fix(deps): update dependency pyarrow to v13

ibis/backends/pandas/execution/temporal.py

@@ -67,11 +67,7 @@ def execute_extract_microsecond_series(op, data, **kwargs):
 
 @execute_node.register(ops.ExtractEpochSeconds, (datetime.datetime, pd.Series))
 def execute_epoch_seconds(op, data, **kwargs):
-    # older versions of dask do not have a view method, so use astype
-    # instead
-    convert = getattr(data, "view", data.astype)
-    series = convert(np.int64)
-    return (series // 1_000_000_000).astype(np.int32)
+    return data.astype("datetime64[s]").astype("int64").astype("int32")
 
 
 @execute_node.register(

ibis/backends/tests/test_aggregation.py

@@ -158,7 +158,7 @@ def test_aggregate(backend, alltypes, df, result_fn, expected_fn):
     # (to match the output format of Ibis `aggregate`)
     expected = pd.DataFrame({"tmp": [expected_fn(df)]})
 
-    backend.assert_frame_equal(result, expected)
+    backend.assert_frame_equal(result, expected, check_dtype=False)
 
 
 @pytest.mark.parametrize(
@@ -187,8 +187,8 @@ def test_aggregate_grouped(backend, alltypes, df, result_fn, expected_fn):
     result2 = result2.sort_values(by=grouping_key_col).reset_index(drop=True)
     expected = expected.sort_values(by=grouping_key_col).reset_index(drop=True)
 
-    backend.assert_frame_equal(result1, expected)
-    backend.assert_frame_equal(result2, expected)
+    backend.assert_frame_equal(result1, expected, check_dtype=False)
+    backend.assert_frame_equal(result2, expected, check_dtype=False)
 
 
 @mark.notimpl(

ibis/backends/tests/test_temporal.py

@@ -318,7 +318,7 @@ def test_timestamp_extract_epoch_seconds(backend, alltypes, df):
     result = expr.execute()
 
     expected = backend.default_series_rename(
-        (df.timestamp_col.view("int64") // 1_000_000_000).astype("int32")
+        df.timestamp_col.astype("datetime64[s]").astype("int64").astype("int32")
     )
     backend.assert_series_equal(result, expected)
 
@@ -547,7 +547,7 @@ def test_date_truncate(backend, alltypes, df, unit):
     result = expr.execute()
     expected = backend.default_series_rename(expected)
 
-    backend.assert_series_equal(result, expected)
+    backend.assert_series_equal(result, expected.astype(result.dtype))
 
 
 @pytest.mark.parametrize(
@@ -732,7 +732,7 @@ def convert_to_offset(offset, displacement_type=displacement_type):
         expected = df.timestamp_col + offset
 
     expected = backend.default_series_rename(expected)
-    backend.assert_series_equal(result, expected.astype("datetime64[ns]"))
+    backend.assert_series_equal(result, expected.astype(result.dtype))
 
 
 @pytest.mark.parametrize(
@@ -975,7 +975,7 @@ def test_temporal_binop(backend, con, alltypes, df, expr_fn, expected_fn):
     result = con.execute(expr)
     expected = backend.default_series_rename(expected)
 
-    backend.assert_series_equal(result, expected)
+    backend.assert_series_equal(result, expected.astype(result.dtype))
 
 
 plus = lambda t, td: t.timestamp_col + pd.Timedelta(td)
@@ -1152,7 +1152,7 @@ def test_temporal_binop_pandas_timedelta(
     result = con.execute(expr)
     expected = backend.default_series_rename(expected)
 
-    backend.assert_series_equal(result, expected)
+    backend.assert_series_equal(result, expected.astype(result.dtype))
 
 
 @pytest.mark.parametrize("func_name", ["gt", "ge", "lt", "le", "eq", "ne"])
@@ -1296,7 +1296,7 @@ def test_interval_add_cast_scalar(backend, alltypes):
     expr = (timestamp_date + delta).name("result")
     result = expr.execute()
     expected = timestamp_date.name("result").execute() + pd.Timedelta(10, unit="D")
-    backend.assert_series_equal(result, expected)
+    backend.assert_series_equal(result, expected.astype(result.dtype))
 
 
 @pytest.mark.never(
@@ -1323,7 +1323,7 @@ def test_interval_add_cast_column(backend, alltypes, df):
         .add(df.bigint_col.astype("timedelta64[D]"))
         .rename("tmp")
     )
-    backend.assert_series_equal(result, expected)
+    backend.assert_series_equal(result, expected.astype(result.dtype))
 
 
 @pytest.mark.parametrize(
@@ -2038,7 +2038,7 @@ def test_date_column_from_ymd(con, alltypes, df):
     tbl = alltypes[expr.name("timestamp_col")]
     result = con.execute(tbl)
 
-    golden = df.timestamp_col.dt.date.astype("datetime64[ns]")
+    golden = df.timestamp_col.dt.date.astype(result.timestamp_col.dtype)
     tm.assert_series_equal(golden, result.timestamp_col)
 
 
@@ -2068,7 +2068,7 @@ def test_timestamp_column_from_ymdhms(con, alltypes, df):
     tbl = alltypes[expr.name("timestamp_col")]
     result = con.execute(tbl)
 
-    golden = df.timestamp_col.dt.floor("s").astype("datetime64[ns]")
+    golden = df.timestamp_col.dt.floor("s").astype(result.timestamp_col.dtype)
     tm.assert_series_equal(golden, result.timestamp_col)
 
 
@@ -2169,7 +2169,7 @@ def test_integer_cast_to_timestamp_column(backend, alltypes, df):
     expr = alltypes.int_col.cast("timestamp")
     expected = pd.to_datetime(df.int_col, unit="s").rename(expr.get_name())
     result = expr.execute()
-    backend.assert_series_equal(result, expected)
+    backend.assert_series_equal(result, expected.astype(result.dtype))
 
 
 @pytest.mark.notimpl(

ibis/backends/tests/test_timecontext.py

@@ -8,7 +8,6 @@
 
 import ibis
 from ibis.backends.tests.test_vectorized_udf import calc_mean, create_demean_struct_udf
-from ibis.config import option_context
 
 pytestmark = pytest.mark.notimpl(
     [
@@ -41,7 +40,8 @@ def context():
 
 
 def filter_by_time_context(df, context):
-    return df[(df["timestamp_col"] >= context[0]) & (df["timestamp_col"] < context[1])]
+    begin, end = context
+    return df[(df.timestamp_col >= begin) & (df.timestamp_col < end)]
 
 
 broken_pandas_grouped_rolling = pytest.mark.xfail(
@@ -51,14 +51,8 @@ def filter_by_time_context(df, context):
 )
 
 
-@pytest.fixture(scope="module")
-def ctx_col():
-    with option_context("context_adjustment.time_col", "timestamp_col"):
-        yield
-
-
 @pytest.mark.notimpl(["dask", "duckdb"])
-@pytest.mark.xfail_version(pyspark=["pyspark<3.1"])
+@pytest.mark.xfail_version(pyspark=["pyspark<3.1"], pandas=["pyarrow>=13", "pandas>=2"])
 @pytest.mark.parametrize(
     "window",
     [
@@ -77,9 +71,10 @@ def ctx_col():
         ),
     ],
 )
-def test_context_adjustment_window_udf(alltypes, context, window, ctx_col):
-    """This test case aims to test context adjustment of udfs in window
-    method."""
+def test_context_adjustment_window_udf(alltypes, context, window, monkeypatch):
+    """Test context adjustment of udfs in window methods."""
+    monkeypatch.setattr(ibis.options.context_adjustment, "time_col", "timestamp_col")
+
     expr = alltypes.mutate(v1=calc_mean(alltypes[TARGET_COL]).over(window))
     result = expr.execute(timecontext=context)
 
@@ -90,7 +85,10 @@ def test_context_adjustment_window_udf(alltypes, context, window, ctx_col):
 
 
 @pytest.mark.notimpl(["dask", "duckdb"])
-def test_context_adjustment_filter_before_window(alltypes, context, ctx_col):
+@pytest.mark.xfail_version(pandas=["pyarrow>=13", "pandas>=2"])
+def test_context_adjustment_filter_before_window(alltypes, context, monkeypatch):
+    monkeypatch.setattr(ibis.options.context_adjustment, "time_col", "timestamp_col")
+
     window = ibis.trailing_window(ibis.interval(days=3), order_by=ORDER_BY_COL)
 
     expr = alltypes[alltypes["bool_col"]]
@@ -106,11 +104,9 @@ def test_context_adjustment_filter_before_window(alltypes, context, ctx_col):
 
 
 @pytest.mark.notimpl(["duckdb", "pyspark"])
-def test_context_adjustment_multi_col_udf_non_grouped(
-    alltypes,
-    context,
-    ctx_col,
-):
+def test_context_adjustment_multi_col_udf_non_grouped(alltypes, context, monkeypatch):
+    monkeypatch.setattr(ibis.options.context_adjustment, "time_col", "timestamp_col")
+
     w = ibis.window(preceding=None, following=None)
 
     demean_struct_udf = create_demean_struct_udf(

pyproject.toml

@@ -39,7 +39,7 @@ numpy = ">=1,<2"
 pandas = ">=1.2.5,<3"
 parsy = ">=2,<3"
 pins = { version = ">=0.8.2,<1", extras = ["gcs"] }
-pyarrow = ">=2,<13"
+pyarrow = ">=2,<14"
 python-dateutil = ">=2.8.2,<3"
 pytz = ">=2022.7"
 rich = ">=12.4.4,<14"