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PERF: Improve performance for arrow engine and dtype_backend=pyarrow for datetime conversion #52548

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merged 7 commits into from
Apr 11, 2023
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PERF: Improve performance for arrow engine and dtype_backend=pyarrow for datetime conversion #52548

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PERF: Improve performance for arrow engine and dtype_backend=pyarrow …
phofl Apr 8, 2023
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Merge branch 'main' into 52546
phofl Apr 11, 2023
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15 changes: 15 additions & 0 deletions asv_bench/benchmarks/io/csv.py
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Original file line number Diff line number Diff line change
Expand Up @@ -555,4 +555,19 @@ def time_read_csv_index_col(self):
read_csv(self.StringIO_input, index_col="a")


class ReadCSVDatePyarrowEngine(StringIORewind):
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Can you fuse this benchmark with ReadCSVParseDates above?
(You might have to disable time_multiple_dates for the pyarrow engine)

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I'd prefer keeping them separate for now since I am expecting to add more arrow versions that aren't really compatible with the others.

def setup(self):
count_elem = 100_000
data = "a\n" + "2019-12-31\n" * count_elem
self.StringIO_input = StringIO(data)

def time_read_csv_index_col(self):
read_csv(
self.StringIO_input,
parse_dates=["a"],
engine="pyarrow",
dtype_backend="pyarrow",
)


from ..pandas_vb_common import setup # noqa: F401 isort:skip
13 changes: 13 additions & 0 deletions pandas/io/parsers/base_parser.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,6 +61,7 @@
from pandas.core.dtypes.missing import isna

from pandas import (
ArrowDtype,
DatetimeIndex,
StringDtype,
)
Expand Down Expand Up @@ -867,6 +868,7 @@ def _do_date_conversions(
self.index_names,
names,
keep_date_col=self.keep_date_col,
dtype_backend=self.dtype_backend,
)

return names, data
Expand Down Expand Up @@ -1203,6 +1205,7 @@ def _process_date_conversion(
index_names,
columns,
keep_date_col: bool = False,
dtype_backend=lib.no_default,
):
def _isindex(colspec):
return (isinstance(index_col, list) and colspec in index_col) or (
Expand All @@ -1228,6 +1231,16 @@ def _isindex(colspec):
colspec = orig_names[colspec]
if _isindex(colspec):
continue
elif dtype_backend == "pyarrow":
import pyarrow as pa

dtype = data_dict[colspec].dtype
if isinstance(dtype, ArrowDtype) and (
pa.types.is_timestamp(dtype.pyarrow_dtype)
or pa.types.is_date(dtype.pyarrow_dtype)
):
continue

# Pyarrow engine returns Series which we need to convert to
# numpy array before converter, its a no-op for other parsers
data_dict[colspec] = converter(
Expand Down