def combine(self, 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
        """
        other_idxlen = len(other.index)  # save for compare

        this, other = self.align(other, copy=False)
        new_index = this.index

        if other.empty and len(new_index) == len(self.index):
            return self.copy()

        if self.empty and len(other) == other_idxlen:
            return other.copy()

        # sorts if possible
        new_columns = this.columns.union(other.columns)
        do_fill = fill_value is not None
        result = {}
        for col in new_columns:
            series = this[col]
            otherSeries = other[col]

            this_dtype = series.dtype
            other_dtype = otherSeries.dtype

            this_mask = isna(series)
            other_mask = isna(otherSeries)

            # don't overwrite columns unecessarily
            # DO propagate if this column is not in the intersection
            if not overwrite and other_mask.all():
                result[col] = this[col].copy()
                continue

            if do_fill:
                series = series.copy()
                otherSeries = otherSeries.copy()
                series[this_mask] = fill_value
                otherSeries[other_mask] = fill_value

            if col not in self.columns:
                # If self DataFrame does not have col in other DataFrame,
                # try to promote series, which is all NaN, as other_dtype.
                new_dtype = other_dtype
                try:
                    series = series.astype(new_dtype, copy=False)
                except ValueError:
                    # e.g. new_dtype is integer types
                    pass
            else:
                # if we have different dtypes, possibly promote
                new_dtype = find_common_type([this_dtype, other_dtype])
                if not is_dtype_equal(this_dtype, new_dtype):
                    series = series.astype(new_dtype)
                if not is_dtype_equal(other_dtype, new_dtype):
                    otherSeries = otherSeries.astype(new_dtype)

            arr = func(series, otherSeries)
            arr = maybe_downcast_to_dtype(arr, this_dtype)

            result[col] = arr

        # convert_objects just in case
        return self._constructor(result, index=new_index,
                                 columns=new_columns)
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