pandas.core.resample.Resampler.bfill#

finalResampler.bfill(limit=None)[source]#

Backward fill the new missing values in the resampled data.

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

Parameters:

limitint, optional

Limit of how many values to fill.

Returns:

Series, DataFrame

An upsampled Series or DataFrame with backward filled NaN values.

See also

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.

ffill

Forward fill NaN values.

Series.fillna

Fill NaN values in the Series using the specified method, which can be ‘backfill’.

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'))>>>s2018-01-01 00:00:00    12018-01-01 01:00:00    22018-01-01 02:00:00    3Freq: h, dtype: int64

>>>s.resample('30min').bfill()2018-01-01 00:00:00    12018-01-01 00:30:00    22018-01-01 01:00:00    22018-01-01 01:30:00    32018-01-01 02:00:00    3Freq: 30min, dtype: int64

>>>s.resample('15min').bfill(limit=2)2018-01-01 00:00:00    1.02018-01-01 00:15:00    NaN2018-01-01 00:30:00    2.02018-01-01 00:45:00    2.02018-01-01 01:00:00    2.02018-01-01 01:15:00    NaN2018-01-01 01:30:00    3.02018-01-01 01:45:00    3.02018-01-01 02:00:00    3.0Freq: 15min, 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  b2018-01-01 00:00:00  2.0  12018-01-01 01:00:00  NaN  32018-01-01 02:00:00  6.0  5

>>>df.resample('30min').bfill()                       a  b2018-01-01 00:00:00  2.0  12018-01-01 00:30:00  NaN  32018-01-01 01:00:00  NaN  32018-01-01 01:30:00  6.0  52018-01-01 02:00:00  6.0  5

>>>df.resample('15min').bfill(limit=2)                       a    b2018-01-01 00:00:00  2.0  1.02018-01-01 00:15:00  NaN  NaN2018-01-01 00:30:00  NaN  3.02018-01-01 00:45:00  NaN  3.02018-01-01 01:00:00  NaN  3.02018-01-01 01:15:00  NaN  NaN2018-01-01 01:30:00  6.0  5.02018-01-01 01:45:00  6.0  5.02018-01-01 02:00:00  6.0  5.0