Chain function produces incorrect indices if period missing

[pricemg]

The chain does not handle missing periods correctly but still produces a result.

import pandas as pd
from pandas import Timestamp
import precon

df_all_periods = pd.DataFrame.from_records([
        (Timestamp('2018-01-01'), 100.000000),
        (Timestamp('2018-02-01'), 100.527400),
        (Timestamp('2018-03-01'), 100.894000),
        (Timestamp('2018-04-01'), 100.689100),
        (Timestamp('2018-05-01'), 102.670400),
        (Timestamp('2018-06-01'), 100.811000),
        (Timestamp('2018-07-01'), 102.632500),
        (Timestamp('2018-08-01'), 103.133200),
        (Timestamp('2018-09-01'), 103.111400),
        (Timestamp('2018-10-01'), 103.417700),
        (Timestamp('2018-11-01'), 103.155800),
        (Timestamp('2018-12-01'), 103.616800),
        (Timestamp('2019-01-01'), 104.246480),
        (Timestamp('2019-02-01'), 101.093900),
        (Timestamp('2019-03-01'), 101.726900),
        (Timestamp('2019-04-01'), 100.478600),  # April 2019 value present
        (Timestamp('2019-05-01'), 100.647800),
        (Timestamp('2019-06-01'), 100.439100),
        (Timestamp('2019-07-01'), 102.181900),
        (Timestamp('2019-08-01'), 100.608800),
        (Timestamp('2019-09-01'), 102.067000),
        (Timestamp('2019-10-01'), 102.418300),
        (Timestamp('2019-11-01'), 102.769600),
        (Timestamp('2019-12-01'), 103.120900),
        (Timestamp('2020-01-01'), 103.519414),
        (Timestamp('2020-02-01'), 100.710500),
    ],
    columns=('period', 'index_value'),
).set_index('period')

df_period_missing = pd.DataFrame.from_records([
        (Timestamp('2018-01-01'), 100.000000),
        (Timestamp('2018-02-01'), 100.527400),
        (Timestamp('2018-03-01'), 100.894000),
        (Timestamp('2018-04-01'), 100.689100),
        (Timestamp('2018-05-01'), 102.670400),
        (Timestamp('2018-06-01'), 100.811000),
        (Timestamp('2018-07-01'), 102.632500),
        (Timestamp('2018-08-01'), 103.133200),
        (Timestamp('2018-09-01'), 103.111400),
        (Timestamp('2018-10-01'), 103.417700),
        (Timestamp('2018-11-01'), 103.155800),
        (Timestamp('2018-12-01'), 103.616800),
        (Timestamp('2019-01-01'), 104.246480),
        (Timestamp('2019-02-01'), 101.093900),
        (Timestamp('2019-03-01'), 101.726900),
        (Timestamp('2019-04-01'), None),  # April 2019 value missing
        (Timestamp('2019-05-01'), 100.647800),
        (Timestamp('2019-06-01'), 100.439100),
        (Timestamp('2019-07-01'), 102.181900),
        (Timestamp('2019-08-01'), 100.608800),
        (Timestamp('2019-09-01'), 102.067000),
        (Timestamp('2019-10-01'), 102.418300),
        (Timestamp('2019-11-01'), 102.769600),
        (Timestamp('2019-12-01'), 103.120900),
        (Timestamp('2020-01-01'), 103.519414),
        (Timestamp('2020-02-01'), 100.710500),
    ],
    columns=('period', 'index_value'),
).set_index('period')

expected = pd.DataFrame.from_records([
        (Timestamp('2018-01-01'), 100.000000),
        (Timestamp('2018-02-01'), 100.527400),
        (Timestamp('2018-03-01'), 100.894000),
        (Timestamp('2018-04-01'), 100.689100),
        (Timestamp('2018-05-01'), 102.670400),
        (Timestamp('2018-06-01'), 100.811000),
        (Timestamp('2018-07-01'), 102.632500),
        (Timestamp('2018-08-01'), 103.133200),
        (Timestamp('2018-09-01'), 103.111400),
        (Timestamp('2018-10-01'), 103.417700),
        (Timestamp('2018-11-01'), 103.155800),
        (Timestamp('2018-12-01'), 103.616800),
        (Timestamp('2019-01-01'), 104.246480),
        (Timestamp('2019-02-01'), 105.386833),
        (Timestamp('2019-03-01'), 106.046713),
        (Timestamp('2019-04-01'), 104.745404),
        (Timestamp('2019-05-01'), 104.921789),
        (Timestamp('2019-06-01'), 104.704227),
        (Timestamp('2019-07-01'), 106.521034),
        (Timestamp('2019-08-01'), 104.881133),
        (Timestamp('2019-09-01'), 106.401255),
        (Timestamp('2019-10-01'), 106.767473),
        (Timestamp('2019-11-01'), 107.133691),
        (Timestamp('2019-12-01'), 107.499909),
        (Timestamp('2020-01-01'), 107.915346),
        (Timestamp('2020-02-01'), 108.682084),
    ],
    columns=('period', 'index_value'),
).set_index('period')

df_all_periods['chained'] = precon.chain(df_all_periods)

df_period_missing['chained'] = precon.chain(df_period_missing)

pd.concat([df_all_periods, df_period_missing, expected], keys=['all_periods', 'period_missing', 'expected'], axis=1)

In the above example expected is calculated for if all periods are present but using the equation of unlinked index * linked base / 100 so the chained indices after the missing period are not affected. precon.chain doesn't have an issue as it uses a backfill after shifting the indices by one period to fill in the first month.