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DOC: update the pandas.core.resample.Resampler.backfill docstring #20083

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DOC: update the pandas.core.resample.Resampler.backfill docstring #20083

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DOC: Resampler.backfill docstring
Mar 9, 2018
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DOC: Improve description, add example
gcbeltramini Mar 11, 2018
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93 changes: 88 additions & 5 deletions pandas/core/resample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -519,21 +519,104 @@ def nearest(self, limit=None):

def backfill(self, limit=None):
"""
Backward fill the values
Backward fill the new missing values in the resampled data.

In statistics, imputation is the process of replacing missing data with
substituted values [1]_. When resampling data, missing values may
appear (e.g., when the resampling frequency is higher than the original
frequency). The backward fill will replace NaN values that appeared in
the resampled data with the next value in the original sequence.
Missing values that existed in the orginal data will not be modified.

Parameters
----------
limit : integer, optional
limit of how many values to fill
Limit of how many values to fill.

Returns
-------
an upsampled Series
Series, DataFrame
An upsampled Series or DataFrame with backward filled NaN values.

See Also
--------
Series.fillna
DataFrame.fillna
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can you add a Resampler.pad, nearest, and fillna refs @jorisvandenbossche how do we reference these exactly here?

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It might be you can simply refer to them as 'pad', 'nearest', .. because they live on the same class. But need to check.

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If I run python make.py --single pandas.core.resample.Resampler.backfill, the links are never created. But if I run python make.py html, the links are created correctly if I use: pandas.Series.fillna (Series.fillna and pandas.core.series.Series.fillna don't work) and fillna or Resampler.fillna.

bfill : Alias of backfill.
fillna : Fill NaN values using the specified method, which can be
'backfill'.
nearest : Fill NaN values with nearest neighbor starting from center.
pad : Forward fill NaN values.
pandas.Series.fillna : Fill NaN values in the Series using the
specified method, which can be 'backfill'.
pandas.DataFrame.fillna : Fill NaN values in the DataFrame using the
specified method, which can be 'backfill'.

References
----------
.. [1] https://en.wikipedia.org/wiki/Imputation_(statistics)

Examples
--------

Resampling a Series:

>>> s = pd.Series([1, 2, 3],
... index=pd.date_range('20180101', periods=3, freq='h'))
>>> s
2018-01-01 00:00:00 1
2018-01-01 01:00:00 2
2018-01-01 02:00:00 3
Freq: H, dtype: int64

>>> s.resample('30min').backfill()
2018-01-01 00:00:00 1
2018-01-01 00:30:00 2
2018-01-01 01:00:00 2
2018-01-01 01:30:00 3
2018-01-01 02:00:00 3
Freq: 30T, dtype: int64

>>> s.resample('15min').backfill(limit=2)
2018-01-01 00:00:00 1.0
2018-01-01 00:15:00 NaN
2018-01-01 00:30:00 2.0
2018-01-01 00:45:00 2.0
2018-01-01 01:00:00 2.0
2018-01-01 01:15:00 NaN
2018-01-01 01:30:00 3.0
2018-01-01 01:45:00 3.0
2018-01-01 02:00:00 3.0
Freq: 15T, dtype: float64

Resampling a DataFrame that has missing values:

>>> df = pd.DataFrame({'a': [2, np.nan, 6], 'b': [1, 3, 5]},
... index=pd.date_range('20180101', periods=3,
... freq='h'))
>>> df
a b
2018-01-01 00:00:00 2.0 1
2018-01-01 01:00:00 NaN 3
2018-01-01 02:00:00 6.0 5

>>> df.resample('30min').backfill()
a b
2018-01-01 00:00:00 2.0 1
2018-01-01 00:30:00 NaN 3
2018-01-01 01:00:00 NaN 3
2018-01-01 01:30:00 6.0 5
2018-01-01 02:00:00 6.0 5

>>> df.resample('15min').backfill(limit=2)
a b
2018-01-01 00:00:00 2.0 1.0
2018-01-01 00:15:00 NaN NaN
2018-01-01 00:30:00 NaN 3.0
2018-01-01 00:45:00 NaN 3.0
2018-01-01 01:00:00 NaN 3.0
2018-01-01 01:15:00 NaN NaN
2018-01-01 01:30:00 6.0 5.0
2018-01-01 01:45:00 6.0 5.0
2018-01-01 02:00:00 6.0 5.0
"""
return self._upsample('backfill', limit=limit)
bfill = backfill
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