from __future__ import annotations import gzip import io import pathlib import tarfile from typing import ( TYPE_CHECKING, Any, Callable, ) import uuid import zipfile from pandas.compat import ( get_bz2_file, get_lzma_file, ) from pandas.compat._optional import import_optional_dependency import pandas as pd from pandas._testing.contexts import ensure_clean if TYPE_CHECKING: from pandas._typing import ( FilePath, ReadPickleBuffer, ) from pandas import ( DataFrame, Series, ) # ------------------------------------------------------------------ # File-IO def round_trip_pickle( obj: Any, path: FilePath | ReadPickleBuffer | None = None ) -> DataFrame | Series: """ Pickle an object and then read it again. Parameters ---------- obj : any object The object to pickle and then re-read. path : str, path object or file-like object, default None The path where the pickled object is written and then read. Returns ------- pandas object The original object that was pickled and then re-read. """ _path = path if _path is None: _path = f"__{uuid.uuid4()}__.pickle" with ensure_clean(_path) as temp_path: pd.to_pickle(obj, temp_path) return pd.read_pickle(temp_path) def round_trip_pathlib(writer, reader, path: str | None = None): """ Write an object to file specified by a pathlib.Path and read it back Parameters ---------- writer : callable bound to pandas object IO writing function (e.g. DataFrame.to_csv ) reader : callable IO reading function (e.g. pd.read_csv ) path : str, default None The path where the object is written and then read. Returns ------- pandas object The original object that was serialized and then re-read. """ Path = pathlib.Path if path is None: path = "___pathlib___" with ensure_clean(path) as path: writer(Path(path)) # type: ignore[arg-type] obj = reader(Path(path)) # type: ignore[arg-type] return obj def round_trip_localpath(writer, reader, path: str | None = None): """ Write an object to file specified by a py.path LocalPath and read it back. Parameters ---------- writer : callable bound to pandas object IO writing function (e.g. DataFrame.to_csv ) reader : callable IO reading function (e.g. pd.read_csv ) path : str, default None The path where the object is written and then read. Returns ------- pandas object The original object that was serialized and then re-read. """ import pytest LocalPath = pytest.importorskip("py.path").local if path is None: path = "___localpath___" with ensure_clean(path) as path: writer(LocalPath(path)) obj = reader(LocalPath(path)) return obj def write_to_compressed(compression, path, data, dest: str = "test"): """ Write data to a compressed file. Parameters ---------- compression : {'gzip', 'bz2', 'zip', 'xz', 'zstd'} The compression type to use. path : str The file path to write the data. data : str The data to write. dest : str, default "test" The destination file (for ZIP only) Raises ------ ValueError : An invalid compression value was passed in. """ args: tuple[Any, ...] = (data,) mode = "wb" method = "write" compress_method: Callable if compression == "zip": compress_method = zipfile.ZipFile mode = "w" args = (dest, data) method = "writestr" elif compression == "tar": compress_method = tarfile.TarFile mode = "w" file = tarfile.TarInfo(name=dest) bytes = io.BytesIO(data) file.size = len(data) args = (file, bytes) method = "addfile" elif compression == "gzip": compress_method = gzip.GzipFile elif compression == "bz2": compress_method = get_bz2_file() elif compression == "zstd": compress_method = import_optional_dependency("zstandard").open elif compression == "xz": compress_method = get_lzma_file() else: raise ValueError(f"Unrecognized compression type: {compression}") with compress_method(path, mode=mode) as f: getattr(f, method)(*args)