Enable mypy strict equality

py-polars/pyproject.toml

@@ -40,7 +40,7 @@ disallow_untyped_calls = true
 warn_redundant_casts = true
 # warn_return_any = true
 no_implicit_reexport = true
-# strict_equality = true
+strict_equality = true
 # TODO: When all flags are enabled, replace by strict = true
 enable_error_code = [
   "redundant-expr",

py-polars/tests/db-benchmark/various.py

@@ -1,6 +1,7 @@
 # may contain many things that seemed to go wrong at scale
 
 import time
+from typing import cast
 
 import numpy as np
 
@@ -50,8 +51,8 @@
     computed = permuted.select(
         [pl.col("id").min().alias("min"), pl.col("id").max().alias("max")]
     )
-    assert computed[0, "min"] == minimum
-    assert computed[0, "max"] == maximum
+    assert cast(int, computed[0, "min"]) == minimum
+    assert cast(float, computed[0, "max"]) == maximum
 
 
 def test_windows_not_cached() -> None:

py-polars/tests/io/test_csv.py

@@ -7,6 +7,7 @@
 import zlib
 from datetime import date
 from pathlib import Path
+from typing import cast
 
 import pytest
 
@@ -402,8 +403,8 @@ def test_csv_globbing(examples_dir: str) -> None:
 
     df = pl.read_csv(path, columns=["category", "sugars_g"])
     assert df.shape == (135, 2)
-    assert df.row(-1) == ("seafood", 1)
-    assert df.row(0) == ("vegetables", 2)
+    assert df.row(-1) == ("seafood", 1)  # type: ignore[comparison-overlap]
+    assert df.row(0) == ("vegetables", 2)  # type: ignore[comparison-overlap]
 
     with pytest.raises(ValueError):
         _ = pl.read_csv(path, dtypes=[pl.Utf8, pl.Int64, pl.Int64, pl.Int64])
@@ -509,7 +510,10 @@ def fallback_chrono_parser() -> None:
     2021-10-10,2021-10-10
     """
     )
-    assert pl.read_csv(data.encode(), parse_dates=True).null_count().row(0) == (0, 0)
+    assert cast(
+        tuple[int, int],
+        pl.read_csv(data.encode(), parse_dates=True).null_count().row(0),
+    ) == (0, 0)
 
 
 def test_csv_string_escaping() -> None:

py-polars/tests/test_constructors.py

@@ -29,7 +29,8 @@ def test_init_dict() -> None:
 
     # List of empty list/tuple
     df = pl.DataFrame({"a": [[]], "b": [()]})
-    assert df.schema == {"a": pl.List(pl.Float64), "b": pl.List(pl.Float64)}
+    expected = {"a": pl.List(pl.Float64), "b": pl.List(pl.Float64)}
+    assert df.schema == expected  # type: ignore[comparison-overlap]
     assert df.rows() == [([], [])]
 
     # Mixed dtypes

py-polars/tests/test_datatypes.py

@@ -14,7 +14,7 @@ def test_dtype_init_equivalence() -> None:
         if inspect.isclass(dtype) and issubclass(dtype, datatypes.DataType)
     }
     for dtype in all_datatypes:
-        assert dtype == dtype()
+        assert dtype == dtype()  # type: ignore[comparison-overlap]
 
 
 def test_dtype_temporal_units() -> None:

py-polars/tests/test_datelike.py

@@ -1,9 +1,8 @@
 from __future__ import annotations
 
 import io
-import typing
 from datetime import date, datetime, time, timedelta
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, no_type_check
 
 import numpy as np
 import pandas as pd
@@ -154,7 +153,7 @@ def test_datetime_consistency() -> None:
             pl.lit(dt).cast(pl.Datetime("ns")).alias("dt_ns"),
         ]
     )
-    assert ddf.schema == {
+    assert ddf.schema == {  # type: ignore[comparison-overlap]
         "date": pl.Datetime("us"),
         "dt": pl.Datetime("us"),
         "dt_ms": pl.Datetime("ms"),
@@ -886,7 +885,7 @@ def test_agg_logical() -> None:
     assert s.min() == dates[0]
 
 
-@typing.no_type_check
+@no_type_check
 def test_from_time_arrow() -> None:
     times = pa.array([10, 20, 30], type=pa.time32("s"))
     times_table = pa.table([times], names=["times"])
@@ -1027,7 +1026,8 @@ def test_datetime_instance_selection() -> None:
         ],
     )
     for tu in DTYPE_TEMPORAL_UNITS:
-        assert df.select(pl.col([pl.Datetime(tu)])).dtypes == [pl.Datetime(tu)]
+        res = df.select(pl.col([pl.Datetime(tu)])).dtypes
+        assert res == [pl.Datetime(tu)]  # type: ignore[comparison-overlap]
         assert len(df.filter(pl.col(tu) == test_data[tu][0])) == 1
 
 

py-polars/tests/test_df.py

@@ -90,7 +90,7 @@ def test_selection() -> None:
 
     # select columns by mask
     assert df[:2, :1].shape == (2, 1)
-    assert df[:2, "a"].shape == (2, 1)
+    assert df[:2, "a"].shape == (2, 1)  # type: ignore[comparison-overlap]
 
     # column selection by string(s) in first dimension
     assert df["a"].to_list() == [1, 2, 3]
@@ -117,11 +117,11 @@ def test_selection() -> None:
     assert df[[1, 2], [1, 2]].frame_equal(
         pl.DataFrame({"b": [2.0, 3.0], "c": ["b", "c"]})
     )
-    assert df[1, 2] == "b"
-    assert df[1, 1] == 2.0
-    assert df[2, 0] == 3
+    assert typing.cast(str, df[1, 2]) == "b"
+    assert typing.cast(float, df[1, 1]) == 2.0
+    assert typing.cast(int, df[2, 0]) == 3
 
-    assert df[[0, 1], "b"].shape == (2, 1)
+    assert df[[0, 1], "b"].shape == (2, 1)  # type: ignore[comparison-overlap]
     assert df[[2], ["a", "b"]].shape == (1, 2)
     assert df.to_series(0).name == "a"
     assert (df["a"] == df["a"]).sum() == 3
@@ -132,10 +132,10 @@ def test_selection() -> None:
     assert df[1, [2]].frame_equal(expect)
     expect = pl.DataFrame({"b": [1.0, 3.0]})
     assert df[[0, 2], [1]].frame_equal(expect)
-    assert df[0, "c"] == "a"
-    assert df[1, "c"] == "b"
-    assert df[2, "c"] == "c"
-    assert df[0, "a"] == 1
+    assert typing.cast(str, df[0, "c"]) == "a"
+    assert typing.cast(str, df[1, "c"]) == "b"
+    assert typing.cast(str, df[2, "c"]) == "c"
+    assert typing.cast(int, df[0, "a"]) == 1
 
     # more slicing
     expect = pl.DataFrame({"a": [3, 2, 1], "b": [3.0, 2.0, 1.0], "c": ["c", "b", "a"]})
@@ -766,9 +766,9 @@ def test_df_fold() -> None:
 
 def test_row_tuple() -> None:
     df = pl.DataFrame({"a": ["foo", "bar", "2"], "b": [1, 2, 3], "c": [1.0, 2.0, 3.0]})
-    assert df.row(0) == ("foo", 1, 1.0)
-    assert df.row(1) == ("bar", 2, 2.0)
-    assert df.row(-1) == ("2", 3, 3.0)
+    assert df.row(0) == ("foo", 1, 1.0)  # type: ignore[comparison-overlap]
+    assert df.row(1) == ("bar", 2, 2.0)  # type: ignore[comparison-overlap]
+    assert df.row(-1) == ("2", 3, 3.0)  # type: ignore[comparison-overlap]
 
 
 def test_df_apply() -> None:
@@ -1058,7 +1058,7 @@ def dot_product() -> None:
     df = pl.DataFrame({"a": [1, 2, 3, 4], "b": [2, 2, 2, 2]})
 
     assert df["a"].dot(df["b"]) == 20
-    assert df.select([pl.col("a").dot("b")])[0, "a"] == 20
+    assert typing.cast(int, df.select([pl.col("a").dot("b")])[0, "a"]) == 20
 
 
 def test_hash_rows() -> None:

py-polars/tests/test_exprs.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from typing import cast
+
 import polars as pl
 from polars.testing import assert_series_equal, verify_series_and_expr_api
 
@@ -82,7 +84,7 @@ def test_count_expr() -> None:
 
     out = df.select(pl.count())
     assert out.shape == (1, 1)
-    assert out[0, 0] == 5
+    assert cast(int, out[0, 0]) == 5
 
     out = df.groupby("b", maintain_order=True).agg(pl.count())
     assert out["b"].to_list() == ["a", "b"]
@@ -274,9 +276,11 @@ def test_regex_in_filter() -> None:
         }
     )
 
-    assert df.filter(
+    res = df.filter(
         pl.fold(acc=False, f=lambda acc, s: acc | s, exprs=(pl.col("^nrs|flt*$") < 3))
-    ).row(0) == (1, "foo", 1.0)
+    ).row(0)
+    expected = (1, "foo", 1.0)
+    assert res == expected  # type: ignore[comparison-overlap]
 
 
 def test_arr_contains() -> None:

py-polars/tests/test_interop.py

@@ -388,7 +388,7 @@ def test_from_pandas_ns_resolution() -> None:
         [pd.Timestamp(year=2021, month=1, day=1, hour=1, second=1, nanosecond=1)],
         columns=["date"],
     )
-    assert pl.from_pandas(df)[0, 0] == datetime(2021, 1, 1, 1, 0, 1)
+    assert cast(datetime, pl.from_pandas(df)[0, 0]) == datetime(2021, 1, 1, 1, 0, 1)
 
 
 @no_type_check

py-polars/tests/test_lazy.py

@@ -472,7 +472,7 @@ def test_is_finite_is_infinite() -> None:
 
 def test_len() -> None:
     df = pl.DataFrame({"nrs": [1, 2, 3]})
-    assert df.select(col("nrs").len())[0, 0] == 3
+    assert cast(int, df.select(col("nrs").len())[0, 0]) == 3
 
 
 def test_cum_agg() -> None:
@@ -505,7 +505,7 @@ def test_round() -> None:
 
 def test_dot() -> None:
     df = pl.DataFrame({"a": [1.8, 1.2, 3.0], "b": [3.2, 1, 2]})
-    assert df.select(pl.col("a").dot(pl.col("b")))[0, 0] == 12.96
+    assert cast(float, df.select(pl.col("a").dot(pl.col("b")))[0, 0]) == 12.96
 
 
 def test_sort() -> None:
@@ -696,8 +696,8 @@ def test_rolling(fruits_cars: pl.DataFrame) -> None:
         ]
     )
 
-    assert out_single_val_variance[0, "std"] == 0.0
-    assert out_single_val_variance[0, "var"] == 0.0
+    assert cast(float, out_single_val_variance[0, "std"]) == 0.0
+    assert cast(float, out_single_val_variance[0, "var"]) == 0.0
 
 
 def test_rolling_apply() -> None:
@@ -993,16 +993,16 @@ def test_join_suffix() -> None:
 def test_str_concat() -> None:
     df = pl.DataFrame({"foo": [1, None, 2]})
     df = df.select(pl.col("foo").str.concat("-"))
-    assert df[0, 0] == "1-null-2"
+    assert cast(str, df[0, 0]) == "1-null-2"
 
 
 @pytest.mark.parametrize("no_optimization", [False, True])
 def test_collect_all(df: pl.DataFrame, no_optimization: bool) -> None:
     lf1 = df.lazy().select(pl.col("int").sum())
     lf2 = df.lazy().select((pl.col("floats") * 2).sum())
     out = pl.collect_all([lf1, lf2], no_optimization=no_optimization)
-    assert out[0][0, 0] == 6
-    assert out[1][0, 0] == 12.0
+    assert cast(int, out[0][0, 0]) == 6
+    assert cast(float, out[1][0, 0]) == 12.0
 
 
 def test_spearman_corr() -> None:
@@ -1058,8 +1058,10 @@ def test_pearson_corr() -> None:
 
 
 def test_cov(fruits_cars: pl.DataFrame) -> None:
-    assert fruits_cars.select(pl.cov("A", "B"))[0, 0] == -2.5
-    assert fruits_cars.select(pl.cov(pl.col("A"), pl.col("B")))[0, 0] == -2.5
+    assert cast(float, fruits_cars.select(pl.cov("A", "B"))[0, 0]) == -2.5
+    assert (
+        cast(float, fruits_cars.select(pl.cov(pl.col("A"), pl.col("B")))[0, 0]) == -2.5
+    )
 
 
 def test_std(fruits_cars: pl.DataFrame) -> None:

py-polars/tests/test_lists.py

@@ -78,7 +78,7 @@ def test_dtype() -> None:
             ("dtm", pl.List(pl.Datetime)),
         ],
     )
-    assert df.schema == {
+    assert df.schema == {  # type: ignore[comparison-overlap]
         "i": pl.List(pl.Int8),
         "tm": pl.List(pl.Time),
         "dt": pl.List(pl.Date),

py-polars/tests/test_queries.py

@@ -316,7 +316,7 @@ def test_when_then_edge_cases_3994() -> None:
         .groupby(["id"])
         .agg(pl.col("type"))
         .with_column(
-            pl.when(pl.col("type").arr.lengths == 0)
+            pl.when(pl.col("type").arr.lengths() == 0)
             .then(pl.lit(None))
             .otherwise(pl.col("type"))
             .keep_name()