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# pandas.DataFrame.fillna

DataFrame.fillna(value, *, axis=None, inplace=False, limit=None)

  Fill NA/NaN values with value.

  This method replaces missing values with a specified value.

  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.

    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.

    inplace bool, 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).

    limit int, default None

      This is the maximum number of entries along the entire axis where NaNs will be filled. Must be greater than 0 if not None.

  Returns:
    Series/DataFrame

      Object with missing values filled.

  See also

  `ffill`

    Fill values by propagating the last valid observation to next valid.

  `bfill`

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

  `interpolate`

    Fill NaN values using interpolation.

  `reindex`

    Conform object to new index.

  `asfreq`

    Convert TimeSeries to specified frequency.

  Notes

  For non-object dtype, `value=None` will use the NA value of the dtype. See more details in the Filling missing data section.

  Examples

      >>> df = pd.DataFrame(
      ...     [
      ...         [np.nan, 2, np.nan, 0],
      ...         [3, 4, np.nan, 1],
      ...         [np.nan, np.nan, np.nan, np.nan],
      ...         [np.nan, 3, np.nan, 4],
      ...     ],
      ...     columns=list("ABCD"),
      ... )
      >>> df
           A    B   C    D
      0  NaN  2.0 NaN  0.0
      1  3.0  4.0 NaN  1.0
      2  NaN  NaN NaN  NaN
      3  NaN  3.0 NaN  4.0

  Replace all NaN elements with 0s.

      >>> df.fillna(0)
           A    B    C    D
      0  0.0  2.0  0.0  0.0
      1  3.0  4.0  0.0  1.0
      2  0.0  0.0  0.0  0.0
      3  0.0  3.0  0.0  4.0

  Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

      >>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
      >>> df.fillna(value=values)
           A    B    C    D
      0  0.0  2.0  2.0  0.0
      1  3.0  4.0  2.0  1.0
      2  0.0  1.0  2.0  3.0
      3  0.0  3.0  2.0  4.0

  Only replace the first NaN element.

      >>> df.fillna(value=values, limit=1)
           A    B    C    D
      0  0.0  2.0  2.0  0.0
      1  3.0  4.0  NaN  1.0
      2  NaN  1.0  NaN  3.0
      3  NaN  3.0  NaN  4.0

  When filling using a DataFrame, replacement happens along the same column names and same indices

      >>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
      >>> df.fillna(df2)
           A    B    C    D
      0  0.0  2.0  0.0  0.0
      1  3.0  4.0  0.0  1.0
      2  0.0  0.0  0.0  NaN
      3  0.0  3.0  0.0  4.0

  Note that column D is not affected since it is not present in df2.