make test match GH issue and move test to proper location

Author: Julian048

pandas/tests/dtypes/test_dtypes.py

@@ -1,7 +1,6 @@
 import re
 
 import numpy as np
-import pyarrow as pa
 import pytest
 import pytz
 
@@ -33,14 +32,9 @@
     CategoricalIndex,
     DatetimeIndex,
     IntervalIndex,
-    NaT,
     Series,
     SparseDtype,
-    Timedelta,
-    Timestamp,
-    concat,
     date_range,
-    timedelta_range,
 )
 import pandas._testing as tm
 from pandas.core.arrays.sparse import SparseArray
@@ -1199,275 +1193,3 @@ def test_multi_column_dtype_assignment():
 
     df["b"] = 0
     tm.assert_frame_equal(df, expected)
-
-
-@pytest.mark.parametrize(
-    "unit",
-    [
-        "s",
-        "ms",
-        "us",
-        "ns",
-    ],
-)
-def test_convert_dtypes_timestamp(unit):
-    series = Series(date_range("2020-01-01", "2020-01-02", freq="1min"))
-    expected = series.astype(f"timestamp[{unit}][pyarrow]")
-
-    converted = expected.convert_dtypes(dtype_backend="pyarrow")
-
-    tm.assert_series_equal(expected, converted)
-
-
-@pytest.mark.parametrize(
-    "unit",
-    [
-        "s",
-        "ms",
-        "us",
-        "ns",
-    ],
-)
-def test_convert_dtypes_duration(unit):
-    series = Series(timedelta_range("1s", "10s", freq="1s"))
-    expected = series.astype(f"duration[{unit}][pyarrow]")
-
-    converted = expected.convert_dtypes(dtype_backend="pyarrow")
-
-    tm.assert_series_equal(expected, converted)
-
-
-@pytest.mark.parametrize(
-    "timestamp_unit, duration_unit",
-    [
-        ("s", "s"),
-        ("s", "ms"),
-        ("s", "us"),
-        ("s", "ns"),
-        ("ms", "s"),
-        ("ms", "ms"),
-        ("ms", "us"),
-        ("ms", "ns"),
-        ("us", "s"),
-        ("us", "ms"),
-        ("us", "us"),
-        ("us", "ns"),
-        ("ns", "s"),
-        ("ns", "ms"),
-        ("ns", "us"),
-        ("ns", "ns"),
-    ],
-)
-def test_convert_dtypes_timestamp_and_duration(timestamp_unit, duration_unit):
-    timestamp_series = Series(
-        date_range("2020-01-01", "2020-01-02", freq="1min")
-    ).astype(f"timestamp[{timestamp_unit}][pyarrow]")
-    duration_series = Series(timedelta_range("1s", "10s", freq="1s")).astype(
-        f"duration[{duration_unit}][pyarrow]"
-    )
-
-    df = concat([timestamp_series, duration_series], axis=1)
-    converted = df.convert_dtypes(dtype_backend="pyarrow")
-
-    tm.assert_frame_equal(df, converted)
-
-
-@pytest.mark.parametrize(
-    "unit",
-    [
-        "s",
-        "ms",
-        "us",
-        "ns",
-    ],
-)
-def test_convert_dtypes_datetime(unit):
-    series = Series(date_range("2020-01-01", "2020-01-02", freq="1min")).astype(
-        f"datetime64[{unit}]"
-    )
-
-    expected = series.astype(f"timestamp[{unit}][pyarrow]")
-    converted = series.convert_dtypes(dtype_backend="pyarrow")
-
-    tm.assert_series_equal(expected, converted)
-
-
-@pytest.mark.parametrize(
-    "unit",
-    [
-        "s",
-        "ms",
-        "us",
-        "ns",
-    ],
-)
-def test_convert_dtypes_timedelta(unit):
-    series = Series(timedelta_range("1s", "10s", freq="1s")).astype(
-        f"timedelta64[{unit}]"
-    )
-
-    expected = series.astype(f"duration[{unit}][pyarrow]")
-    converted = series.convert_dtypes(dtype_backend="pyarrow")
-
-    tm.assert_series_equal(expected, converted)
-
-
-@pytest.mark.parametrize(
-    "unit",
-    [
-        "s",
-        "ms",
-        "us",
-        "ns",
-    ],
-)
-def test_pa_table_to_pandas_datetime(unit):
-    df = pd.DataFrame(date_range("2020-01-01", "2020-01-02", freq="1min")).astype(
-        f"datetime64[{unit}]"
-    )
-    df_converted_to_pa = pa.table(df)
-    df_back_to_pd = df_converted_to_pa.to_pandas()
-
-    tm.assert_frame_equal(df, df_back_to_pd)
-
-
-@pytest.mark.parametrize(
-    "unit",
-    [
-        "s",
-        "ms",
-        "us",
-        "ns",
-    ],
-)
-def test_pa_table_to_pandas_timedelta(unit):
-    df = pd.DataFrame(timedelta_range("1s", "10s", freq="1s")).astype(
-        f"timedelta64[{unit}]"
-    )
-    df_converted_to_pa = pa.table(df)
-    df_back_to_pd = df_converted_to_pa.to_pandas()
-
-    tm.assert_frame_equal(df, df_back_to_pd)
-
-
-@pytest.mark.parametrize(
-    "datetime_unit, timedelta_unit",
-    [
-        ("s", "s"),
-        ("s", "ms"),
-        ("s", "us"),
-        ("s", "ns"),
-        ("ms", "s"),
-        ("ms", "ms"),
-        ("ms", "us"),
-        ("ms", "ns"),
-        ("us", "s"),
-        ("us", "ms"),
-        ("us", "us"),
-        ("us", "ns"),
-        ("ns", "s"),
-        ("ns", "ms"),
-        ("ns", "us"),
-        ("ns", "ns"),
-    ],
-)
-def test_pa_table_and_to_pandas_datetime_and_timedelta(datetime_unit, timedelta_unit):
-    timestamp_series = Series(
-        date_range("2020-01-01", "2020-01-02", freq="1min")
-    ).astype(f"datetime64[{datetime_unit}]")
-    duration_series = Series(timedelta_range("1s", "10s", freq="1s")).astype(
-        f"timedelta64[{timedelta_unit}]"
-    )
-
-    df = concat([timestamp_series, duration_series], axis=1)
-    df_converted_to_pa = pa.table(df)
-    df_back_to_pd = df_converted_to_pa.to_pandas()
-
-    tm.assert_frame_equal(df, df_back_to_pd)
-
-
-@pytest.mark.parametrize(
-    "unit",
-    [
-        "s",
-        "ms",
-        "us",
-        "ns",
-    ],
-)
-def test_pa_table_to_pandas_timestamp(unit):
-    df = pd.DataFrame(date_range("2020-01-01", "2020-01-02", freq="1min")).astype(
-        f"timestamp[{unit}][pyarrow]"
-    )
-    df_converted_to_pa = pa.table(df)
-    df_back_to_pd = df_converted_to_pa.to_pandas()
-
-    tm.assert_frame_equal(df, df_back_to_pd)
-
-
-@pytest.mark.parametrize(
-    "unit",
-    [
-        "s",
-        "ms",
-        "us",
-        "ns",
-    ],
-)
-def test_pa_table_to_pandas_duration(unit):
-    df = pd.DataFrame(timedelta_range("1s", "10s", freq="1s")).astype(
-        f"duration[{unit}][pyarrow]"
-    )
-    df_converted_to_pa = pa.table(df)
-    df_back_to_pd = df_converted_to_pa.to_pandas()
-
-    tm.assert_frame_equal(df, df_back_to_pd)
-
-
-@pytest.mark.parametrize(
-    "timestamp_unit, duration_unit",
-    [
-        ("s", "s"),
-        ("s", "ms"),
-        ("s", "us"),
-        ("s", "ns"),
-        ("ms", "s"),
-        ("ms", "ms"),
-        ("ms", "us"),
-        ("ms", "ns"),
-        ("us", "s"),
-        ("us", "ms"),
-        ("us", "us"),
-        ("us", "ns"),
-        ("ns", "s"),
-        ("ns", "ms"),
-        ("ns", "us"),
-        ("ns", "ns"),
-    ],
-)
-def test_pa_table_and_to_pandas_timestamp_and_duration(timestamp_unit, duration_unit):
-    timestamp_series = Series(
-        date_range("2020-01-01", "2020-01-02", freq="1min")
-    ).astype(f"timestamp[{timestamp_unit}][pyarrow]")
-    duration_series = Series(timedelta_range("1s", "10s", freq="1s")).astype(
-        f"duration[{duration_unit}][pyarrow]"
-    )
-
-    df = concat([timestamp_series, duration_series], axis=1)
-    df_converted_to_pa = pa.table(df)
-    df_back_to_pd = df_converted_to_pa.to_pandas()
-
-    tm.assert_frame_equal(df, df_back_to_pd)
-
-
-def test_conversion_with_missing_values():
-    df = pd.DataFrame(
-        {
-            "timestamp_col": [Timestamp("2020-01-01"), NaT],
-            "duration_col": [Timedelta("1s"), NaT],
-        }
-    )
-    df_coverted_to_pa = pa.table(df)
-    df_back_to_pd = df_coverted_to_pa.to_pandas()
-
-    tm.assert_frame_equal(df, df_back_to_pd)

pandas/tests/frame/methods/test_convert_dtypes.py

@@ -167,3 +167,18 @@ def test_convert_dtypes_pyarrow_to_np_nullable(self):
         result = ser.convert_dtypes(dtype_backend="numpy_nullable")
         expected = pd.DataFrame(range(2), dtype="Int32")
         tm.assert_frame_equal(result, expected)
+
+    def test_convert_dtypes_timestamp_and_duration(self):
+        # GH 54191
+        pytest.importorskip("pyarrow")
+        timestamp_series = pd.Series(
+            pd.date_range("2020-01-01", "2020-01-02", freq="1min")
+        ).astype("timestamp[ms][pyarrow]")
+        duration_series = pd.Series(pd.timedelta_range("1s", "10s", freq="1s")).astype(
+            "duration[ms][pyarrow]"
+        )
+
+        df = pd.concat([timestamp_series, duration_series], axis=1)
+        converted = df.convert_dtypes(dtype_backend="pyarrow")
+
+        tm.assert_frame_equal(df, converted)