snowflake.snowpark.modin.plugin.extensions.resample_overrides.Resampler.ffill

Resampler.ffill(limit: Optional[int] = None) → 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#L238-L254)

Forward fill values for missing resample bins.

Parameters:

limit (int, optional) – This parameter is not supported and will raise NotImplementedError.

Returns:

A DataFrame with values forward filled for missing resample bins.

Return type:

Series or DataFrame

Examples

For Series: >>> lst1 = pd.to_datetime([‘2020-01-03’, ‘2020-01-04’, ‘2020-01-05’, ‘2020-01-07’, ‘2020-01-08’]) >>> ser1 = pd.Series([1, 2, 3, 4, 5], index=lst1) >>> ser1 2020-01-03 1 2020-01-04 2 2020-01-05 3 2020-01-07 4 2020-01-08 5 Freq: None, dtype: int64

>>> ser1.resample('1D').ffill()
2020-01-03    1
2020-01-04    2
2020-01-05    3
2020-01-06    3
2020-01-07    4
2020-01-08    5
Freq: None, dtype: int64
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>>> ser1.resample('3D').ffill()
2020-01-03    1
2020-01-06    3
Freq: None, dtype: int64
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>>> lst2 = pd.to_datetime(pd.Index(['2023-01-03 1:00:00', '2023-01-04', '2023-01-05 23:00:00', '2023-01-06', '2023-01-07 2:00:00', '2023-01-10']))
>>> ser2 = pd.Series([1, 2, 3, 4, None, 6], index=lst2)
>>> ser2
2023-01-03 01:00:00    1.0
2023-01-04 00:00:00    2.0
2023-01-05 23:00:00    3.0
2023-01-06 00:00:00    4.0
2023-01-07 02:00:00    NaN
2023-01-10 00:00:00    6.0
Freq: None, dtype: float64
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>>> ser2.resample('1D').ffill()
2023-01-03    NaN
2023-01-04    2.0
2023-01-05    2.0
2023-01-06    4.0
2023-01-07    4.0
2023-01-08    NaN
2023-01-09    NaN
2023-01-10    6.0
Freq: None, dtype: float64
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>>> ser2.resample('2D').ffill()
2023-01-03    NaN
2023-01-05    2.0
2023-01-07    4.0
2023-01-09    NaN
Freq: None, dtype: float64
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For DataFrame:

>>> index1 = pd.to_datetime(['2020-01-03', '2020-01-04', '2020-01-05', '2020-01-07', '2020-01-08'])
>>> df1 = pd.DataFrame({'a': range(len(index1)),
... 'b': range(len(index1) + 10, len(index1) * 2 + 10)},
...  index=index1)
>>> df1
            a   b
2020-01-03  0  15
2020-01-04  1  16
2020-01-05  2  17
2020-01-07  3  18
2020-01-08  4  19
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>>> df1.resample('1D').ffill()
            a   b
2020-01-03  0  15
2020-01-04  1  16
2020-01-05  2  17
2020-01-06  2  17
2020-01-07  3  18
2020-01-08  4  19
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>>> df1.resample('3D').ffill()
            a   b
2020-01-03  0  15
2020-01-06  2  17
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>>> index2 = pd.to_datetime(pd.Index(['2023-01-03 1:00:00', '2023-01-04', '2023-01-05 23:00:00', '2023-01-06', '2023-01-07 2:00:00', '2023-01-10']))
>>> df2 = pd.DataFrame({'a': range(len(index2)),
... 'b': range(len(index2) + 10, len(index2) * 2 + 10)},
...  index=index2)
>>> df2
                     a   b
2023-01-03 01:00:00  0  16
2023-01-04 00:00:00  1  17
2023-01-05 23:00:00  2  18
2023-01-06 00:00:00  3  19
2023-01-07 02:00:00  4  20
2023-01-10 00:00:00  5  21
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>>> df2.resample('1D').ffill()
              a     b
2023-01-03  NaN   NaN
2023-01-04  1.0  17.0
2023-01-05  1.0  17.0
2023-01-06  3.0  19.0
2023-01-07  3.0  19.0
2023-01-08  4.0  20.0
2023-01-09  4.0  20.0
2023-01-10  5.0  21.0
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>>> df2.resample('2D').ffill()
              a     b
2023-01-03  NaN   NaN
2023-01-05  1.0  17.0
2023-01-07  3.0  19.0
2023-01-09  4.0  20.0
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