# pandas.DataFrame.combine

DataFrame.combine(other, func, fill_value=None, overwrite=True)

  Perform column-wise combine with another DataFrame.

  Combines a DataFrame with other DataFrame using func to element-wise combine columns. The row and column indexes of the resulting DataFrame will be the union of the two.

  Parameters
    other DataFrame

      The DataFrame to merge column-wise.

    func function

      Function that takes two series as inputs and return a Series or a scalar. Used to merge the two dataframes column by columns.

    fill_value scalar value, default None

      The value to fill NaNs with prior to passing any column to the merge func.

    overwrite bool, default True

      If True, columns in self that do not exist in other will be overwritten with NaNs.

  Returns
    DataFrame

      Combination of the provided DataFrames.

  See also

  `DataFrame.combine_first`

    Combine two DataFrame objects and default to non-null values in frame calling the method.

  Examples

  Combine using a simple function that chooses the smaller column.

      >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
      >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
      >>> take_smaller = lambda s1, s2: s1 if s1.sum() < s2.sum() else s2
      >>> df1.combine(df2, take_smaller)
         A  B
      0  0  3
      1  0  3

  Example using a true element-wise combine function.

      >>> df1 = pd.DataFrame({'A': [5, 0], 'B': [2, 4]})
      >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
      >>> df1.combine(df2, np.minimum)
         A  B
      0  1  2
      1  0  3

  Using fill_value fills Nones prior to passing the column to the merge function.

      >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
      >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [3, 3]})
      >>> df1.combine(df2, take_smaller, fill_value=-5)
         A    B
      0  0 -5.0
      1  0  4.0

  However, if the same element in both dataframes is None, that None is preserved

      >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [None, 4]})
      >>> df2 = pd.DataFrame({'A': [1, 1], 'B': [None, 3]})
      >>> df1.combine(df2, take_smaller, fill_value=-5)
          A    B
      0  0 -5.0
      1  0  3.0

  Example that demonstrates the use of overwrite and behavior when the axis differ between the dataframes.

      >>> df1 = pd.DataFrame({'A': [0, 0], 'B': [4, 4]})
      >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [-10, 1], }, index=[1, 2])
      >>> df1.combine(df2, take_smaller)
           A    B     C
      0  NaN  NaN   NaN
      1  NaN  3.0 -10.0
      2  NaN  3.0   1.0

      >>> df1.combine(df2, take_smaller, overwrite=False)
           A    B     C
      0  0.0  NaN   NaN
      1  0.0  3.0 -10.0
      2  NaN  3.0   1.0

  Demonstrating the preference of the passed in dataframe.

      >>> df2 = pd.DataFrame({'B': [3, 3], 'C': [1, 1], }, index=[1, 2])
      >>> df2.combine(df1, take_smaller)
         A    B   C
      0  0.0  NaN NaN
      1  0.0  3.0 NaN
      2  NaN  3.0 NaN

      >>> df2.combine(df1, take_smaller, overwrite=False)
           A    B   C
      0  0.0  NaN NaN
      1  0.0  3.0 1.0
      2  NaN  3.0 1.0