(data, nrows, starting_index=None, accounting_column=None)
return pd.concat([data, new_rows]), added_index, copied_index
Add rows to data table according to a given nrows.
    New rows will have their IDs set to NaN.

    Parameters
    ----------
    data : pandas.DataFrame
    nrows : int
        Number of rows to add.
    starting_index : int, optional
        The starting index from which to calculate indexes for the new
        rows. If not given the max + 1 of the index of `data` will be used.
    accounting_column: string, optional
        Name of column with accounting totals/quanties to apply towards the control. If not provided
        then row counts will be used for accounting.

    Returns
    -------
    updated : pandas.DataFrame
        Table with rows added. New rows will have their index values
        set to NaN.
    added : pandas.Index
        New indexes of the rows that were added.
    copied : pandas.Index
        Indexes of rows that were copied. A row copied multiple times
        will have multiple entries.
Add rows to data table according to a given nrows.
    New rows will have their IDs set to NaN.
Add rows to data table according to a given nrows . New rows will have their IDs set to NaN .
def add_rows(data, nrows, starting_index=None, accounting_column=None):
    """
    Add rows to data table according to a given nrows.
    New rows will have their IDs set to NaN.

    Parameters
    ----------
    data : pandas.DataFrame
    nrows : int
        Number of rows to add.
    starting_index : int, optional
        The starting index from which to calculate indexes for the new
        rows. If not given the max + 1 of the index of `data` will be used.
    accounting_column: string, optional
        Name of column with accounting totals/quanties to apply towards the control. If not provided
        then row counts will be used for accounting.

    Returns
    -------
    updated : pandas.DataFrame
        Table with rows added. New rows will have their index values
        set to NaN.
    added : pandas.Index
        New indexes of the rows that were added.
    copied : pandas.Index
        Indexes of rows that were copied. A row copied multiple times
        will have multiple entries.

    """
    logger.debug('start: adding {} rows in transition model'.format(nrows))
    if nrows == 0:
        return data, _empty_index(), _empty_index()

    if not starting_index:
        starting_index = data.index.values.max() + 1

    new_rows = sample_rows(nrows, data, accounting_column=accounting_column)
    copied_index = new_rows.index
    added_index = pd.Index(np.arange(
        starting_index, starting_index + len(new_rows.index), dtype=np.int))
    new_rows.index = added_index

    logger.debug(
        'finish: added {} rows in transition model'.format(len(new_rows)))
    return pd.concat([data, new_rows]), added_index, copied_index
def add_rows ( data , nrows , starting_index = None , accounting_column = None ) : logger . debug ( 'start: adding {} rows in transition model' . format ( nrows ) ) if nrows == 0 : return data , _empty_index ( ) , _empty_index ( ) if not starting_index : starting_index = data . index . values . max ( ) + 1 new_rows = sample_rows ( nrows , data , accounting_column = accounting_column ) copied_index = new_rows . index added_index = pd . Index ( np . arange ( starting_index , starting_index + len ( new_rows . index ) , dtype = np . int ) ) new_rows . index = added_index logger . debug ( 'finish: added {} rows in transition model' . format ( len ( new_rows ) ) ) return pd . concat ( [ data , new_rows ] ) , added_index , copied_index