191 lines
6.1 KiB
Python
191 lines
6.1 KiB
Python
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
|
|
|
|
from pandas.core.dtypes.common import pandas_dtype
|
|
|
|
import pandas as pd
|
|
|
|
if TYPE_CHECKING:
|
|
from collections.abc import (
|
|
Callable,
|
|
Hashable,
|
|
Sequence,
|
|
)
|
|
|
|
import pyarrow
|
|
|
|
from pandas._typing import (
|
|
DtypeArg,
|
|
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,
|
|
dtype: DtypeArg | None = None,
|
|
names: Sequence[Hashable] | None = None,
|
|
) -> pd.DataFrame:
|
|
pa = import_optional_dependency("pyarrow")
|
|
|
|
to_pandas_kwargs = {} if to_pandas_kwargs is None else to_pandas_kwargs
|
|
|
|
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()
|
|
elif dtype is not None:
|
|
# GH#56136 Avoid lossy conversion to float64
|
|
# We'll convert to numpy below if
|
|
types_mapper = {
|
|
pa.int8(): pd.Int8Dtype(),
|
|
pa.int16(): pd.Int16Dtype(),
|
|
pa.int32(): pd.Int32Dtype(),
|
|
pa.int64(): pd.Int64Dtype(),
|
|
}.get
|
|
else:
|
|
types_mapper = None
|
|
elif dtype_backend is lib.no_default or dtype_backend == "numpy":
|
|
if dtype is not None:
|
|
# GH#56136 Avoid lossy conversion to float64
|
|
# We'll convert to numpy below if
|
|
types_mapper = {
|
|
pa.int8(): pd.Int8Dtype(),
|
|
pa.int16(): pd.Int16Dtype(),
|
|
pa.int32(): pd.Int32Dtype(),
|
|
pa.int64(): pd.Int64Dtype(),
|
|
}.get
|
|
else:
|
|
types_mapper = None
|
|
else:
|
|
raise NotImplementedError
|
|
|
|
df = table.to_pandas(types_mapper=types_mapper, **to_pandas_kwargs)
|
|
return _post_convert_dtypes(df, dtype_backend, dtype, names)
|
|
|
|
|
|
def _post_convert_dtypes(
|
|
df: pd.DataFrame,
|
|
dtype_backend: DtypeBackend | Literal["numpy"] | lib.NoDefault,
|
|
dtype: DtypeArg | None,
|
|
names: Sequence[Hashable] | None,
|
|
) -> pd.DataFrame:
|
|
if dtype is not None and (
|
|
dtype_backend is lib.no_default or dtype_backend == "numpy"
|
|
):
|
|
# GH#56136 apply any user-provided dtype, and convert any IntegerDtype
|
|
# columns the user didn't explicitly ask for.
|
|
if isinstance(dtype, dict):
|
|
if names is not None:
|
|
df.columns = names
|
|
|
|
cmp_dtypes = {
|
|
pd.Int8Dtype(),
|
|
pd.Int16Dtype(),
|
|
pd.Int32Dtype(),
|
|
pd.Int64Dtype(),
|
|
}
|
|
for col in df.columns:
|
|
if col not in dtype and df[col].dtype in cmp_dtypes:
|
|
# Any key that the user didn't explicitly specify
|
|
# that got converted to IntegerDtype now gets converted
|
|
# to numpy dtype.
|
|
dtype[col] = df[col].dtype.numpy_dtype
|
|
|
|
# Ignore non-existent columns from dtype mapping
|
|
# like other parsers do
|
|
dtype = {
|
|
key: pandas_dtype(dtype[key]) for key in dtype if key in df.columns
|
|
}
|
|
|
|
else:
|
|
dtype = pandas_dtype(dtype)
|
|
|
|
try:
|
|
df = df.astype(dtype)
|
|
except TypeError as err:
|
|
# GH#44901 reraise to keep api consistent
|
|
raise ValueError(str(err)) from err
|
|
|
|
if (
|
|
not using_string_dtype()
|
|
and dtype != "str"
|
|
and (dtype_backend is lib.no_default or dtype_backend == "numpy")
|
|
):
|
|
# Convert any StringDtype columns back to object dtype (pyarrow always
|
|
# uses string dtype even when the infer_string option is False)
|
|
for col, dtype in zip(df.columns, df.dtypes, strict=True):
|
|
if isinstance(dtype, pd.StringDtype) and dtype.na_value is np.nan:
|
|
df[col] = df[col].astype("object").fillna(None)
|
|
if isinstance(dtype, pd.CategoricalDtype):
|
|
cat_dtype = dtype.categories.dtype
|
|
if (
|
|
isinstance(cat_dtype, pd.StringDtype)
|
|
and cat_dtype.na_value is np.nan
|
|
):
|
|
cat_dtype = pd.CategoricalDtype(
|
|
categories=dtype.categories.astype("object"),
|
|
ordered=dtype.ordered,
|
|
)
|
|
df[col] = df[col].astype(cat_dtype)
|
|
|
|
return df
|