osci/notify/get_contributors_ranking_mbm_report.py (27 lines of code) (raw):
"""Copyright since 2019, EPAM Systems
This file is part of OSCI.
OSCI is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
OSCI is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with OSCI. If not, see <http://www.gnu.org/licenses/>."""
from functools import reduce
from typing import Iterable, Tuple
from datetime import datetime
import pandas as pd
from osci.datalake.schemas.public import ContributorsRankingMBMReportSchema
def get_contributors_ranking_mbm_change_report(reports: Iterable[Tuple[datetime, pd.DataFrame]],
contributor_field: str,
commits_amount_field: str) -> pd.DataFrame:
"""Creates a combined report, that shows the difference between several contributors rankings
:param reports: collection of datatime and pandas DataFrame objects
:param contributor_field: contributor column name
:param commits_amount_field: amount of commits column name
"""
report = pd.DataFrame(columns=ContributorsRankingMBMReportSchema.required)
df_list = []
for item in reports:
tmp_df = pd \
.pivot_table(item[1],
values=[commits_amount_field, ],
index=[item[1].index.values, contributor_field]) \
.rename(columns={commits_amount_field: datetime.strftime(item[0], '%b')}) \
.reset_index(contributor_field)
df_list.append(tmp_df)
df = reduce(lambda x, y: pd.merge(x, y, how='outer', on=contributor_field), df_list) \
.sort_values(by=contributor_field).fillna(0)
df.iloc[:, 1:] = df.iloc[:, 1:].astype(int)
df[ContributorsRankingMBMReportSchema.total] = df.sum(axis=1)
df = df.sort_values(ContributorsRankingMBMReportSchema.total, ascending=False)
for column in report.columns:
if column in df.columns:
report[column] = df[column]
return report