# pandas.core.resample.Resampler.fillna

`Resampler.``fillna`(method, limit=None)

  Fill missing values introduced by upsampling.

  In statistics, imputation is the process of replacing missing data with substituted values [R15]. When resampling data, missing values may appear (e.g., when the resampling frequency is higher than the original frequency).

  Missing values that existed in the original data will not be modified.

  Parameters:

  method : {‘pad’, ‘backfill’, ‘ffill’, ‘bfill’, ‘nearest’}

  > Method to use for filling holes in resampled data

  > - ‘pad’ or ‘ffill’: use previous valid observation to fill gap (forward fill).
  > - ‘backfill’ or ‘bfill’: use next valid observation to fill gap.
  > - ‘nearest’: use nearest valid observation to fill gap.

  limit : integer, optional

  > Limit of how many consecutive missing values to fill.

  Returns:

  Series or DataFrame

  > An upsampled Series or DataFrame with missing values filled.

  See also

  `backfill`
    Backward fill NaN values in the resampled data.
  `pad`
    Forward fill NaN values in the resampled data.
  `nearest`
    Fill NaN values in the resampled data with nearest neighbor starting from center.
  `interpolate`
    Fill NaN values using interpolation.
  `Series.fillna`
    Fill NaN values in the Series using the specified method, which can be ‘bfill’ and ‘ffill’.
  `DataFrame.fillna`
    Fill NaN values in the DataFrame using the specified method, which can be ‘bfill’ and ‘ffill’.

  References

  [R15] | (1, 2) 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

  Without filling the missing values you get:

      >>> s.resample("30min").asfreq()
      2018-01-01 00:00:00    1.0
      2018-01-01 00:30:00    NaN
      2018-01-01 01:00:00    2.0
      2018-01-01 01:30:00    NaN
      2018-01-01 02:00:00    3.0
      Freq: 30T, dtype: float64

      >>> s.resample('30min').fillna("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').fillna("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

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

      >>> s.resample('30min').fillna("nearest")
      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

  Missing values present before the upsampling are not affected.

      >>> sm = pd.Series([1, None, 3],
      ...               index=pd.date_range('20180101', periods=3, freq='h'))
      >>> sm
      2018-01-01 00:00:00    1.0
      2018-01-01 01:00:00    NaN
      2018-01-01 02:00:00    3.0
      Freq: H, dtype: float64

      >>> sm.resample('30min').fillna('backfill')
      2018-01-01 00:00:00    1.0
      2018-01-01 00:30:00    NaN
      2018-01-01 01:00:00    NaN
      2018-01-01 01:30:00    3.0
      2018-01-01 02:00:00    3.0
      Freq: 30T, dtype: float64

      >>> sm.resample('30min').fillna('pad')
      2018-01-01 00:00:00    1.0
      2018-01-01 00:30:00    1.0
      2018-01-01 01:00:00    NaN
      2018-01-01 01:30:00    NaN
      2018-01-01 02:00:00    3.0
      Freq: 30T, dtype: float64

      >>> sm.resample('30min').fillna('nearest')
      2018-01-01 00:00:00    1.0
      2018-01-01 00:30:00    NaN
      2018-01-01 01:00:00    NaN
      2018-01-01 01:30:00    3.0
      2018-01-01 02:00:00    3.0
      Freq: 30T, dtype: float64

  DataFrame resampling is done column-wise. All the same options are available.

      >>> 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').fillna("bfill")
                             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

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