pandas.Series.bfill#

Series.bfill(*, axis=None, inplace=False, limit=None, limit_area=None)[source]#

Fill NA/NaN values by using the next valid observation to fill the gap.

This method fills missing values in a backward direction along the specified axis, propagating non-null values from later positions to earlier positions containing NaN.

Parameters:
axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame

Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

inplacebool, default False

If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).

limitint, default None

Maximum number of consecutive NaN values to fill. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. Must be greater than 0 if not None.

limit_area{None, ‘inside’, ‘outside’}, default None

Restrict which NaNs are filled based on their position relative to the valid values.

  • None: No fill restriction.

  • ‘inside’: Only fill NaNs surrounded by valid values (interpolate).

  • ‘outside’: Only fill NaNs outside valid values (extrapolate).

Added in version 2.2.0.

Returns:
Series/DataFrame

Object with missing values filled.

See also

DataFrame.ffill

Fill NA/NaN values by propagating the last valid observation to next valid.

Examples

For Series:

>>> s = pd.Series([1, None, None, 2])
>>> s.bfill()
0    1.0
1    2.0
2    2.0
3    2.0
dtype: float64
>>> s.bfill(limit=1)
0    1.0
1    NaN
2    2.0
3    2.0
dtype: float64

With DataFrame:

>>> df = pd.DataFrame({"A": [1, None, None, 4], "B": [None, 5, None, 7]})
>>> df
      A     B
0   1.0   NaN
1   NaN   5.0
2   NaN   NaN
3   4.0   7.0
>>> df.bfill()
      A     B
0   1.0   5.0
1   4.0   5.0
2   4.0   7.0
3   4.0   7.0
>>> df.bfill(limit=1)
      A     B
0   1.0   5.0
1   NaN   5.0
2   4.0   7.0
3   4.0   7.0
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