# pandas.core.resample.Resampler.backfill

Resampler.backfill(limit=None)

  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 original data will not be modified.

  Parameters
    limit int, 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.

  `pad`

    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'))
      >>> 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