TYP/CI: Demonstrate possible integration of VirtusLab's type tests

pandas/tests/typing/valid/test_interval.py

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+# flake8: noqa: F841
+# pyright: reportGeneralTypeIssues = true
+
+import pandas as pd
+
+
+def test_interval_init() -> None:
+    i1: pd.Interval = pd.Interval(1, 2, closed="both")
+    i2: pd.Interval = pd.Interval(1, right=2, closed="right")
+    i3: pd.Interval = pd.Interval(left=1, right=2, closed="left")
+
+
+def test_interval_arithmetic() -> None:
+    i1: pd.Interval = pd.Interval(1, 2, closed="both")
+    i2: pd.Interval = pd.Interval(1, right=2, closed="right")
+
+    i3: pd.Interval = i1 + 1
+    i4: pd.Interval = i1 - 1
+    i5: pd.Interval = i1 * 2
+    i6: pd.Interval = i1 / 2
+    i7: pd.Interval = i1 // 2
+
+
+def test_max_intervals() -> None:
+    i1 = pd.Interval(
+        pd.Timestamp("2000-01-01"), pd.Timestamp("2000-01-02"), closed="both"
+    )
+    i2 = pd.Interval(
+        pd.Timestamp("2000-01-01T12:00:00"), pd.Timestamp("2000-01-02"), closed="both"
+    )
+    print(max(i1.left, i2.left))

pandas/tests/typing/valid/test_pandas.py

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+# flake8: noqa: F841
+# TODO: many functions need return types annotations for pyright
+# to run with reportGeneralTypeIssues = true
+import tempfile
+from typing import (
+    Any,
+    Dict,
+    List,
+    Union,
+)
+
+import pandas as pd
+
+from pandas.io.parsers import TextFileReader
+
+
+def test_types_to_datetime() -> None:
+    df = pd.DataFrame({"year": [2015, 2016], "month": [2, 3], "day": [4, 5]})
+    # error: No overload variant of "to_datetime" matches argument type "DataFrame"
+    pd.to_datetime(df)  # type: ignore[call-overload]
+    # error: No overload variant of "to_datetime" matches argument types "DataFrame",
+    # "str", "str", "bool"
+    pd.to_datetime(  # type: ignore[call-overload]
+        df, unit="s", origin="unix", infer_datetime_format=True
+    )
+    # error: No overload variant of "to_datetime" matches argument types "DataFrame",
+    # "str", "bool", "None", "str", "bool"
+    pd.to_datetime(  # type: ignore[call-overload]
+        df, unit="ns", dayfirst=True, utc=None, format="%M:%D", exact=False
+    )
+    pd.to_datetime([1, 2], unit="D", origin=pd.Timestamp("01/01/2000"))
+    pd.to_datetime([1, 2], unit="D", origin=3)
+
+
+def test_types_concat() -> None:
+    s = pd.Series([0, 1, -10])
+    s2 = pd.Series([7, -5, 10])
+
+    pd.concat([s, s2])
+    pd.concat([s, s2], axis=1)
+    pd.concat([s, s2], keys=["first", "second"], sort=True)
+    pd.concat([s, s2], keys=["first", "second"], names=["source", "row"])
+
+    # Depends on the axis
+    # error: Argument 1 to "concat" has incompatible type "Dict[str, Series]"; expected
+    # "Union[Iterable[DataFrame], Mapping[Hashable, DataFrame]]"
+    rs1: Union[pd.Series, pd.DataFrame] = pd.concat(
+        {"a": s, "b": s2}  # type:ignore[arg-type]
+    )
+    # error: Argument 1 to "concat" has incompatible type "Dict[str, Series]"; expected
+    # "Union[Iterable[NDFrame], Mapping[Hashable, NDFrame]]"
+    rs1a: Union[pd.Series, pd.DataFrame] = pd.concat(
+        {"a": s, "b": s2}, axis=1  # type:ignore[arg-type]
+    )
+    # error: Argument 1 to "concat" has incompatible type "Dict[int, Series]"; expected
+    # "Union[Iterable[DataFrame], Mapping[Hashable, DataFrame]]"
+    rs2: Union[pd.Series, pd.DataFrame] = pd.concat(
+        {1: s, 2: s2}  # type:ignore[arg-type]
+    )
+    # error: Argument 1 to "concat" has incompatible type "Dict[int, Series]"; expected
+    # "Union[Iterable[NDFrame], Mapping[Hashable, NDFrame]]"
+    rs2a: Union[pd.Series, pd.DataFrame] = pd.concat(
+        {1: s, 2: s2}, axis=1  # type:ignore[arg-type]
+    )
+    # error: Argument 1 to "concat" has incompatible type "Dict[Optional[int], Series]";
+    # expected "Union[Iterable[DataFrame], Mapping[Hashable, DataFrame]]"
+    rs3: Union[pd.Series, pd.DataFrame] = pd.concat(
+        {1: s, None: s2}  # type:ignore[arg-type]
+    )
+    # error: Argument 1 to "concat" has incompatible type "Dict[Optional[int], Series]";
+    # expected "Union[Iterable[NDFrame], Mapping[Hashable, NDFrame]]"
+    rs3a: Union[pd.Series, pd.DataFrame] = pd.concat(
+        {1: s, None: s2}, axis=1  # type:ignore[arg-type]
+    )
+
+    df = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})
+    df2 = pd.DataFrame(data={"col1": [10, 20], "col2": [30, 40]})
+
+    pd.concat([df, df2])
+    pd.concat([df, df2], axis=1)
+    pd.concat([df, df2], keys=["first", "second"], sort=True)
+    pd.concat([df, df2], keys=["first", "second"], names=["source", "row"])
+
+    # error: Incompatible types in assignment (expression has type "Union[DataFrame,
+    # Series]", variable has type "DataFrame")
+    # error: Argument 1 to "concat" has incompatible type "Dict[str, DataFrame]";
+    # expected "Union[Iterable[NDFrame], Mapping[Hashable, NDFrame]]"
+    result: pd.DataFrame = pd.concat(  # type: ignore[assignment]
+        {
+            "a": pd.DataFrame([1, 2, 3]),
+            "b": pd.DataFrame([4, 5, 6]),
+        },  # type:ignore[arg-type]
+        axis=1,
+    )
+    # error: Argument 1 to "concat" has incompatible type "Dict[str, Series]"; expected
+    # "Union[Iterable[NDFrame], Mapping[Hashable, NDFrame]]"
+    result2: Union[pd.DataFrame, pd.Series] = pd.concat(
+        {
+            "a": pd.Series([1, 2, 3]),
+            "b": pd.Series([4, 5, 6]),
+        },  # type:ignore[arg-type]
+        axis=1,
+    )
+
+    # error: Argument 1 to "concat" has incompatible type "Dict[str, DataFrame]";
+    # expected "Union[Iterable[DataFrame], Mapping[Hashable, DataFrame]]"
+    rdf1: pd.DataFrame = pd.concat({"a": df, "b": df2})  # type:ignore[arg-type]
+    # error: Argument 1 to "concat" has incompatible type "Dict[int, DataFrame]";
+    # expected "Union[Iterable[DataFrame], Mapping[Hashable, DataFrame]]"
+    rdf2: pd.DataFrame = pd.concat({1: df, 2: df2})  # type:ignore[arg-type]
+    # error: Argument 1 to "concat" has incompatible type "Dict[Optional[int],
+    # DataFrame]"; expected "Union[Iterable[DataFrame], Mapping[Hashable, DataFrame]]"
+    rdf3: pd.DataFrame = pd.concat({1: df, None: df2})  # type:ignore[arg-type]
+
+    rdf4: pd.DataFrame = pd.concat(list(map(lambda x: s2, ["some_value", 3])), axis=1)
+
+
+def test_types_json_normalize() -> None:
+    data1: List[Dict[str, Any]] = [
+        {"id": 1, "name": {"first": "Coleen", "last": "Volk"}},
+        {"name": {"given": "More", "family": "Regner"}},
+        {"id": 2, "name": "Faye Raker"},
+    ]
+    df1: pd.DataFrame = pd.json_normalize(data=data1)
+    df2: pd.DataFrame = pd.json_normalize(data=data1, max_level=0, sep=";")
+    df3: pd.DataFrame = pd.json_normalize(
+        data=data1, meta_prefix="id", record_prefix="name", errors="raise"
+    )
+    df4: pd.DataFrame = pd.json_normalize(data=data1, record_path=None, meta="id")
+    data2: Dict[str, Any] = {"name": {"given": "More", "family": "Regner"}}
+    df5: pd.DataFrame = pd.json_normalize(data=data2)
+
+
+def test_types_read_csv() -> None:
+    df = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})
+    # error: Incompatible types in assignment (expression has type "Optional[str]",
+    # variable has type "str")
+    csv_df: str = df.to_csv()  # type: ignore[assignment]
+
+    with tempfile.NamedTemporaryFile() as file:
+        df.to_csv(file.name)
+        df2: pd.DataFrame = pd.read_csv(file.name)
+        df3: pd.DataFrame = pd.read_csv(file.name, sep="a", squeeze=False)
+        df4: pd.DataFrame = pd.read_csv(
+            file.name,
+            header=None,
+            prefix="b",
+            mangle_dupe_cols=True,
+            keep_default_na=False,
+        )
+        df5: pd.DataFrame = pd.read_csv(
+            file.name, engine="python", true_values=[0, 1, 3], na_filter=False
+        )
+        df6: pd.DataFrame = pd.read_csv(
+            file.name,
+            skiprows=lambda x: x in [0, 2],
+            skip_blank_lines=True,
+            dayfirst=False,
+        )
+        df7: pd.DataFrame = pd.read_csv(file.name, nrows=2)
+        tfr1: TextFileReader = pd.read_csv(
+            file.name, nrows=2, iterator=True, chunksize=3
+        )
+        tfr2: TextFileReader = pd.read_csv(file.name, nrows=2, chunksize=1)
+        tfr3: TextFileReader = pd.read_csv(
+            file.name, nrows=2, iterator=False, chunksize=1
+        )
+        tfr4: TextFileReader = pd.read_csv(file.name, nrows=2, iterator=True)