677 lines
23 KiB
Python
677 lines
23 KiB
Python
""" parquet compat """
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from __future__ import annotations
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import io
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import json
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import os
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from typing import (
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TYPE_CHECKING,
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Any,
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Literal,
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)
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import warnings
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from warnings import catch_warnings
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from pandas._config import using_pyarrow_string_dtype
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from pandas._config.config import _get_option
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from pandas._libs import lib
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from pandas.compat._optional import import_optional_dependency
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from pandas.errors import AbstractMethodError
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from pandas.util._decorators import doc
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from pandas.util._exceptions import find_stack_level
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from pandas.util._validators import check_dtype_backend
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import pandas as pd
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from pandas import (
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DataFrame,
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get_option,
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)
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from pandas.core.shared_docs import _shared_docs
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from pandas.io._util import arrow_string_types_mapper
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from pandas.io.common import (
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IOHandles,
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get_handle,
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is_fsspec_url,
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is_url,
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stringify_path,
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)
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if TYPE_CHECKING:
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from pandas._typing import (
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DtypeBackend,
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FilePath,
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ReadBuffer,
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StorageOptions,
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WriteBuffer,
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)
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def get_engine(engine: str) -> BaseImpl:
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"""return our implementation"""
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if engine == "auto":
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engine = get_option("io.parquet.engine")
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if engine == "auto":
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# try engines in this order
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engine_classes = [PyArrowImpl, FastParquetImpl]
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error_msgs = ""
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for engine_class in engine_classes:
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try:
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return engine_class()
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except ImportError as err:
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error_msgs += "\n - " + str(err)
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raise ImportError(
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"Unable to find a usable engine; "
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"tried using: 'pyarrow', 'fastparquet'.\n"
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"A suitable version of "
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"pyarrow or fastparquet is required for parquet "
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"support.\n"
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"Trying to import the above resulted in these errors:"
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f"{error_msgs}"
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)
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if engine == "pyarrow":
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return PyArrowImpl()
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elif engine == "fastparquet":
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return FastParquetImpl()
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raise ValueError("engine must be one of 'pyarrow', 'fastparquet'")
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def _get_path_or_handle(
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path: FilePath | ReadBuffer[bytes] | WriteBuffer[bytes],
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fs: Any,
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storage_options: StorageOptions | None = None,
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mode: str = "rb",
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is_dir: bool = False,
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) -> tuple[
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FilePath | ReadBuffer[bytes] | WriteBuffer[bytes], IOHandles[bytes] | None, Any
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]:
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"""File handling for PyArrow."""
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path_or_handle = stringify_path(path)
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if fs is not None:
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pa_fs = import_optional_dependency("pyarrow.fs", errors="ignore")
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fsspec = import_optional_dependency("fsspec", errors="ignore")
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if pa_fs is not None and isinstance(fs, pa_fs.FileSystem):
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if storage_options:
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raise NotImplementedError(
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"storage_options not supported with a pyarrow FileSystem."
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)
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elif fsspec is not None and isinstance(fs, fsspec.spec.AbstractFileSystem):
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pass
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else:
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raise ValueError(
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f"filesystem must be a pyarrow or fsspec FileSystem, "
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f"not a {type(fs).__name__}"
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)
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if is_fsspec_url(path_or_handle) and fs is None:
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if storage_options is None:
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pa = import_optional_dependency("pyarrow")
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pa_fs = import_optional_dependency("pyarrow.fs")
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try:
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fs, path_or_handle = pa_fs.FileSystem.from_uri(path)
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except (TypeError, pa.ArrowInvalid):
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pass
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if fs is None:
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fsspec = import_optional_dependency("fsspec")
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fs, path_or_handle = fsspec.core.url_to_fs(
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path_or_handle, **(storage_options or {})
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)
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elif storage_options and (not is_url(path_or_handle) or mode != "rb"):
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# can't write to a remote url
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# without making use of fsspec at the moment
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raise ValueError("storage_options passed with buffer, or non-supported URL")
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handles = None
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if (
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not fs
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and not is_dir
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and isinstance(path_or_handle, str)
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and not os.path.isdir(path_or_handle)
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):
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# use get_handle only when we are very certain that it is not a directory
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# fsspec resources can also point to directories
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# this branch is used for example when reading from non-fsspec URLs
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handles = get_handle(
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path_or_handle, mode, is_text=False, storage_options=storage_options
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)
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fs = None
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path_or_handle = handles.handle
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return path_or_handle, handles, fs
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class BaseImpl:
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@staticmethod
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def validate_dataframe(df: DataFrame) -> None:
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if not isinstance(df, DataFrame):
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raise ValueError("to_parquet only supports IO with DataFrames")
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def write(self, df: DataFrame, path, compression, **kwargs):
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raise AbstractMethodError(self)
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def read(self, path, columns=None, **kwargs) -> DataFrame:
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raise AbstractMethodError(self)
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class PyArrowImpl(BaseImpl):
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def __init__(self) -> None:
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import_optional_dependency(
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"pyarrow", extra="pyarrow is required for parquet support."
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)
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import pyarrow.parquet
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# import utils to register the pyarrow extension types
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import pandas.core.arrays.arrow.extension_types # pyright: ignore[reportUnusedImport] # noqa: F401
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self.api = pyarrow
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def write(
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self,
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df: DataFrame,
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path: FilePath | WriteBuffer[bytes],
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compression: str | None = "snappy",
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index: bool | None = None,
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storage_options: StorageOptions | None = None,
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partition_cols: list[str] | None = None,
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filesystem=None,
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**kwargs,
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) -> None:
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self.validate_dataframe(df)
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from_pandas_kwargs: dict[str, Any] = {"schema": kwargs.pop("schema", None)}
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if index is not None:
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from_pandas_kwargs["preserve_index"] = index
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table = self.api.Table.from_pandas(df, **from_pandas_kwargs)
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if df.attrs:
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df_metadata = {"PANDAS_ATTRS": json.dumps(df.attrs)}
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existing_metadata = table.schema.metadata
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merged_metadata = {**existing_metadata, **df_metadata}
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table = table.replace_schema_metadata(merged_metadata)
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path_or_handle, handles, filesystem = _get_path_or_handle(
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path,
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filesystem,
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storage_options=storage_options,
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mode="wb",
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is_dir=partition_cols is not None,
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)
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if (
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isinstance(path_or_handle, io.BufferedWriter)
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and hasattr(path_or_handle, "name")
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and isinstance(path_or_handle.name, (str, bytes))
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):
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if isinstance(path_or_handle.name, bytes):
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path_or_handle = path_or_handle.name.decode()
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else:
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path_or_handle = path_or_handle.name
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try:
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if partition_cols is not None:
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# writes to multiple files under the given path
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self.api.parquet.write_to_dataset(
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table,
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path_or_handle,
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compression=compression,
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partition_cols=partition_cols,
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filesystem=filesystem,
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**kwargs,
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)
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else:
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# write to single output file
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self.api.parquet.write_table(
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table,
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path_or_handle,
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compression=compression,
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filesystem=filesystem,
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**kwargs,
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)
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finally:
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if handles is not None:
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handles.close()
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def read(
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self,
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path,
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columns=None,
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filters=None,
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use_nullable_dtypes: bool = False,
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dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
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storage_options: StorageOptions | None = None,
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filesystem=None,
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**kwargs,
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) -> DataFrame:
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kwargs["use_pandas_metadata"] = True
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to_pandas_kwargs = {}
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if dtype_backend == "numpy_nullable":
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from pandas.io._util import _arrow_dtype_mapping
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mapping = _arrow_dtype_mapping()
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to_pandas_kwargs["types_mapper"] = mapping.get
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elif dtype_backend == "pyarrow":
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to_pandas_kwargs["types_mapper"] = pd.ArrowDtype # type: ignore[assignment]
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elif using_pyarrow_string_dtype():
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to_pandas_kwargs["types_mapper"] = arrow_string_types_mapper()
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manager = _get_option("mode.data_manager", silent=True)
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if manager == "array":
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to_pandas_kwargs["split_blocks"] = True # type: ignore[assignment]
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path_or_handle, handles, filesystem = _get_path_or_handle(
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path,
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filesystem,
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storage_options=storage_options,
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mode="rb",
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)
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try:
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pa_table = self.api.parquet.read_table(
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path_or_handle,
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columns=columns,
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filesystem=filesystem,
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filters=filters,
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**kwargs,
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)
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result = pa_table.to_pandas(**to_pandas_kwargs)
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if manager == "array":
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result = result._as_manager("array", copy=False)
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if pa_table.schema.metadata:
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if b"PANDAS_ATTRS" in pa_table.schema.metadata:
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df_metadata = pa_table.schema.metadata[b"PANDAS_ATTRS"]
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result.attrs = json.loads(df_metadata)
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return result
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finally:
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if handles is not None:
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handles.close()
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class FastParquetImpl(BaseImpl):
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def __init__(self) -> None:
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# since pandas is a dependency of fastparquet
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# we need to import on first use
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fastparquet = import_optional_dependency(
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"fastparquet", extra="fastparquet is required for parquet support."
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)
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self.api = fastparquet
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def write(
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self,
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df: DataFrame,
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path,
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compression: Literal["snappy", "gzip", "brotli"] | None = "snappy",
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index=None,
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partition_cols=None,
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storage_options: StorageOptions | None = None,
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filesystem=None,
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**kwargs,
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) -> None:
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self.validate_dataframe(df)
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if "partition_on" in kwargs and partition_cols is not None:
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raise ValueError(
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"Cannot use both partition_on and "
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"partition_cols. Use partition_cols for partitioning data"
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)
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if "partition_on" in kwargs:
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partition_cols = kwargs.pop("partition_on")
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if partition_cols is not None:
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kwargs["file_scheme"] = "hive"
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if filesystem is not None:
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raise NotImplementedError(
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"filesystem is not implemented for the fastparquet engine."
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)
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# cannot use get_handle as write() does not accept file buffers
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path = stringify_path(path)
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if is_fsspec_url(path):
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fsspec = import_optional_dependency("fsspec")
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# if filesystem is provided by fsspec, file must be opened in 'wb' mode.
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kwargs["open_with"] = lambda path, _: fsspec.open(
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path, "wb", **(storage_options or {})
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).open()
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elif storage_options:
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raise ValueError(
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"storage_options passed with file object or non-fsspec file path"
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)
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with catch_warnings(record=True):
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self.api.write(
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path,
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df,
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compression=compression,
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write_index=index,
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partition_on=partition_cols,
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**kwargs,
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)
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def read(
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self,
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path,
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columns=None,
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filters=None,
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storage_options: StorageOptions | None = None,
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filesystem=None,
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**kwargs,
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) -> DataFrame:
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parquet_kwargs: dict[str, Any] = {}
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use_nullable_dtypes = kwargs.pop("use_nullable_dtypes", False)
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dtype_backend = kwargs.pop("dtype_backend", lib.no_default)
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# We are disabling nullable dtypes for fastparquet pending discussion
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parquet_kwargs["pandas_nulls"] = False
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if use_nullable_dtypes:
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raise ValueError(
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"The 'use_nullable_dtypes' argument is not supported for the "
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"fastparquet engine"
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)
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if dtype_backend is not lib.no_default:
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raise ValueError(
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"The 'dtype_backend' argument is not supported for the "
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"fastparquet engine"
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)
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if filesystem is not None:
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raise NotImplementedError(
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"filesystem is not implemented for the fastparquet engine."
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)
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path = stringify_path(path)
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handles = None
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if is_fsspec_url(path):
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fsspec = import_optional_dependency("fsspec")
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parquet_kwargs["fs"] = fsspec.open(path, "rb", **(storage_options or {})).fs
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elif isinstance(path, str) and not os.path.isdir(path):
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# use get_handle only when we are very certain that it is not a directory
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# fsspec resources can also point to directories
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# this branch is used for example when reading from non-fsspec URLs
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handles = get_handle(
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path, "rb", is_text=False, storage_options=storage_options
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)
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path = handles.handle
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try:
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parquet_file = self.api.ParquetFile(path, **parquet_kwargs)
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return parquet_file.to_pandas(columns=columns, filters=filters, **kwargs)
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finally:
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if handles is not None:
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handles.close()
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@doc(storage_options=_shared_docs["storage_options"])
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def to_parquet(
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df: DataFrame,
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path: FilePath | WriteBuffer[bytes] | None = None,
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engine: str = "auto",
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compression: str | None = "snappy",
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index: bool | None = None,
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storage_options: StorageOptions | None = None,
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partition_cols: list[str] | None = None,
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filesystem: Any = None,
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**kwargs,
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) -> bytes | None:
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"""
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Write a DataFrame to the parquet format.
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Parameters
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----------
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df : DataFrame
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path : str, path object, file-like object, or None, default None
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String, path object (implementing ``os.PathLike[str]``), or file-like
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object implementing a binary ``write()`` function. If None, the result is
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returned as bytes. If a string, it will be used as Root Directory path
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when writing a partitioned dataset. The engine fastparquet does not
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accept file-like objects.
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engine : {{'auto', 'pyarrow', 'fastparquet'}}, default 'auto'
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Parquet library to use. If 'auto', then the option
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``io.parquet.engine`` is used. The default ``io.parquet.engine``
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behavior is to try 'pyarrow', falling back to 'fastparquet' if
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'pyarrow' is unavailable.
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When using the ``'pyarrow'`` engine and no storage options are provided
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and a filesystem is implemented by both ``pyarrow.fs`` and ``fsspec``
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(e.g. "s3://"), then the ``pyarrow.fs`` filesystem is attempted first.
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Use the filesystem keyword with an instantiated fsspec filesystem
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if you wish to use its implementation.
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compression : {{'snappy', 'gzip', 'brotli', 'lz4', 'zstd', None}},
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default 'snappy'. Name of the compression to use. Use ``None``
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for no compression.
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index : bool, default None
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If ``True``, include the dataframe's index(es) in the file output. If
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``False``, they will not be written to the file.
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If ``None``, similar to ``True`` the dataframe's index(es)
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will be saved. However, instead of being saved as values,
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the RangeIndex will be stored as a range in the metadata so it
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doesn't require much space and is faster. Other indexes will
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be included as columns in the file output.
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partition_cols : str or list, optional, default None
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Column names by which to partition the dataset.
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Columns are partitioned in the order they are given.
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Must be None if path is not a string.
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{storage_options}
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filesystem : fsspec or pyarrow filesystem, default None
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Filesystem object to use when reading the parquet file. Only implemented
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for ``engine="pyarrow"``.
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.. versionadded:: 2.1.0
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kwargs
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Additional keyword arguments passed to the engine
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Returns
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-------
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bytes if no path argument is provided else None
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"""
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if isinstance(partition_cols, str):
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partition_cols = [partition_cols]
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impl = get_engine(engine)
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path_or_buf: FilePath | WriteBuffer[bytes] = io.BytesIO() if path is None else path
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|
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impl.write(
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df,
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path_or_buf,
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compression=compression,
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index=index,
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partition_cols=partition_cols,
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storage_options=storage_options,
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filesystem=filesystem,
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**kwargs,
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)
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if path is None:
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assert isinstance(path_or_buf, io.BytesIO)
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return path_or_buf.getvalue()
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else:
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return None
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@doc(storage_options=_shared_docs["storage_options"])
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def read_parquet(
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path: FilePath | ReadBuffer[bytes],
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engine: str = "auto",
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columns: list[str] | None = None,
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storage_options: StorageOptions | None = None,
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use_nullable_dtypes: bool | lib.NoDefault = lib.no_default,
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dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
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filesystem: Any = None,
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filters: list[tuple] | list[list[tuple]] | None = None,
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**kwargs,
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) -> DataFrame:
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"""
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Load a parquet object from the file path, returning a DataFrame.
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Parameters
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----------
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path : str, path object or file-like object
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String, path object (implementing ``os.PathLike[str]``), or file-like
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object implementing a binary ``read()`` function.
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The string could be a URL. Valid URL schemes include http, ftp, s3,
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gs, and file. For file URLs, a host is expected. A local file could be:
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``file://localhost/path/to/table.parquet``.
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A file URL can also be a path to a directory that contains multiple
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partitioned parquet files. Both pyarrow and fastparquet support
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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,
|
|
)
|