jamtur01's picture
Upload folder using huggingface_hub
9c6594c verified
from __future__ import annotations
from typing import (
TYPE_CHECKING,
Literal,
)
import numpy as np
from pandas._config import using_string_dtype
from pandas._libs import lib
from pandas.compat import (
pa_version_under18p0,
pa_version_under19p0,
)
from pandas.compat._optional import import_optional_dependency
import pandas as pd
if TYPE_CHECKING:
from collections.abc import Callable
import pyarrow
from pandas._typing import DtypeBackend
def _arrow_dtype_mapping() -> dict:
pa = import_optional_dependency("pyarrow")
return {
pa.int8(): pd.Int8Dtype(),
pa.int16(): pd.Int16Dtype(),
pa.int32(): pd.Int32Dtype(),
pa.int64(): pd.Int64Dtype(),
pa.uint8(): pd.UInt8Dtype(),
pa.uint16(): pd.UInt16Dtype(),
pa.uint32(): pd.UInt32Dtype(),
pa.uint64(): pd.UInt64Dtype(),
pa.bool_(): pd.BooleanDtype(),
pa.string(): pd.StringDtype(),
pa.float32(): pd.Float32Dtype(),
pa.float64(): pd.Float64Dtype(),
pa.string(): pd.StringDtype(),
pa.large_string(): pd.StringDtype(),
}
def _arrow_string_types_mapper() -> Callable:
pa = import_optional_dependency("pyarrow")
mapping = {
pa.string(): pd.StringDtype(na_value=np.nan),
pa.large_string(): pd.StringDtype(na_value=np.nan),
}
if not pa_version_under18p0:
mapping[pa.string_view()] = pd.StringDtype(na_value=np.nan)
return mapping.get
def arrow_table_to_pandas(
table: pyarrow.Table,
dtype_backend: DtypeBackend | Literal["numpy"] | lib.NoDefault = lib.no_default,
null_to_int64: bool = False,
to_pandas_kwargs: dict | None = None,
) -> pd.DataFrame:
if to_pandas_kwargs is None:
to_pandas_kwargs = {}
pa = import_optional_dependency("pyarrow")
types_mapper: type[pd.ArrowDtype] | None | Callable
if dtype_backend == "numpy_nullable":
mapping = _arrow_dtype_mapping()
if null_to_int64:
# Modify the default mapping to also map null to Int64
# (to match other engines - only for CSV parser)
mapping[pa.null()] = pd.Int64Dtype()
types_mapper = mapping.get
elif dtype_backend == "pyarrow":
types_mapper = pd.ArrowDtype
elif using_string_dtype():
if pa_version_under19p0:
types_mapper = _arrow_string_types_mapper()
else:
types_mapper = None
elif dtype_backend is lib.no_default or dtype_backend == "numpy":
types_mapper = None
else:
raise NotImplementedError
df = table.to_pandas(types_mapper=types_mapper, **to_pandas_kwargs)
return df