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feat: add Series.__contains__ #1480

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merged 10 commits into from
Dec 3, 2024
Merged

feat: add Series.__contains__ #1480

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e36d4ed
feat: add Series.__contains__
FBruzzesi Dec 1, 2024
e6d9419
use pa.scalar with explicit type
FBruzzesi Dec 1, 2024
9a90632
use pa.scalar with explicit type
FBruzzesi Dec 1, 2024
3aa5b75
use pa.scalar with explicit type
FBruzzesi Dec 1, 2024
c653e48
Merge branch 'main' into feat/series-contains
FBruzzesi Dec 1, 2024
3f66045
I got fooled
FBruzzesi Dec 1, 2024
c9f0b36
Merge branch 'main' into feat/series-contains
FBruzzesi Dec 3, 2024
b2b005a
use pandas-like equals, raise custom error, deal with arrow scalar
FBruzzesi Dec 3, 2024
38c5833
arrow None
FBruzzesi Dec 3, 2024
634c262
add ArrowInvalid
FBruzzesi Dec 3, 2024
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10 changes: 10 additions & 0 deletions narwhals/_arrow/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1017,6 +1017,16 @@ def __iter__(self: Self) -> Iterator[Any]:
for x in self._native_series.__iter__()
)

def __contains__(self: Self, other: Any) -> bool:
import pyarrow as pa # ignore-banned-imports
import pyarrow.compute as pc # ignore-banned-imports

native_series = self._native_series
return maybe_extract_py_scalar( # type: ignore[no-any-return]
pc.is_in(pa.scalar(other, type=native_series.type), native_series),
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if other is of a type not convertible to native_series.type, will this raise? I'm wondering if we should return False in such a case?

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looks like polars would also raise here:

In [2]: 'foo' in pl.Series([1,2,3])
---------------------------------------------------------------------------
InvalidOperationError                     Traceback (most recent call last)
Cell In[2], line 1
----> 1 'foo' in pl.Series([1,2,3])

File ~/scratch/.venv/lib/python3.12/site-packages/polars/series/series.py:1233, in Series.__contains__(self, item)
   1231 if item is None:
   1232     return self.has_nulls()
-> 1233 return self.implode().list.contains(item).item()

File ~/scratch/.venv/lib/python3.12/site-packages/polars/series/utils.py:106, in call_expr.<locals>.wrapper(self, *args, **kwargs)
    104     expr = getattr(expr, namespace)
    105 f = getattr(expr, func.__name__)
--> 106 return s.to_frame().select_seq(f(*args, **kwargs)).to_series()

File ~/scratch/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py:9138, in DataFrame.select_seq(self, *exprs, **named_exprs)
   9115 def select_seq(
   9116     self, *exprs: IntoExpr | Iterable[IntoExpr], **named_exprs: IntoExpr
   9117 ) -> DataFrame:
   9118     """
   9119     Select columns from this DataFrame.
   9120
   (...)
   9136     select
   9137     """
-> 9138     return self.lazy().select_seq(*exprs, **named_exprs).collect(_eager=True)

File ~/scratch/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py:2029, in LazyFrame.collect(self, type_coercion, predicate_pushdown, projection_pushdown, simplify_expression, slice_pushdown, comm_subplan_elim, comm_subexpr_elim, cluster_with_columns, collapse_joins, no_optimization, streaming, engine, background, _eager, **_kwargs)
   2027 # Only for testing purposes
   2028 callback = _kwargs.get("post_opt_callback", callback)
-> 2029 return wrap_df(ldf.collect(callback))

InvalidOperationError: is_in operation not supported for dtypes `str` and `list[i64]`

probably ok to just follow them here then

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still, probably good to have a test to check it raises? we can leave the expression unification for a separate topic

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@FBruzzesi FBruzzesi Dec 3, 2024 •
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Will add a test and make sure that same exception is raised πŸ‘Œ

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@FBruzzesi FBruzzesi Dec 3, 2024 •
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Ok this is definitly wrong behaviour for arrow as the casting would convert from float to int, leading to the following:

import pyarrow as pa
import narwhals as nw

data = [100, 200, None]
s = nw.from_native(pa.table({"a": data}), eager_only=True)["a"]

100.3 in s
True

On the Exception side, I would not be sure how to catch the error for pandas as:

(pd.Series([1,2,3]) == "foo").any()
np.False_

In a way, I find polars behaviour more surprising

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@MarcoGorelli MarcoGorelli Dec 3, 2024 •
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tbh we could just dispatch to (s._compliant_series == other).any() for all backends πŸ˜„ that would still be better than the status quo

return_py_scalar=True,
)

@property
def shape(self: Self) -> tuple[int]:
return (len(self._native_series),)
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5 changes: 5 additions & 0 deletions narwhals/_pandas_like/series.py
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Expand Up @@ -851,6 +851,11 @@ def rolling_mean(
def __iter__(self: Self) -> Iterator[Any]:
yield from self._native_series.__iter__()

def __contains__(self: Self, other: Any) -> bool:
if other is None:
return self._native_series.isna().any() # type: ignore[no-any-return]
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return self._native_series.isin({other}).any() # type: ignore[no-any-return]
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why this over return (self._native_series == other).any()?

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No strong feelings πŸ˜‚ I guess I went for series.isin(...) as I initially fell for the other in series

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@MarcoGorelli MarcoGorelli Dec 3, 2024 •
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"fell for" πŸ˜„ love that

from a quick test, it looks a lot faster

In [13]: s = pd.Series(rng.integers(0, 10, size=1_000_000))

In [14]: %timeit s.isin({0}).any()
6.58 ms Β± 687 ΞΌs per loop (mean Β± std. dev. of 7 runs, 100 loops each)

In [15]: %timeit (s == 0).any()
326 ΞΌs Β± 45.8 ΞΌs per loop (mean Β± std. dev. of 7 runs, 1,000 loops each)


def is_finite(self: Self) -> Self:
s = self._native_series
return self._from_native_series((s > float("-inf")) & (s < float("inf")))
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3 changes: 3 additions & 0 deletions narwhals/series.py
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Expand Up @@ -3324,6 +3324,9 @@ def rolling_mean(
def __iter__(self: Self) -> Iterator[Any]:
yield from self._compliant_series.__iter__()

def __contains__(self: Self, other: Any) -> bool:
return self._compliant_series.__contains__(other) # type: ignore[no-any-return]

@property
def str(self: Self) -> SeriesStringNamespace[Self]:
return SeriesStringNamespace(self)
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23 changes: 23 additions & 0 deletions tests/series_only/__contains___test.py
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@@ -0,0 +1,23 @@
from __future__ import annotations

from typing import TYPE_CHECKING

import pytest

import narwhals.stable.v1 as nw

if TYPE_CHECKING:
from tests.utils import ConstructorEager

data = [100, 200, None]


@pytest.mark.parametrize(("other", "expected"), [(100, True), (None, True), (3, False)])
def test_contains(
constructor_eager: ConstructorEager,
other: int | None,
expected: bool, # noqa: FBT001
) -> None:
s = nw.from_native(constructor_eager({"a": data}), eager_only=True)["a"]

assert (other in s) == expected
1 change: 1 addition & 0 deletions utils/check_api_reference.py
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Expand Up @@ -32,6 +32,7 @@
"value_counts",
"zip_with",
"__iter__",
"__contains__",
}
BASE_DTYPES = {
"NumericType",
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