500 lines
17 KiB
Python
500 lines
17 KiB
Python
|
""" parquet compat """
|
||
|
from __future__ import annotations
|
||
|
|
||
|
import io
|
||
|
import os
|
||
|
from typing import Any
|
||
|
from warnings import catch_warnings
|
||
|
|
||
|
from pandas._typing import (
|
||
|
FilePath,
|
||
|
ReadBuffer,
|
||
|
StorageOptions,
|
||
|
WriteBuffer,
|
||
|
)
|
||
|
from pandas.compat._optional import import_optional_dependency
|
||
|
from pandas.errors import AbstractMethodError
|
||
|
from pandas.util._decorators import doc
|
||
|
|
||
|
from pandas import (
|
||
|
DataFrame,
|
||
|
MultiIndex,
|
||
|
get_option,
|
||
|
)
|
||
|
from pandas.core.shared_docs import _shared_docs
|
||
|
from pandas.util.version import Version
|
||
|
|
||
|
from pandas.io.common import (
|
||
|
IOHandles,
|
||
|
get_handle,
|
||
|
is_fsspec_url,
|
||
|
is_url,
|
||
|
stringify_path,
|
||
|
)
|
||
|
|
||
|
|
||
|
def get_engine(engine: str) -> BaseImpl:
|
||
|
"""return our implementation"""
|
||
|
if engine == "auto":
|
||
|
engine = get_option("io.parquet.engine")
|
||
|
|
||
|
if engine == "auto":
|
||
|
# try engines in this order
|
||
|
engine_classes = [PyArrowImpl, FastParquetImpl]
|
||
|
|
||
|
error_msgs = ""
|
||
|
for engine_class in engine_classes:
|
||
|
try:
|
||
|
return engine_class()
|
||
|
except ImportError as err:
|
||
|
error_msgs += "\n - " + str(err)
|
||
|
|
||
|
raise ImportError(
|
||
|
"Unable to find a usable engine; "
|
||
|
"tried using: 'pyarrow', 'fastparquet'.\n"
|
||
|
"A suitable version of "
|
||
|
"pyarrow or fastparquet is required for parquet "
|
||
|
"support.\n"
|
||
|
"Trying to import the above resulted in these errors:"
|
||
|
f"{error_msgs}"
|
||
|
)
|
||
|
|
||
|
if engine == "pyarrow":
|
||
|
return PyArrowImpl()
|
||
|
elif engine == "fastparquet":
|
||
|
return FastParquetImpl()
|
||
|
|
||
|
raise ValueError("engine must be one of 'pyarrow', 'fastparquet'")
|
||
|
|
||
|
|
||
|
def _get_path_or_handle(
|
||
|
path: FilePath | ReadBuffer[bytes] | WriteBuffer[bytes],
|
||
|
fs: Any,
|
||
|
storage_options: StorageOptions = None,
|
||
|
mode: str = "rb",
|
||
|
is_dir: bool = False,
|
||
|
) -> tuple[
|
||
|
FilePath | ReadBuffer[bytes] | WriteBuffer[bytes], IOHandles[bytes] | None, Any
|
||
|
]:
|
||
|
"""File handling for PyArrow."""
|
||
|
path_or_handle = stringify_path(path)
|
||
|
if is_fsspec_url(path_or_handle) and fs is None:
|
||
|
fsspec = import_optional_dependency("fsspec")
|
||
|
|
||
|
fs, path_or_handle = fsspec.core.url_to_fs(
|
||
|
path_or_handle, **(storage_options or {})
|
||
|
)
|
||
|
elif storage_options and (not is_url(path_or_handle) or mode != "rb"):
|
||
|
# can't write to a remote url
|
||
|
# without making use of fsspec at the moment
|
||
|
raise ValueError("storage_options passed with buffer, or non-supported URL")
|
||
|
|
||
|
handles = None
|
||
|
if (
|
||
|
not fs
|
||
|
and not is_dir
|
||
|
and isinstance(path_or_handle, str)
|
||
|
and not os.path.isdir(path_or_handle)
|
||
|
):
|
||
|
# use get_handle only when we are very certain that it is not a directory
|
||
|
# fsspec resources can also point to directories
|
||
|
# this branch is used for example when reading from non-fsspec URLs
|
||
|
handles = get_handle(
|
||
|
path_or_handle, mode, is_text=False, storage_options=storage_options
|
||
|
)
|
||
|
fs = None
|
||
|
path_or_handle = handles.handle
|
||
|
return path_or_handle, handles, fs
|
||
|
|
||
|
|
||
|
class BaseImpl:
|
||
|
@staticmethod
|
||
|
def validate_dataframe(df: DataFrame) -> None:
|
||
|
|
||
|
if not isinstance(df, DataFrame):
|
||
|
raise ValueError("to_parquet only supports IO with DataFrames")
|
||
|
|
||
|
# must have value column names for all index levels (strings only)
|
||
|
if isinstance(df.columns, MultiIndex):
|
||
|
if not all(
|
||
|
x.inferred_type in {"string", "empty"} for x in df.columns.levels
|
||
|
):
|
||
|
raise ValueError(
|
||
|
"""
|
||
|
parquet must have string column names for all values in
|
||
|
each level of the MultiIndex
|
||
|
"""
|
||
|
)
|
||
|
else:
|
||
|
if df.columns.inferred_type not in {"string", "empty"}:
|
||
|
raise ValueError("parquet must have string column names")
|
||
|
|
||
|
# index level names must be strings
|
||
|
valid_names = all(
|
||
|
isinstance(name, str) for name in df.index.names if name is not None
|
||
|
)
|
||
|
if not valid_names:
|
||
|
raise ValueError("Index level names must be strings")
|
||
|
|
||
|
def write(self, df: DataFrame, path, compression, **kwargs):
|
||
|
raise AbstractMethodError(self)
|
||
|
|
||
|
def read(self, path, columns=None, **kwargs) -> DataFrame:
|
||
|
raise AbstractMethodError(self)
|
||
|
|
||
|
|
||
|
class PyArrowImpl(BaseImpl):
|
||
|
def __init__(self):
|
||
|
import_optional_dependency(
|
||
|
"pyarrow", extra="pyarrow is required for parquet support."
|
||
|
)
|
||
|
import pyarrow.parquet
|
||
|
|
||
|
# import utils to register the pyarrow extension types
|
||
|
import pandas.core.arrays._arrow_utils # noqa:F401
|
||
|
|
||
|
self.api = pyarrow
|
||
|
|
||
|
def write(
|
||
|
self,
|
||
|
df: DataFrame,
|
||
|
path: FilePath | WriteBuffer[bytes],
|
||
|
compression: str | None = "snappy",
|
||
|
index: bool | None = None,
|
||
|
storage_options: StorageOptions = None,
|
||
|
partition_cols: list[str] | None = None,
|
||
|
**kwargs,
|
||
|
) -> None:
|
||
|
self.validate_dataframe(df)
|
||
|
|
||
|
from_pandas_kwargs: dict[str, Any] = {"schema": kwargs.pop("schema", None)}
|
||
|
if index is not None:
|
||
|
from_pandas_kwargs["preserve_index"] = index
|
||
|
|
||
|
table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
|
||
|
|
||
|
path_or_handle, handles, kwargs["filesystem"] = _get_path_or_handle(
|
||
|
path,
|
||
|
kwargs.pop("filesystem", None),
|
||
|
storage_options=storage_options,
|
||
|
mode="wb",
|
||
|
is_dir=partition_cols is not None,
|
||
|
)
|
||
|
try:
|
||
|
if partition_cols is not None:
|
||
|
# writes to multiple files under the given path
|
||
|
self.api.parquet.write_to_dataset(
|
||
|
table,
|
||
|
path_or_handle,
|
||
|
compression=compression,
|
||
|
partition_cols=partition_cols,
|
||
|
**kwargs,
|
||
|
)
|
||
|
else:
|
||
|
# write to single output file
|
||
|
self.api.parquet.write_table(
|
||
|
table, path_or_handle, compression=compression, **kwargs
|
||
|
)
|
||
|
finally:
|
||
|
if handles is not None:
|
||
|
handles.close()
|
||
|
|
||
|
def read(
|
||
|
self,
|
||
|
path,
|
||
|
columns=None,
|
||
|
use_nullable_dtypes=False,
|
||
|
storage_options: StorageOptions = None,
|
||
|
**kwargs,
|
||
|
) -> DataFrame:
|
||
|
kwargs["use_pandas_metadata"] = True
|
||
|
|
||
|
to_pandas_kwargs = {}
|
||
|
if use_nullable_dtypes:
|
||
|
import pandas as pd
|
||
|
|
||
|
mapping = {
|
||
|
self.api.int8(): pd.Int8Dtype(),
|
||
|
self.api.int16(): pd.Int16Dtype(),
|
||
|
self.api.int32(): pd.Int32Dtype(),
|
||
|
self.api.int64(): pd.Int64Dtype(),
|
||
|
self.api.uint8(): pd.UInt8Dtype(),
|
||
|
self.api.uint16(): pd.UInt16Dtype(),
|
||
|
self.api.uint32(): pd.UInt32Dtype(),
|
||
|
self.api.uint64(): pd.UInt64Dtype(),
|
||
|
self.api.bool_(): pd.BooleanDtype(),
|
||
|
self.api.string(): pd.StringDtype(),
|
||
|
}
|
||
|
to_pandas_kwargs["types_mapper"] = mapping.get
|
||
|
manager = get_option("mode.data_manager")
|
||
|
if manager == "array":
|
||
|
to_pandas_kwargs["split_blocks"] = True # type: ignore[assignment]
|
||
|
|
||
|
path_or_handle, handles, kwargs["filesystem"] = _get_path_or_handle(
|
||
|
path,
|
||
|
kwargs.pop("filesystem", None),
|
||
|
storage_options=storage_options,
|
||
|
mode="rb",
|
||
|
)
|
||
|
try:
|
||
|
result = self.api.parquet.read_table(
|
||
|
path_or_handle, columns=columns, **kwargs
|
||
|
).to_pandas(**to_pandas_kwargs)
|
||
|
if manager == "array":
|
||
|
result = result._as_manager("array", copy=False)
|
||
|
return result
|
||
|
finally:
|
||
|
if handles is not None:
|
||
|
handles.close()
|
||
|
|
||
|
|
||
|
class FastParquetImpl(BaseImpl):
|
||
|
def __init__(self):
|
||
|
# since pandas is a dependency of fastparquet
|
||
|
# we need to import on first use
|
||
|
fastparquet = import_optional_dependency(
|
||
|
"fastparquet", extra="fastparquet is required for parquet support."
|
||
|
)
|
||
|
self.api = fastparquet
|
||
|
|
||
|
def write(
|
||
|
self,
|
||
|
df: DataFrame,
|
||
|
path,
|
||
|
compression="snappy",
|
||
|
index=None,
|
||
|
partition_cols=None,
|
||
|
storage_options: StorageOptions = None,
|
||
|
**kwargs,
|
||
|
) -> None:
|
||
|
self.validate_dataframe(df)
|
||
|
# thriftpy/protocol/compact.py:339:
|
||
|
# DeprecationWarning: tostring() is deprecated.
|
||
|
# Use tobytes() instead.
|
||
|
|
||
|
if "partition_on" in kwargs and partition_cols is not None:
|
||
|
raise ValueError(
|
||
|
"Cannot use both partition_on and "
|
||
|
"partition_cols. Use partition_cols for partitioning data"
|
||
|
)
|
||
|
elif "partition_on" in kwargs:
|
||
|
partition_cols = kwargs.pop("partition_on")
|
||
|
|
||
|
if partition_cols is not None:
|
||
|
kwargs["file_scheme"] = "hive"
|
||
|
|
||
|
# cannot use get_handle as write() does not accept file buffers
|
||
|
path = stringify_path(path)
|
||
|
if is_fsspec_url(path):
|
||
|
fsspec = import_optional_dependency("fsspec")
|
||
|
|
||
|
# if filesystem is provided by fsspec, file must be opened in 'wb' mode.
|
||
|
kwargs["open_with"] = lambda path, _: fsspec.open(
|
||
|
path, "wb", **(storage_options or {})
|
||
|
).open()
|
||
|
elif storage_options:
|
||
|
raise ValueError(
|
||
|
"storage_options passed with file object or non-fsspec file path"
|
||
|
)
|
||
|
|
||
|
with catch_warnings(record=True):
|
||
|
self.api.write(
|
||
|
path,
|
||
|
df,
|
||
|
compression=compression,
|
||
|
write_index=index,
|
||
|
partition_on=partition_cols,
|
||
|
**kwargs,
|
||
|
)
|
||
|
|
||
|
def read(
|
||
|
self, path, columns=None, storage_options: StorageOptions = None, **kwargs
|
||
|
) -> DataFrame:
|
||
|
parquet_kwargs: dict[str, Any] = {}
|
||
|
use_nullable_dtypes = kwargs.pop("use_nullable_dtypes", False)
|
||
|
if Version(self.api.__version__) >= Version("0.7.1"):
|
||
|
# We are disabling nullable dtypes for fastparquet pending discussion
|
||
|
parquet_kwargs["pandas_nulls"] = False
|
||
|
if use_nullable_dtypes:
|
||
|
raise ValueError(
|
||
|
"The 'use_nullable_dtypes' argument is not supported for the "
|
||
|
"fastparquet engine"
|
||
|
)
|
||
|
path = stringify_path(path)
|
||
|
handles = None
|
||
|
if is_fsspec_url(path):
|
||
|
fsspec = import_optional_dependency("fsspec")
|
||
|
|
||
|
if Version(self.api.__version__) > Version("0.6.1"):
|
||
|
parquet_kwargs["fs"] = fsspec.open(
|
||
|
path, "rb", **(storage_options or {})
|
||
|
).fs
|
||
|
else:
|
||
|
parquet_kwargs["open_with"] = lambda path, _: fsspec.open(
|
||
|
path, "rb", **(storage_options or {})
|
||
|
).open()
|
||
|
elif isinstance(path, str) and not os.path.isdir(path):
|
||
|
# use get_handle only when we are very certain that it is not a directory
|
||
|
# fsspec resources can also point to directories
|
||
|
# this branch is used for example when reading from non-fsspec URLs
|
||
|
handles = get_handle(
|
||
|
path, "rb", is_text=False, storage_options=storage_options
|
||
|
)
|
||
|
path = handles.handle
|
||
|
|
||
|
parquet_file = self.api.ParquetFile(path, **parquet_kwargs)
|
||
|
|
||
|
result = parquet_file.to_pandas(columns=columns, **kwargs)
|
||
|
|
||
|
if handles is not None:
|
||
|
handles.close()
|
||
|
return result
|
||
|
|
||
|
|
||
|
@doc(storage_options=_shared_docs["storage_options"])
|
||
|
def to_parquet(
|
||
|
df: DataFrame,
|
||
|
path: FilePath | WriteBuffer[bytes] | None = None,
|
||
|
engine: str = "auto",
|
||
|
compression: str | None = "snappy",
|
||
|
index: bool | None = None,
|
||
|
storage_options: StorageOptions = None,
|
||
|
partition_cols: list[str] | None = None,
|
||
|
**kwargs,
|
||
|
) -> bytes | None:
|
||
|
"""
|
||
|
Write a DataFrame to the parquet format.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
df : DataFrame
|
||
|
path : str, path object, file-like object, or None, default None
|
||
|
String, path object (implementing ``os.PathLike[str]``), or file-like
|
||
|
object implementing a binary ``write()`` function. If None, the result is
|
||
|
returned as bytes. If a string, it will be used as Root Directory path
|
||
|
when writing a partitioned dataset. The engine fastparquet does not
|
||
|
accept file-like objects.
|
||
|
|
||
|
.. versionchanged:: 1.2.0
|
||
|
|
||
|
engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
|
||
|
Parquet library to use. If 'auto', then the option
|
||
|
``io.parquet.engine`` is used. The default ``io.parquet.engine``
|
||
|
behavior is to try 'pyarrow', falling back to 'fastparquet' if
|
||
|
'pyarrow' is unavailable.
|
||
|
compression : {{'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}},
|
||
|
default 'snappy'. Name of the compression to use. Use ``None``
|
||
|
for no compression. The supported compression methods actually
|
||
|
depend on which engine is used. For 'pyarrow', 'snappy', 'gzip',
|
||
|
'brotli', 'lz4', 'zstd' are all supported. For 'fastparquet',
|
||
|
only 'gzip' and 'snappy' are supported.
|
||
|
index : bool, default None
|
||
|
If ``True``, include the dataframe's index(es) in the file output. If
|
||
|
``False``, they will not be written to the file.
|
||
|
If ``None``, similar to ``True`` the dataframe's index(es)
|
||
|
will be saved. However, instead of being saved as values,
|
||
|
the RangeIndex will be stored as a range in the metadata so it
|
||
|
doesn't require much space and is faster. Other indexes will
|
||
|
be included as columns in the file output.
|
||
|
partition_cols : str or list, optional, default None
|
||
|
Column names by which to partition the dataset.
|
||
|
Columns are partitioned in the order they are given.
|
||
|
Must be None if path is not a string.
|
||
|
{storage_options}
|
||
|
|
||
|
.. versionadded:: 1.2.0
|
||
|
|
||
|
kwargs
|
||
|
Additional keyword arguments passed to the engine
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
bytes if no path argument is provided else None
|
||
|
"""
|
||
|
if isinstance(partition_cols, str):
|
||
|
partition_cols = [partition_cols]
|
||
|
impl = get_engine(engine)
|
||
|
|
||
|
path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path is None else path
|
||
|
|
||
|
impl.write(
|
||
|
df,
|
||
|
path_or_buf,
|
||
|
compression=compression,
|
||
|
index=index,
|
||
|
partition_cols=partition_cols,
|
||
|
storage_options=storage_options,
|
||
|
**kwargs,
|
||
|
)
|
||
|
|
||
|
if path is None:
|
||
|
assert isinstance(path_or_buf, io.BytesIO)
|
||
|
return path_or_buf.getvalue()
|
||
|
else:
|
||
|
return None
|
||
|
|
||
|
|
||
|
@doc(storage_options=_shared_docs["storage_options"])
|
||
|
def read_parquet(
|
||
|
path,
|
||
|
engine: str = "auto",
|
||
|
columns=None,
|
||
|
storage_options: StorageOptions = None,
|
||
|
use_nullable_dtypes: bool = False,
|
||
|
**kwargs,
|
||
|
) -> DataFrame:
|
||
|
"""
|
||
|
Load a parquet object from the file path, returning a DataFrame.
|
||
|
|
||
|
Parameters
|
||
|
----------
|
||
|
path : str, path object or file-like object
|
||
|
String, path object (implementing ``os.PathLike[str]``), or file-like
|
||
|
object implementing a binary ``read()`` function.
|
||
|
The string could be a URL. Valid URL schemes include http, ftp, s3,
|
||
|
gs, and file. For file URLs, a host is expected. A local file could be:
|
||
|
``file://localhost/path/to/table.parquet``.
|
||
|
A file URL can also be a path to a directory that contains multiple
|
||
|
partitioned parquet files. Both pyarrow and fastparquet support
|
||
|
paths to directories as well as file URLs. A directory path could be:
|
||
|
``file://localhost/path/to/tables`` or ``s3://bucket/partition_dir``.
|
||
|
engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
|
||
|
Parquet library to use. If 'auto', then the option
|
||
|
``io.parquet.engine`` is used. The default ``io.parquet.engine``
|
||
|
behavior is to try 'pyarrow', falling back to 'fastparquet' if
|
||
|
'pyarrow' is unavailable.
|
||
|
columns : list, default=None
|
||
|
If not None, only these columns will be read from the file.
|
||
|
|
||
|
{storage_options}
|
||
|
|
||
|
.. versionadded:: 1.3.0
|
||
|
|
||
|
use_nullable_dtypes : bool, default False
|
||
|
If True, use dtypes that use ``pd.NA`` as missing value indicator
|
||
|
for the resulting DataFrame. (only applicable for the ``pyarrow``
|
||
|
engine)
|
||
|
As new dtypes are added that support ``pd.NA`` in the future, the
|
||
|
output with this option will change to use those dtypes.
|
||
|
Note: this is an experimental option, and behaviour (e.g. additional
|
||
|
support dtypes) may change without notice.
|
||
|
|
||
|
.. versionadded:: 1.2.0
|
||
|
|
||
|
**kwargs
|
||
|
Any additional kwargs are passed to the engine.
|
||
|
|
||
|
Returns
|
||
|
-------
|
||
|
DataFrame
|
||
|
"""
|
||
|
impl = get_engine(engine)
|
||
|
|
||
|
return impl.read(
|
||
|
path,
|
||
|
columns=columns,
|
||
|
storage_options=storage_options,
|
||
|
use_nullable_dtypes=use_nullable_dtypes,
|
||
|
**kwargs,
|
||
|
)
|