ueshin commented on code in PR #41240:
URL: https://github.com/apache/spark/pull/41240#discussion_r1203029027
##########
python/pyspark/sql/pandas/conversion.py:
##########
@@ -375,22 +379,105 @@ def _convert_from_pandas(
assert isinstance(self, SparkSession)
if timezone is not None:
- from pyspark.sql.pandas.types import
_check_series_convert_timestamps_tz_local
+ from pyspark.sql.pandas.types import (
+ _check_series_convert_timestamps_tz_local,
+ _get_local_timezone,
+ )
from pandas.core.dtypes.common import is_datetime64tz_dtype,
is_timedelta64_dtype
copied = False
if isinstance(schema, StructType):
- for field in schema:
- # TODO: handle nested timestamps, such as
ArrayType(TimestampType())?
- if isinstance(field.dataType, TimestampType):
- s =
_check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
- if s is not pdf[field.name]:
- if not copied:
- # Copy once if the series is modified to
prevent the original
- # Pandas DataFrame from being updated
- pdf = pdf.copy()
- copied = True
- pdf[field.name] = s
+
+ def _create_converter(data_type: DataType) ->
Callable[[pd.Series], pd.Series]:
+ if isinstance(data_type, TimestampType):
+
+ def correct_timestamp(pser: pd.Series) -> pd.Series:
+ return
_check_series_convert_timestamps_tz_local(pser, timezone)
+
+ return correct_timestamp
+
+ def _converter(dt: DataType) -> Optional[Callable[[Any],
Any]]:
+ if isinstance(dt, ArrayType):
+ element_conv = _converter(dt.elementType) or
(lambda x: x)
+
+ def convert_array(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return [element_conv(v) for v in value]
+
+ return convert_array
+
+ elif isinstance(dt, MapType):
+ key_conv = _converter(dt.keyType) or (lambda x: x)
+ value_conv = _converter(dt.valueType) or (lambda
x: x)
+
+ def convert_map(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return {key_conv(k): value_conv(v) for k,
v in value.items()}
+
+ return convert_map
+
+ elif isinstance(dt, StructType):
+ field_names = dt.names
+ dedup_field_names = _dedup_names(field_names)
+ field_convs = [
+ _converter(f.dataType) or (lambda x: x) for f
in dt.fields
+ ]
+
+ def convert_struct(value: Any) -> Any:
+ if value is None:
+ return None
+ elif isinstance(value, dict):
+ _values = [
+ field_convs[i](value.get(name, None))
+ for i, name in
enumerate(dedup_field_names)
+ ]
+ return _create_row(field_names, _values)
+ else:
+ _values = [
+ field_convs[i](value[i]) for i, name
in enumerate(value)
+ ]
+ return _create_row(field_names, _values)
+
+ return convert_struct
+
+ elif isinstance(dt, TimestampType):
+
+ def convert_timestamp(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return (
+ pd.Timestamp(value)
+ .tz_localize(timezone,
ambiguous=False) # type: ignore
Review Comment:
Let me leave it without the error tag. I see a weird error in CI I can't
reproduce in my local:
```
starting mypy annotations test...
annotations failed mypy checks:
python/pyspark/sql/pandas/conversion.py:454: error: Unexpected keyword
argument "ambiguous" for "tz_localize" of "Timestamp"; did you mean
"ambigious"? [call-arg]
/usr/local/lib/python3.9/dist-packages/pandas/_libs/tslibs/timestamps.pyi:38:
note: "tz_localize" of "Timestamp" defined here
python/pyspark/sql/pandas/conversion.py:456: error: unused "type: ignore"
comment
Found 2 errors in 1 file (checked 512 source files)
```
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