def transform(self, table, pivot_column, metric_column, x_axis_column):
"""Returns pivot table based on the pivot column"""
# NOTE:: Split transformation is for single metric. Future implementation for multiple metric
# Get index of the pivot column
pivot_column_index = table.columns.index(pivot_column)
# Find the index of the metric column
metric_column_index = table.columns.index(metric_column)
new_split_columns = OrderedSet([])
# Get values of new columns
for data in table.data:
new_split_columns.add(data[pivot_column_index])
new_split_columns = list(new_split_columns)
# Set the metric for the new columns
grouping_column_index = x_axis_column
new_split_data = OrderedDict()
new_data = []
for index, data in enumerate(table.data):
temp_metric = data[metric_column_index]
temp_pivot_value = data[pivot_column_index]
temp_grouping_column = data[grouping_column_index]
if not new_split_data.get(temp_grouping_column):
new_split_data[temp_grouping_column] = [
'-'
] * len(new_split_columns)
new_split_data[temp_grouping_column][new_split_columns.index(
temp_pivot_value)] = temp_metric
for key in new_split_data:
new_data.append([key] + new_split_data[key])
table.columns = [table.columns[x_axis_column]
] + [column for column in new_split_columns]
table.data = new_data
return table
