pistachiots 0.1.0

PolarsTS



Time series handling on top of polars: a TSPolars object that keeps track of

the time index and the panel keys, with methods for sorting, lagging,

differencing and validation.



This is an independent project, not affiliated with the polars development team.





INSTALLATION



    pip install PolarsTS





THE TSPOLARS OBJECT



TSPolars wraps a polars DataFrame and remembers three things: which column is

the time index (ts_col), which columns identify each series in a panel

(id_cols), and what kind of time step the index represents (ts_op).



    import polars as pl

    import PolarsTS as cln



    ts = cln.TSPolars(df, ts_col='fecha', id_cols=['region'], ts_op='yearmonth')



Valid values for ts_op: yearweek, yearmonth, yearquarter, date, datetime,

time, int.



Any polars method works directly on the object and returns a new TSPolars, so

the index and the keys survive the operation.





LAGS



add_lags creates lagged columns named L1x, L2x, and so on, computed within each

key so values never leak across series.



    ts.add_lags(n_lags = 3)

    ts.add_lags(var = 'ipc', n_lags = 6)

    ts.add_lags(lags = {'ipc': 3, 'ventas': [1, 6, 12]})



rem_lags is the inverse and takes the same arguments.





OTHER METHODS



    valid(keys_policy, n_keys_allowed)  checks the object before using it

    sort_ts(keys_first)                 sorts by keys and index

    group_lags(response)                puts each variable next to its lags

    dif_vars(var)                       differences the given variables

    format_ts_col()                     formats the index as text per ts_op



Every method takes inplace: with False (the default) it returns a new object,

with True it modifies the existing one.





LICENSE



MIT. See the LICENSE file.