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# pandas.core.groupby.SeriesGroupBy.fillna

SeriesGroupBy.fillna(value=None, method=None, axis=<no_default>, inplace=False, limit=None, downcast=<no_default>)

  Fill NA/NaN values using the specified method within groups.

  Deprecated since version 2.2.0: This method is deprecated and will be removed in a future version. Use the `SeriesGroupBy.ffill()` or `SeriesGroupBy.bfill()` for forward or backward filling instead. If you want to fill with a single value, use `Series.fillna()` instead.

  Parameters:
    valuescalar, dict, Series, or DataFrame

      Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list. Users wanting to use the `value` argument and not `method` should prefer `Series.fillna()` as this will produce the same result and be more performant.

    method{{‘bfill’, ‘ffill’, None}}, default None

      Method to use for filling holes. `'ffill'` will propagate the last valid observation forward within a group. `'bfill'` will use next valid observation to fill the gap.

    axis{0 or ‘index’, 1 or ‘columns’}

      Unused, only for compatibility with `DataFrameGroupBy.fillna()`.

    inplacebool, default False

      Broken. Do not set to True.

    limitint, default None

      If method is specified, this is the maximum number of consecutive NaN values to forward/backward fill within a group. In other words, if there is a gap with more than this number of consecutive NaNs, it will only be partially filled. If method is not specified, this is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

    downcastdict, default is None

      A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

  Returns:
    Series

      Object with missing values filled within groups.

  See also

  `ffill`

    Forward fill values within a group.

  `bfill`

    Backward fill values within a group.

  Examples

  For SeriesGroupBy:

      >>> lst = ['cat', 'cat', 'cat', 'mouse', 'mouse']
      >>> ser = pd.Series([1, None, None, 2, None], index=lst)
      >>> ser
      cat    1.0
      cat    NaN
      cat    NaN
      mouse  2.0
      mouse  NaN
      dtype: float64
      >>> ser.groupby(level=0).fillna(0, limit=1)
      cat    1.0
      cat    0.0
      cat    NaN
      mouse  2.0
      mouse  0.0
      dtype: float64