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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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53 changes: 48 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 +519,64 @@ def nearest(self, limit=None):

def backfill(self, limit=None):
"""
Backward fill the values
Backward fill the values.

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It might be interesting to add a more thorough explanation of what exactly is a backward fill for novice users who have never seem this term. Something along the lines of: 'get all the NA values and substitute with the value on the next row that has a non-NA value'

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if you can find a wikipedia reference would be great as well.

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 NA values with the next
non-NA value in the sequence.

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

Returns
-------
an upsampled Series
Series
An upsampled Series with backward filled NA 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.

Series.fillna : Fill NA/NaN values in the Series using the specified
method, which can be 'backfill'.
DataFrame.fillna : Fill NA/NaN values in the DataFrame using the
specified method, which can be 'backfill'.

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

Examples
--------
>>> 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
"""
return self._upsample('backfill', limit=limit)
bfill = backfill
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