polars.LazyFrame.with_context#

LazyFrame.with_context(other: Self | list[Self]) → Self[source]#

Add an external context to the computation graph.

This allows expressions to also access columns from DataFrames that are not part of this one.

Parameters:

other: Lazy DataFrame to join with.

Examples

>>> lf = pl.LazyFrame({"a": [1, 2, 3], "b": ["a", "c", None]})
>>> lf_other = pl.LazyFrame({"c": ["foo", "ham"]})
>>> lf.with_context(lf_other).select(
...     pl.col("b") + pl.col("c").first()
... ).collect()
shape: (3, 1)
┌──────┐
│ b    │
│ ---  │
│ str  │
╞══════╡
│ afoo │
│ cfoo │
│ null │
└──────┘

Fill nulls with the median from another DataFrame:

>>> train_lf = pl.LazyFrame(
...     {"feature_0": [-1.0, 0, 1], "feature_1": [-1.0, 0, 1]}
... )
>>> test_lf = pl.LazyFrame(
...     {"feature_0": [-1.0, None, 1], "feature_1": [-1.0, 0, 1]}
... )
>>> test_lf.with_context(
...     train_lf.select(pl.all().name.suffix("_train"))
... ).select(
...     pl.col("feature_0").fill_null(pl.col("feature_0_train").median())
... ).collect()
shape: (3, 1)
┌───────────┐
│ feature_0 │
│ ---       │
│ f64       │
╞═══════════╡
│ -1.0      │
│ 0.0       │
│ 1.0       │
└───────────┘