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