rok commented on code in PR #48979:
URL: https://github.com/apache/arrow/pull/48979#discussion_r2828567128
##########
python/pyarrow/tests/parquet/test_basic.py:
##########
@@ -759,17 +760,23 @@ def test_fastparquet_cross_compatibility(tempdir):
fp_file = fp.ParquetFile(file_arrow)
df_fp = fp_file.to_pandas()
- tm.assert_frame_equal(df, df_fp)
+ # TODO: once fastparquet supports pandas 3 dtypes revert string and
categorical
+ # tests by removing `check_dtype=False` and `check_categorical=False` so
type
+ # equality is asserted again
+ tm.assert_frame_equal(df, df_fp, check_dtype=False,
check_categorical=False)
# Fastparquet -> arrow
file_fastparquet = str(tempdir / "cross_compat_fastparquet.parquet")
- fp.write(file_fastparquet, df)
+ # fastparquet can't write pandas 3.0 StringDtype
+ df_for_fp = df.copy()
+ df_for_fp['a'] = df_for_fp['a'].astype(object)
+ fp.write(file_fastparquet, df_for_fp)
table_fp = pq.read_pandas(file_fastparquet)
# for fastparquet written file, categoricals comes back as strings
# (no arrow schema in parquet metadata)
- df['f'] = df['f'].astype(object)
- tm.assert_frame_equal(table_fp.to_pandas(), df)
+ tm.assert_frame_equal(table_fp.to_pandas(), df_for_fp, check_dtype=False,
+ check_categorical=False)
Review Comment:
Maybe we can reuse `expected_types`?
```suggestion
tm.assert_frame_equal(table_fp.to_pandas(), df.astype(expected_types))
```
##########
python/pyarrow/tests/parquet/test_basic.py:
##########
@@ -759,17 +760,23 @@ def test_fastparquet_cross_compatibility(tempdir):
fp_file = fp.ParquetFile(file_arrow)
df_fp = fp_file.to_pandas()
- tm.assert_frame_equal(df, df_fp)
+ # TODO: once fastparquet supports pandas 3 dtypes revert string and
categorical
+ # tests by removing `check_dtype=False` and `check_categorical=False` so
type
+ # equality is asserted again
+ tm.assert_frame_equal(df, df_fp, check_dtype=False,
check_categorical=False)
Review Comment:
Would this work instead? Since underlying type of categorical is not `int`
it feels like it should?
```suggestion
# TODO: once fastparquet supports pandas 3's pd.StringDtype() remove
casting
expected_types = {"a": pd.StringDtype()}
tm.assert_frame_equal(df, df_fp.astype(expected_types))
```
##########
python/pyarrow/tests/parquet/test_basic.py:
##########
@@ -759,17 +760,23 @@ def test_fastparquet_cross_compatibility(tempdir):
fp_file = fp.ParquetFile(file_arrow)
df_fp = fp_file.to_pandas()
- tm.assert_frame_equal(df, df_fp)
+ # TODO: once fastparquet supports pandas 3 dtypes revert string and
categorical
+ # tests by removing `check_dtype=False` and `check_categorical=False` so
type
+ # equality is asserted again
+ tm.assert_frame_equal(df, df_fp, check_dtype=False,
check_categorical=False)
# Fastparquet -> arrow
file_fastparquet = str(tempdir / "cross_compat_fastparquet.parquet")
- fp.write(file_fastparquet, df)
+ # fastparquet can't write pandas 3.0 StringDtype
+ df_for_fp = df.copy()
+ df_for_fp['a'] = df_for_fp['a'].astype(object)
+ fp.write(file_fastparquet, df_for_fp)
Review Comment:
Perhaps we can avoid creating a new dataframe and only non-destructively
cast at write-time?
```suggestion
# fastparquet can't write pandas 3.0 StringDtype
fp_compatible_types = {"a": object}
fp.write(file_fastparquet, df.astype(fp_compatible_types))
```
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