Add fill_foward to headers

[iBuitron]

Hi!, i dont know how to add this code on GitHub, so hope some can add this

Code original:

def dataframe_to_rows(df, index=True, header=True):
    """
    Convert a Pandas dataframe into something suitable for passing into a worksheet.
    If index is True then the index will be included, starting one row below the header.
    If header is True then column headers will be included starting one column to the right.
    Formatting should be done by client code.
    """
    from pandas import Timestamp

    if header:
        if df.columns.nlevels > 1:
            rows = expand_index(df.columns, header)
        else:
            rows = [list(df.columns.values)]
        for row in rows:
            
            n = []
            for v in row:
                if isinstance(v, numpy.datetime64):
                    v = Timestamp(v)
                n.append(v)
            row = n
            if index:
                row = [None]*df.index.nlevels + row
            yield row

    if index:
        yield df.index.names

    expanded = ([v] for v in df.index)
    if df.index.nlevels > 1:
        expanded = expand_index(df.index)

    # Using the expanded index is preferable to df.itertuples(index=True) so that we have 'None' inserted where applicable
    for (df_index, row) in zip(expanded, df.itertuples(index=False)):
        row = list(row)
        if index:
            row = df_index + row
        yield row

Adding:

def fill_nones(data):
    """
    Fills in None values in a list with the preceding non-None value.

    Args:
        data (list): The list containing data, possibly with None values.

    Returns:
        list: The list with None values filled in.
    """
    previous_value = None
    for i in range(len(data)):
        if data[i] is None:
            data[i] = previous_value
        else:
            previous_value = data[i]
    return data

def dataframe_to_rows(df, index=True, header=True, fill_foward=False):
    """
    Convert a Pandas dataframe into something suitable for passing into a worksheet.
    If index is True then the index will be included, starting one row below the header.
    If header is True then column headers will be included starting one column to the right.
    Formatting should be done by client code.
    """
    from pandas import Timestamp

    if header:
        if df.columns.nlevels > 1:
            rows = expand_index(df.columns, header)
        else:
            rows = [list(df.columns.values)]
        for row in rows:
            
            if fill_foward:                 # HERE IS WHERE PUT THE CONDITION
                row = fill_nones(row)
                
            
            n = []
            for v in row:
                if isinstance(v, numpy.datetime64):
                    v = Timestamp(v)
                n.append(v)
            row = n
            if index:
                row = [None]*df.index.nlevels + row
            yield row

    if index:
        yield df.index.names

    expanded = ([v] for v in df.index)
    if df.index.nlevels > 1:
        expanded = expand_index(df.index)

    # Using the expanded index is preferable to df.itertuples(index=True) so that we have 'None' inserted where applicable
    for (df_index, row) in zip(expanded, df.itertuples(index=False)):
        row = list(row)
        if index:
            row = df_index + row
        yield row

Why? This helps when you use pandas with multiindex, writting using

    for r in dataframe_to_rows(
        df, index=False, header=True, fill_foward=True
    ):

        ws.append(r)

This keep the "merge" on the header/multiindex, instead a None values.