polars.LazyFrame.join_asof#
- LazyFrame.join_asof(other: LazyFrame, *, left_on: str | None | Expr = None, right_on: str | None | Expr = None, on: str | None | Expr = None, by_left: str | Sequence[str] | None = None, by_right: str | Sequence[str] | None = None, by: str | Sequence[str] | None = None, strategy: AsofJoinStrategy = 'backward', suffix: str = '_right', tolerance: str | int | float | None = None, allow_parallel: bool = True, force_parallel: bool = False) Self [source]#
Perform an asof join.
This is similar to a left-join except that we match on nearest key rather than equal keys.
Both DataFrames must be sorted by the join_asof key.
For each row in the left DataFrame:
A “backward” search selects the last row in the right DataFrame whose ‘on’ key is less than or equal to the left’s key.
A “forward” search selects the first row in the right DataFrame whose ‘on’ key is greater than or equal to the left’s key.
The default is “backward”.
- Parameters:
- other
Lazy DataFrame to join with.
- left_on
Join column of the left DataFrame.
- right_on
Join column of the right DataFrame.
- on
Join column of both DataFrames. If set, left_on and right_on should be None.
- by
Join on these columns before doing asof join.
- by_left
Join on these columns before doing asof join.
- by_right
Join on these columns before doing asof join.
- strategy{‘backward’, ‘forward’}
Join strategy.
- suffix
Suffix to append to columns with a duplicate name.
- tolerance
Numeric tolerance. By setting this the join will only be done if the near keys are within this distance. If an asof join is done on columns of dtype “Date”, “Datetime”, “Duration” or “Time” you use the following string language:
1ns (1 nanosecond)
1us (1 microsecond)
1ms (1 millisecond)
1s (1 second)
1m (1 minute)
1h (1 hour)
1d (1 day)
1w (1 week)
1mo (1 calendar month)
1y (1 calendar year)
1i (1 index count)
Or combine them: “3d12h4m25s” # 3 days, 12 hours, 4 minutes, and 25 seconds
- allow_parallel
Allow the physical plan to optionally evaluate the computation of both DataFrames up to the join in parallel.
- force_parallel
Force the physical plan to evaluate the computation of both DataFrames up to the join in parallel.
Examples
>>> from datetime import datetime >>> gdp = pl.LazyFrame( ... { ... "date": [ ... datetime(2016, 1, 1), ... datetime(2017, 1, 1), ... datetime(2018, 1, 1), ... datetime(2019, 1, 1), ... ], # note record date: Jan 1st (sorted!) ... "gdp": [4164, 4411, 4566, 4696], ... } ... ) >>> population = pl.LazyFrame( ... { ... "date": [ ... datetime(2016, 5, 12), ... datetime(2017, 5, 12), ... datetime(2018, 5, 12), ... datetime(2019, 5, 12), ... ], # note record date: May 12th (sorted!) ... "population": [82.19, 82.66, 83.12, 83.52], ... } ... ) >>> population.join_asof( ... gdp, left_on="date", right_on="date", strategy="backward" ... ).collect() shape: (4, 3) ┌─────────────────────┬────────────┬──────┐ │ date ┆ population ┆ gdp │ │ --- ┆ --- ┆ --- │ │ datetime[μs] ┆ f64 ┆ i64 │ ╞═════════════════════╪════════════╪══════╡ │ 2016-05-12 00:00:00 ┆ 82.19 ┆ 4164 │ │ 2017-05-12 00:00:00 ┆ 82.66 ┆ 4411 │ │ 2018-05-12 00:00:00 ┆ 83.12 ┆ 4566 │ │ 2019-05-12 00:00:00 ┆ 83.52 ┆ 4696 │ └─────────────────────┴────────────┴──────┘