Dataframe modules¶

The pandas_utils and polars_utils modules provide the same API to work with pandas.Series and polars.Series/polars.Expr respectively.

The utilities come in two flavors: functions and extensions.

Functions¶

The functions approach takes the series/expr as an argument and returns a new series/expr.

Available functions are:

datetime_to_isoweek and datetime_to_isoweekdate: converts a datetime series to an ISO week (date) series.
isoweek_to_datetime and isoweekdate_to_datetime: converts an ISO week date series to a datetime series.
is_isoweek_series and is_isoweekdate_series: checks if a series is an ISO week (date) series.

pandas

import pandas as pd
from datetime import date, timedelta
from iso_week_date.pandas_utils import datetime_to_isoweek, isoweek_to_datetime, is_isoweek_series

s_date = pd.Series(pd.date_range(date(2023, 1, 1), date(2023, 1, 10), freq="1d"))
datetime_to_isoweek(series=s_date, offset=pd.Timedelta(days=1)).to_list()
# ['2022-W52', '2022-W52', '2023-W01',..., '2023-W01', '2023-W02']

s_iso = pd.Series(["2022-W52", "2023-W01", "2023-W02"])
isoweek_to_datetime(series=s_iso, offset=pd.Timedelta(days=1))
'''
0   2022-12-27
1   2023-01-03
2   2023-01-10
dtype: datetime64[ns]
'''

is_isoweek_series(s_iso)  # True
is_isoweek_series(s_iso + "abc")  # False

polars

import polars as pl
from datetime import date, timedelta
from iso_week_date.polars_utils import datetime_to_isoweekdate, isoweekdate_to_datetime, is_isoweekdate_series

s_date = pl.date_range(date(2023, 1, 1), date(2023, 1, 10), interval="1d", eager=True)
datetime_to_isoweekdate(s_date, offset=timedelta(days=1)).to_list()
# ['2022-W52-6', '2022-W52-7', '2023-W01-1',..., '2023-W01-7', '2023-W02-1']

s_iso = pl.Series(["2022-W52-1", "2023-W01-2", "2023-W02-7"])
isoweekdate_to_datetime(series=s_iso,offset=timedelta(days=1))
'''
date
2022-12-27
2023-01-04
2023-01-16
'''

is_isoweekdate_series(s_iso)  # True
is_isoweekdate_series(s_iso + "abc")  # False

Extensions¶

On the other hand the extensions¹ approach extends the pandas.Series and polars.Series/polars.Expr classes with new methods.

The extensions are available through the iwd (isoweekdate) namespace, and the methods available are the same as the functions.

"Translating" the previous examples to extensions:

pandas

import pandas as pd
from iso_week_date.pandas_utils import SeriesIsoWeek  # noqa: F401 (1)

s_date = pd.Series(pd.date_range(date(2023, 1, 1), date(2023, 1, 10), freq="1d"))
s_date.iwd.datetime_to_isoweek(offset=pd.Timedelta(days=1)).to_list()
# ['2022-W52', '2022-W52', '2023-W01',..., '2023-W01', '2023-W02']

s_iso = pd.Series(["2022-W52", "2023-W01", "2023-W02"])
s_iso.iwd.isoweek_to_datetime(offset=pd.Timedelta(days=1))
'''
0   2022-12-27
1   2023-01-03
2   2023-01-10
dtype: datetime64[ns]
'''

s_iso.iwd.is_isoweek(s_iso)  # True
s_iso.iwd.is_isoweek(s_iso + "abc")  # False

The import of SeriesIsoWeek is needed to register the extensions.

noqa: F401 is added to avoid the linter(s) warning about the unused import.

polars

import polars as pl
from iso_week_date.polars_utils import SeriesIsoWeek  # noqa: F401 (1)

s_date = pl.date_range(date(2023, 1, 1), date(2023, 1, 10), interval="1d")
s_date.iwd.datetime_to_isoweekdate(offset=timedelta(days=1)).to_list()
# ['2022-W52-6', '2022-W52-7', '2023-W01-1',..., '2023-W01-7', '2023-W02-1']

s_iso = pl.Series(["2022-W52-1", "2023-W01-2", "2023-W02-7"])
s_iso.iwd.isoweekdate_to_datetime(offset=timedelta(days=1))
'''
date
2022-12-27
2023-01-04
2023-01-16
'''

s_iso.iwd.is_isoweekdate()  # True
(s_iso + "abc").iwd.is_isoweekdate_series()  # False

The import of SeriesIsoWeek is needed to register the extensions.

noqa: F401 is added to avoid the linter(s) warning about the unused import.

Note

Polars extension is valid for both Series and Expr classes.

This means that it is possible to use the extension in any polars context in which it would be possible to use an expression.

Extending pandas and polars ↩