677 lines
23 KiB
Python
677 lines
23 KiB
Python
|
""" parquet compat """
|
||
|
|
from __future__ import annotations
|
||
|
|
|
||
|
|
import io
|
||
|
|
import json
|
||
|
|
import os
|
||
|
|
from typing import (
|
||
|
|
TYPE_CHECKING,
|
||
|
|
Any,
|
||
|
|
Literal,
|
||
|
|
)
|
||
|
|
import warnings
|
||
|
|
from warnings import catch_warnings
|
||
|
|
|
||
|
|
from pandas._config import using_pyarrow_string_dtype
|
||
|
|
from pandas._config.config import _get_option
|
||
|
|
|
||
|
|
from pandas._libs import lib
|
||
|
|
from pandas.compat._optional import import_optional_dependency
|
||
|
|
from pandas.errors import AbstractMethodError
|
||
|
|
from pandas.util._decorators import doc
|
||
|
|
from pandas.util._exceptions import find_stack_level
|
||
|
|
from pandas.util._validators import check_dtype_backend
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas import (
|
||
|
|
DataFrame,
|
||
|
|
get_option,
|
||
|
|
)
|
||
|
|
from pandas.core.shared_docs import _shared_docs
|
||
|
|
|
||
|
|
from pandas.io._util import arrow_string_types_mapper
|
||
|
|
from pandas.io.common import (
|
||
|
|
IOHandles,
|
||
|
|
get_handle,
|
||
|
|
is_fsspec_url,
|
||
|
|
is_url,
|
||
|
|
stringify_path,
|
||
|
|
)
|
||
|
|
|
||
|
|
if TYPE_CHECKING:
|
||
|
|
from pandas._typing import (
|
||
|
|
DtypeBackend,
|
||
|
|
FilePath,
|
||
|
|
ReadBuffer,
|
||
|
|
StorageOptions,
|
||
|
|
WriteBuffer,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
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 = 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 fs is not None:
|
||
|
|
pa_fs = import_optional_dependency("pyarrow.fs", errors="ignore")
|
||
|
|
fsspec = import_optional_dependency("fsspec", errors="ignore")
|
||
|
|
if pa_fs is not None and isinstance(fs, pa_fs.FileSystem):
|
||
|
|
if storage_options:
|
||
|
|
raise NotImplementedError(
|
||
|
|
"storage_options not supported with a pyarrow FileSystem."
|
||
|
|
)
|
||
|
|
elif fsspec is not None and isinstance(fs, fsspec.spec.AbstractFileSystem):
|
||
|
|
pass
|
||
|
|
else:
|
||
|
|
raise ValueError(
|
||
|
|
f"filesystem must be a pyarrow or fsspec FileSystem, "
|
||
|
|
f"not a {type(fs).__name__}"
|
||
|
|
)
|
||
|
|
if is_fsspec_url(path_or_handle) and fs is None:
|
||
|
|
if storage_options is None:
|
||
|
|
pa = import_optional_dependency("pyarrow")
|
||
|
|
pa_fs = import_optional_dependency("pyarrow.fs")
|
||
|
|
|
||
|
|
try:
|
||
|
|
fs, path_or_handle = pa_fs.FileSystem.from_uri(path)
|
||
|
|
except (TypeError, pa.ArrowInvalid):
|
||
|
|
pass
|
||
|
|
if 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")
|
||
|
|
|
||
|
|
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) -> None:
|
||
|
|
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.extension_types # pyright: ignore[reportUnusedImport] # 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 = None,
|
||
|
|
partition_cols: list[str] | None = None,
|
||
|
|
filesystem=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)
|
||
|
|
|
||
|
|
if df.attrs:
|
||
|
|
df_metadata = {"PANDAS_ATTRS": json.dumps(df.attrs)}
|
||
|
|
existing_metadata = table.schema.metadata
|
||
|
|
merged_metadata = {**existing_metadata, **df_metadata}
|
||
|
|
table = table.replace_schema_metadata(merged_metadata)
|
||
|
|
|
||
|
|
path_or_handle, handles, filesystem = _get_path_or_handle(
|
||
|
|
path,
|
||
|
|
filesystem,
|
||
|
|
storage_options=storage_options,
|
||
|
|
mode="wb",
|
||
|
|
is_dir=partition_cols is not None,
|
||
|
|
)
|
||
|
|
if (
|
||
|
|
isinstance(path_or_handle, io.BufferedWriter)
|
||
|
|
and hasattr(path_or_handle, "name")
|
||
|
|
and isinstance(path_or_handle.name, (str, bytes))
|
||
|
|
):
|
||
|
|
if isinstance(path_or_handle.name, bytes):
|
||
|
|
path_or_handle = path_or_handle.name.decode()
|
||
|
|
else:
|
||
|
|
path_or_handle = path_or_handle.name
|
||
|
|
|
||
|
|
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,
|
||
|
|
filesystem=filesystem,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
# write to single output file
|
||
|
|
self.api.parquet.write_table(
|
||
|
|
table,
|
||
|
|
path_or_handle,
|
||
|
|
compression=compression,
|
||
|
|
filesystem=filesystem,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
finally:
|
||
|
|
if handles is not None:
|
||
|
|
handles.close()
|
||
|
|
|
||
|
|
def read(
|
||
|
|
self,
|
||
|
|
path,
|
||
|
|
columns=None,
|
||
|
|
filters=None,
|
||
|
|
use_nullable_dtypes: bool = False,
|
||
|
|
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
|
||
|
|
storage_options: StorageOptions | None = None,
|
||
|
|
filesystem=None,
|
||
|
|
**kwargs,
|
||
|
|
) -> DataFrame:
|
||
|
|
kwargs["use_pandas_metadata"] = True
|
||
|
|
|
||
|
|
to_pandas_kwargs = {}
|
||
|
|
if dtype_backend == "numpy_nullable":
|
||
|
|
from pandas.io._util import _arrow_dtype_mapping
|
||
|
|
|
||
|
|
mapping = _arrow_dtype_mapping()
|
||
|
|
to_pandas_kwargs["types_mapper"] = mapping.get
|
||
|
|
elif dtype_backend == "pyarrow":
|
||
|
|
to_pandas_kwargs["types_mapper"] = pd.ArrowDtype # type: ignore[assignment]
|
||
|
|
elif using_pyarrow_string_dtype():
|
||
|
|
to_pandas_kwargs["types_mapper"] = arrow_string_types_mapper()
|
||
|
|
|
||
|
|
manager = _get_option("mode.data_manager", silent=True)
|
||
|
|
if manager == "array":
|
||
|
|
to_pandas_kwargs["split_blocks"] = True # type: ignore[assignment]
|
||
|
|
|
||
|
|
path_or_handle, handles, filesystem = _get_path_or_handle(
|
||
|
|
path,
|
||
|
|
filesystem,
|
||
|
|
storage_options=storage_options,
|
||
|
|
mode="rb",
|
||
|
|
)
|
||
|
|
try:
|
||
|
|
pa_table = self.api.parquet.read_table(
|
||
|
|
path_or_handle,
|
||
|
|
columns=columns,
|
||
|
|
filesystem=filesystem,
|
||
|
|
filters=filters,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
result = pa_table.to_pandas(**to_pandas_kwargs)
|
||
|
|
|
||
|
|
if manager == "array":
|
||
|
|
result = result._as_manager("array", copy=False)
|
||
|
|
|
||
|
|
if pa_table.schema.metadata:
|
||
|
|
if b"PANDAS_ATTRS" in pa_table.schema.metadata:
|
||
|
|
df_metadata = pa_table.schema.metadata[b"PANDAS_ATTRS"]
|
||
|
|
result.attrs = json.loads(df_metadata)
|
||
|
|
return result
|
||
|
|
finally:
|
||
|
|
if handles is not None:
|
||
|
|
handles.close()
|
||
|
|
|
||
|
|
|
||
|
|
class FastParquetImpl(BaseImpl):
|
||
|
|
def __init__(self) -> None:
|
||
|
|
# 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: Literal["snappy", "gzip", "brotli"] | None = "snappy",
|
||
|
|
index=None,
|
||
|
|
partition_cols=None,
|
||
|
|
storage_options: StorageOptions | None = None,
|
||
|
|
filesystem=None,
|
||
|
|
**kwargs,
|
||
|
|
) -> None:
|
||
|
|
self.validate_dataframe(df)
|
||
|
|
|
||
|
|
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"
|
||
|
|
)
|
||
|
|
if "partition_on" in kwargs:
|
||
|
|
partition_cols = kwargs.pop("partition_on")
|
||
|
|
|
||
|
|
if partition_cols is not None:
|
||
|
|
kwargs["file_scheme"] = "hive"
|
||
|
|
|
||
|
|
if filesystem is not None:
|
||
|
|
raise NotImplementedError(
|
||
|
|
"filesystem is not implemented for the fastparquet engine."
|
||
|
|
)
|
||
|
|
|
||
|
|
# 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,
|
||
|
|
filters=None,
|
||
|
|
storage_options: StorageOptions | None = None,
|
||
|
|
filesystem=None,
|
||
|
|
**kwargs,
|
||
|
|
) -> DataFrame:
|
||
|
|
parquet_kwargs: dict[str, Any] = {}
|
||
|
|
use_nullable_dtypes = kwargs.pop("use_nullable_dtypes", False)
|
||
|
|
dtype_backend = kwargs.pop("dtype_backend", lib.no_default)
|
||
|
|
# 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"
|
||
|
|
)
|
||
|
|
if dtype_backend is not lib.no_default:
|
||
|
|
raise ValueError(
|
||
|
|
"The 'dtype_backend' argument is not supported for the "
|
||
|
|
"fastparquet engine"
|
||
|
|
)
|
||
|
|
if filesystem is not None:
|
||
|
|
raise NotImplementedError(
|
||
|
|
"filesystem is not implemented for the fastparquet engine."
|
||
|
|
)
|
||
|
|
path = stringify_path(path)
|
||
|
|
handles = None
|
||
|
|
if is_fsspec_url(path):
|
||
|
|
fsspec = import_optional_dependency("fsspec")
|
||
|
|
|
||
|
|
parquet_kwargs["fs"] = fsspec.open(path, "rb", **(storage_options or {})).fs
|
||
|
|
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
|
||
|
|
|
||
|
|
try:
|
||
|
|
parquet_file = self.api.ParquetFile(path, **parquet_kwargs)
|
||
|
|
return parquet_file.to_pandas(columns=columns, filters=filters, **kwargs)
|
||
|
|
finally:
|
||
|
|
if handles is not None:
|
||
|
|
handles.close()
|
||
|
|
|
||
|
|
|
||
|
|
@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 = None,
|
||
|
|
partition_cols: list[str] | None = None,
|
||
|
|
filesystem: Any = 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.
|
||
|
|
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.
|
||
|
|
|
||
|
|
When using the ``'pyarrow'`` engine and no storage options are provided
|
||
|
|
and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
|
||
|
|
(e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
|
||
|
|
Use the filesystem keyword with an instantiated fsspec filesystem
|
||
|
|
if you wish to use its implementation.
|
||
|
|
compression : {{'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}},
|
||
|
|
default 'snappy'. Name of the compression to use. Use ``None``
|
||
|
|
for no compression.
|
||
|
|
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}
|
||
|
|
|
||
|
|
filesystem : fsspec or pyarrow filesystem, default None
|
||
|
|
Filesystem object to use when reading the parquet file. Only implemented
|
||
|
|
for ``engine="pyarrow"``.
|
||
|
|
|
||
|
|
.. versionadded:: 2.1.0
|
||
|
|
|
||
|
|
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,
|
||
|
|
filesystem=filesystem,
|
||
|
|
**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: FilePath | ReadBuffer[bytes],
|
||
|
|
engine: str = "auto",
|
||
|
|
columns: list[str] | None = None,
|
||
|
|
storage_options: StorageOptions | None = None,
|
||
|
|
use_nullable_dtypes: bool | lib.NoDefault = lib.no_default,
|
||
|
|
dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
|
||
|
|
filesystem: Any = None,
|
||
|
|
filters: list[tuple] | list[list[tuple]] | None = None,
|
||
|
|
**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.
|
||
|
|
|
||
|
|
When using the ``'pyarrow'`` engine and no storage options are provided
|
||
|
|
and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
|
||
|
|
(e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
|
||
|
|
Use the filesystem keyword with an instantiated fsspec filesystem
|
||
|
|
if you wish to use its implementation.
|
||
|
|
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.
|
||
|
|
|
||
|
|
.. deprecated:: 2.0
|
||
|
|
|
||
|
|
dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
|
||
|
|
Back-end data type applied to the resultant :class:`DataFrame`
|
||
|
|
(still experimental). Behaviour is as follows:
|
||
|
|
|
||
|
|
* ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
|
||
|
|
(default).
|
||
|
|
* ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
|
||
|
|
DataFrame.
|
||
|
|
|
||
|
|
.. versionadded:: 2.0
|
||
|
|
|
||
|
|
filesystem : fsspec or pyarrow filesystem, default None
|
||
|
|
Filesystem object to use when reading the parquet file. Only implemented
|
||
|
|
for ``engine="pyarrow"``.
|
||
|
|
|
||
|
|
.. versionadded:: 2.1.0
|
||
|
|
|
||
|
|
filters : List[Tuple] or List[List[Tuple]], default None
|
||
|
|
To filter out data.
|
||
|
|
Filter syntax: [[(column, op, val), ...],...]
|
||
|
|
where op is [==, =, >, >=, <, <=, !=, in, not in]
|
||
|
|
The innermost tuples are transposed into a set of filters applied
|
||
|
|
through an `AND` operation.
|
||
|
|
The outer list combines these sets of filters through an `OR`
|
||
|
|
operation.
|
||
|
|
A single list of tuples can also be used, meaning that no `OR`
|
||
|
|
operation between set of filters is to be conducted.
|
||
|
|
|
||
|
|
Using this argument will NOT result in row-wise filtering of the final
|
||
|
|
partitions unless ``engine="pyarrow"`` is also specified. For
|
||
|
|
other engines, filtering is only performed at the partition level, that is,
|
||
|
|
to prevent the loading of some row-groups and/or files.
|
||
|
|
|
||
|
|
.. versionadded:: 2.1.0
|
||
|
|
|
||
|
|
**kwargs
|
||
|
|
Any additional kwargs are passed to the engine.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
DataFrame
|
||
|
|
|
||
|
|
See Also
|
||
|
|
--------
|
||
|
|
DataFrame.to_parquet : Create a parquet object that serializes a DataFrame.
|
||
|
|
|
||
|
|
Examples
|
||
|
|
--------
|
||
|
|
>>> original_df = pd.DataFrame(
|
||
|
|
... {{"foo": range(5), "bar": range(5, 10)}}
|
||
|
|
... )
|
||
|
|
>>> original_df
|
||
|
|
foo bar
|
||
|
|
0 0 5
|
||
|
|
1 1 6
|
||
|
|
2 2 7
|
||
|
|
3 3 8
|
||
|
|
4 4 9
|
||
|
|
>>> df_parquet_bytes = original_df.to_parquet()
|
||
|
|
>>> from io import BytesIO
|
||
|
|
>>> restored_df = pd.read_parquet(BytesIO(df_parquet_bytes))
|
||
|
|
>>> restored_df
|
||
|
|
foo bar
|
||
|
|
0 0 5
|
||
|
|
1 1 6
|
||
|
|
2 2 7
|
||
|
|
3 3 8
|
||
|
|
4 4 9
|
||
|
|
>>> restored_df.equals(original_df)
|
||
|
|
True
|
||
|
|
>>> restored_bar = pd.read_parquet(BytesIO(df_parquet_bytes), columns=["bar"])
|
||
|
|
>>> restored_bar
|
||
|
|
bar
|
||
|
|
0 5
|
||
|
|
1 6
|
||
|
|
2 7
|
||
|
|
3 8
|
||
|
|
4 9
|
||
|
|
>>> restored_bar.equals(original_df[['bar']])
|
||
|
|
True
|
||
|
|
|
||
|
|
The function uses `kwargs` that are passed directly to the engine.
|
||
|
|
In the following example, we use the `filters` argument of the pyarrow
|
||
|
|
engine to filter the rows of the DataFrame.
|
||
|
|
|
||
|
|
Since `pyarrow` is the default engine, we can omit the `engine` argument.
|
||
|
|
Note that the `filters` argument is implemented by the `pyarrow` engine,
|
||
|
|
which can benefit from multithreading and also potentially be more
|
||
|
|
economical in terms of memory.
|
||
|
|
|
||
|
|
>>> sel = [("foo", ">", 2)]
|
||
|
|
>>> restored_part = pd.read_parquet(BytesIO(df_parquet_bytes), filters=sel)
|
||
|
|
>>> restored_part
|
||
|
|
foo bar
|
||
|
|
0 3 8
|
||
|
|
1 4 9
|
||
|
|
"""
|
||
|
|
|
||
|
|
impl = get_engine(engine)
|
||
|
|
|
||
|
|
if use_nullable_dtypes is not lib.no_default:
|
||
|
|
msg = (
|
||
|
|
"The argument 'use_nullable_dtypes' is deprecated and will be removed "
|
||
|
|
"in a future version."
|
||
|
|
)
|
||
|
|
if use_nullable_dtypes is True:
|
||
|
|
msg += (
|
||
|
|
"Use dtype_backend='numpy_nullable' instead of use_nullable_dtype=True."
|
||
|
|
)
|
||
|
|
warnings.warn(msg, FutureWarning, stacklevel=find_stack_level())
|
||
|
|
else:
|
||
|
|
use_nullable_dtypes = False
|
||
|
|
check_dtype_backend(dtype_backend)
|
||
|
|
|
||
|
|
return impl.read(
|
||
|
|
path,
|
||
|
|
columns=columns,
|
||
|
|
filters=filters,
|
||
|
|
storage_options=storage_options,
|
||
|
|
use_nullable_dtypes=use_nullable_dtypes,
|
||
|
|
dtype_backend=dtype_backend,
|
||
|
|
filesystem=filesystem,
|
||
|
|
**kwargs,
|
||
|
|
)
|