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# pandas.core.groupby.DataFrameGroupBy.fillna

DataFrameGroupBy.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 `DataFrameGroupBy.ffill()` or `DataFrameGroupBy.bfill()` for forward or backward filling instead. If you want to fill with a single value, use `DataFrame.fillna()` instead.

  Parameters:
    value scalar, 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 `DataFrame.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’}

      Axis along which to fill missing values. When the `DataFrameGroupBy` `axis` argument is `0`, using `axis=1` here will produce the same results as `DataFrame.fillna()`. When the `DataFrameGroupBy` `axis` argument is `1`, using `axis=0` or `axis=1` here will produce the same results.

    inplace bool, default False

      Broken. Do not set to True.

    limit int, 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.

    downcast dict, 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:
    DataFrame

      Object with missing values filled.

  See also

  `ffill`

    Forward fill values within a group.

  `bfill`

    Backward fill values within a group.

  Examples

      >>> df = pd.DataFrame(
      ...     {
      ...         "key": [0, 0, 1, 1, 1],
      ...         "A": [np.nan, 2, np.nan, 3, np.nan],
      ...         "B": [2, 3, np.nan, np.nan, np.nan],
      ...         "C": [np.nan, np.nan, 2, np.nan, np.nan],
      ...     }
      ... )
      >>> df
         key    A    B   C
      0    0  NaN  2.0 NaN
      1    0  2.0  3.0 NaN
      2    1  NaN  NaN 2.0
      3    1  3.0  NaN NaN
      4    1  NaN  NaN NaN

  Propagate non-null values forward or backward within each group along columns.

      >>> df.groupby("key").fillna(method="ffill")
           A    B   C
      0  NaN  2.0 NaN
      1  2.0  3.0 NaN
      2  NaN  NaN 2.0
      3  3.0  NaN 2.0
      4  3.0  NaN 2.0

      >>> df.groupby("key").fillna(method="bfill")
           A    B   C
      0  2.0  2.0 NaN
      1  2.0  3.0 NaN
      2  3.0  NaN 2.0
      3  3.0  NaN NaN
      4  NaN  NaN NaN

  Propagate non-null values forward or backward within each group along rows.

      >>> df.T.groupby(np.array([0, 0, 1, 1])).fillna(method="ffill").T
         key    A    B    C
      0  0.0  0.0  2.0  2.0
      1  0.0  2.0  3.0  3.0
      2  1.0  1.0  NaN  2.0
      3  1.0  3.0  NaN  NaN
      4  1.0  1.0  NaN  NaN

      >>> df.T.groupby(np.array([0, 0, 1, 1])).fillna(method="bfill").T
         key    A    B    C
      0  0.0  NaN  2.0  NaN
      1  0.0  2.0  3.0  NaN
      2  1.0  NaN  2.0  2.0
      3  1.0  3.0  NaN  NaN
      4  1.0  NaN  NaN  NaN

  Only replace the first NaN element within a group along rows.

      >>> df.groupby("key").fillna(method="ffill", limit=1)
           A    B    C
      0  NaN  2.0  NaN
      1  2.0  3.0  NaN
      2  NaN  NaN  2.0
      3  3.0  NaN  2.0
      4  3.0  NaN  NaN