def _modify_dataframe(self, df):
"""Create single index dataframe inserting grouping rows for higher levels."""
if self.total_columns == []:
columns = df.columns
else:
columns = self.total_columns
flat_row_list = []
n_ix_levels = len(df.index.levels)
# For each row compare index tuple to previous one and see if it changed on any level.
previous_tuple = [''] * n_ix_levels
for level_k, index_tuple in enumerate(df.index):
for level_i, sub_index in enumerate(index_tuple):
if index_tuple[:level_i + 1] != previous_tuple[:level_i + 1]:
if level_i == n_ix_levels - 1:
# If we are on lowest level, add entire row to flat_df
data_rows = df.iloc[[level_k], :]
else:
# If we are on higher level, add row filled with operator on lower level data
if self.operator is OP_NONE:
# For operator None fill row with empty string for each column
data_rows = pd.DataFrame('', columns=df.columns, index=[sub_index])
else:
df_subset = df.loc[index_tuple[:level_i + 1]]
data_rows = self.operator(df_subset[df_subset.applymap(np.isreal)]).to_frame().T
data_rows = data_rows.fillna(0.)
data_rows.loc[:, ~data_rows.columns.isin(columns)] = ''
n_rows = len(data_rows)
data_rows.index = [sub_index] * n_rows
data_rows.loc[:, ORG_ROW_NAMES] = pd.Series([index_tuple[:level_i + 1]], index=data_rows.index)
flat_row_list.append(data_rows)
# Need to address index_level with i instead of sub_index, because sub_index can repeat many times.
self.index_level += [level_i] * n_rows
previous_tuple = index_tuple
flat_df = pd.concat(flat_row_list)
flat_df.index.name = ''
return flat_df
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