Business day utilities for Polars

Project description

Polars-business

Business day utilities for Polars.

✅ blazingly fast, written in Rust!
✅ seamless Polars integration!
✅ define your own custom holidays and weekends!

Installation

Then, you'll need to install polars-business:

pip install polars-business

Then, if you can run

from datetime import date
import polars_business as plb

print(plb.date_range(date(2023, 1, 1), date(2023, 1, 10), eager=True))

it means installation all worked correctly!

Usage

import polars_business as plb
use .bdt accessor on expressions created via plb.col

Supported functions are:

Expr.bdt.offset_by: just like polars.Expr.dt.offset_by, but also accepts:
- '1bd' in the string language (i.e. "1 business day")
- holidays argument, for passing custom holidays
- weekend argument, for passing custom a weekend (default is ('Sat', 'Sun'))
plb.date_range: just like polars.date_range, but also accepts:
- holidays argument, for passing custom holidays
- weekend argument, for passing custom a weekend (default is ('Sat', 'Sun'))
Final result will have holidays and weekend filtered out.

See Examples below!

Examples

Say we start with

from datetime import date

import polars as pl
import polars_business as plb


df = pl.DataFrame(
    {"date": [date(2023, 4, 3), date(2023, 9, 1), date(2024, 1, 4)]}
)

Let's shift Date forwards by 5 days, excluding Saturday and Sunday:

result = df.with_columns(
    date_shifted=plb.col("date").bdt.offset_by('5bd')
)
print(result)

shape: (3, 2)
┌────────────┬──────────────┐
│ date       ┆ date_shifted │
│ ---        ┆ ---          │
│ date       ┆ date         │
╞════════════╪══════════════╡
│ 2023-04-03 ┆ 2023-04-10   │
│ 2023-09-01 ┆ 2023-09-08   │
│ 2024-01-04 ┆ 2024-01-11   │
└────────────┴──────────────┘

Let's shift Date forwards by 5 days, excluding Saturday and Sunday and UK holidays for 2023 and 2024:

import holidays

uk_holidays = holidays.country_holidays("UK", years=[2023, 2024])

result = df.with_columns(
    date_shifted=plb.col("date").bdt.advance_n_days(
      by='5bd',
      holidays=uk_holidays,
    )
)
print(result)

shape: (3, 2)
┌────────────┬──────────────┐
│ date       ┆ date_shifted │
│ ---        ┆ ---          │
│ date       ┆ date         │
╞════════════╪══════════════╡
│ 2023-04-03 ┆ 2023-04-11   │
│ 2023-09-01 ┆ 2023-09-08   │
│ 2024-01-04 ┆ 2024-01-11   │
└────────────┴──────────────┘

Let's shift Date forwards by 5 days, excluding only Sunday:

result = df.with_columns(
    date_shifted=plb.col("date").bdt.offset_by(
      by='5bd',
      weekend=['Sun'],
    )
)
print(result)

shape: (3, 2)
┌────────────┬──────────────┐
│ date       ┆ date_shifted │
│ ---        ┆ ---          │
│ date       ┆ date         │
╞════════════╪══════════════╡
│ 2023-04-03 ┆ 2023-04-08   │
│ 2023-09-01 ┆ 2023-09-07   │
│ 2024-01-04 ┆ 2024-01-10   │
└────────────┴──────────────┘

Benchmarks

Single-threaded performance is:

about on par with NumPy (but more flexible)
at least an order of magnitude faster than pandas.

Check the https://www.kaggle.com/code/marcogorelli/polars-business for some comparisons.

Project details

Release history Release notifications | RSS feed

0.3.22

Dec 19, 2023

0.3.21

Dec 19, 2023

0.3.18

Dec 5, 2023

0.3.16

Dec 2, 2023

0.3.15

Nov 18, 2023

0.3.14

Nov 14, 2023

0.3.11

Nov 10, 2023

0.3.10

Nov 4, 2023

0.3.9

Nov 4, 2023

0.3.8

Nov 4, 2023

0.3.4

Oct 29, 2023

0.3.2

Oct 28, 2023

0.2.3

Oct 28, 2023

0.2.2

Oct 28, 2023

0.2.0

Oct 27, 2023

0.1.29

Oct 24, 2023

0.1.28

Oct 22, 2023

0.1.27

Oct 22, 2023

This version

0.1.26

Oct 22, 2023

0.1.25

Oct 22, 2023

0.1.23

Oct 21, 2023

0.1.21

Oct 21, 2023

0.1.20

Oct 20, 2023

0.1.18

Oct 18, 2023

0.1.17

Oct 18, 2023

0.1.15

Oct 18, 2023

0.1.12

Oct 15, 2023

0.1.11

Oct 14, 2023

0.1.10

Oct 14, 2023

0.1.9

Oct 14, 2023

0.1.8

Oct 13, 2023

0.1.6

Oct 11, 2023

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