Business day utilities for Polars

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Project description

Polars-business

polars-business

PyPI version

Business day utilities for Polars.

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

Installation

First, you need to install Polars.

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

  1. import polars_business as plb
  2. 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:
    • '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.datetime_range: same as above, but the output will be Datetime dtype.

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
  • at least an order of magnitude faster than pandas.

but note that Polars will take care of parallelisation for you.

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

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Release history Release notifications | RSS feed

0.3.22

0.3.21

0.3.18

0.3.16

0.3.15

0.3.14

0.3.11

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0.3.9

0.3.8

0.3.4

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0.1.29

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0.1.12

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