in Snowpark pandas. The execution engine will always be Snowflake.
engine_kwargs (dict, default None) – This parameter is ignored in Snowpark pandas. The execution engine will always be Snowflake.
- Returns:
Computed standard deviation of values within each resample bin.
- Return type:
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').std() 2020-01-01 0.707107 2020-01-03 0.707107 Freq: None, dtype: float64
>>> 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').std() 2020-01-01 00:00:00 0.707107 2020-01-01 00:00:02 NaN Freq: None, dtype: float64
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').std() a b c 2020-01-01 0.0 4.242641 2.121320 2020-01-03 0.0 0.707107 0.707107