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 [R32]. 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 orginal 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.

pandas.Series.fillna

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

pandas.DataFrame.fillna

Fill NaN values in the DataFrame using the specified method, which can be ‘bfill’ and ‘ffill’.

References

[R32]

(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