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

Resampler.max(numeric_only: bool = False, min_count: int = 0, *args: Any, **kwargs: Any) → Union[DataFrame, Series][source] (https://github.com/snowflakedb/snowpark-python/blob/v1.30.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/resample_overrides.py#L411-L432)¶

Compute maximum of resample bins.

Parameters:

• 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 maximum 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

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>>> ser1.resample('2D').max()
2020-01-01    2
2020-01-03    4
Freq: None, dtype: int64

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>>> 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

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>>> ser2.resample('2S').max()
2020-01-01 00:00:00    2.0
2020-01-01 00:00:02    4.0
Freq: None, dtype: float64

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For DataFrame:

>>> data = [[1, 8], [1, 2], [2, 5], [2, 6]]
>>> df1 = pd.DataFrame(data,
... columns=["a", "b"],
... index=pd.date_range('2020-01-01', periods=4, freq='1D'))
>>> df1
            a  b
2020-01-01  1  8
2020-01-02  1  2
2020-01-03  2  5
2020-01-04  2  6

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>>> df1.resample('2D').max()
            a  b
2020-01-01  1  8
2020-01-03  2  6

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>>> df2 = pd.DataFrame(
... {'A': [1, 2, 3, np.nan], 'B': [np.nan, np.nan, 3, 4]},
... index=pd.date_range('2020-01-01', periods=4, freq='1S'))
>>> df2
                       A    B
2020-01-01 00:00:00  1.0  NaN
2020-01-01 00:00:01  2.0  NaN
2020-01-01 00:00:02  3.0  3.0
2020-01-01 00:00:03  NaN  4.0

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>>> df2.resample('2S').max()
                       A    B
2020-01-01 00:00:00  2.0  NaN
2020-01-01 00:00:02  3.0  4.0

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