653 lines
20 KiB
Python
653 lines
20 KiB
Python
"""
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manage legacy pickle tests
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How to add pickle tests:
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1. Install pandas version intended to output the pickle.
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2. Execute "generate_legacy_storage_files.py" to create the pickle.
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$ python generate_legacy_storage_files.py <output_dir> pickle
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3. Move the created pickle to "data/legacy_pickle/<version>" directory.
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"""
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from __future__ import annotations
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from array import array
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import bz2
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import datetime
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import functools
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from functools import partial
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import gzip
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import io
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import os
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from pathlib import Path
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import pickle
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import shutil
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import tarfile
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from typing import Any
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import uuid
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import zipfile
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import numpy as np
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import pytest
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from pandas.compat import (
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get_lzma_file,
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is_platform_little_endian,
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)
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from pandas.compat._optional import import_optional_dependency
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from pandas.compat.compressors import flatten_buffer
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import (
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DataFrame,
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Index,
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Series,
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period_range,
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)
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import pandas._testing as tm
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from pandas.tests.io.generate_legacy_storage_files import create_pickle_data
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import pandas.io.common as icom
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from pandas.tseries.offsets import (
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Day,
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MonthEnd,
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)
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# ---------------------
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# comparison functions
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# ---------------------
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def compare_element(result, expected, typ):
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if isinstance(expected, Index):
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tm.assert_index_equal(expected, result)
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return
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if typ.startswith("sp_"):
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tm.assert_equal(result, expected)
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elif typ == "timestamp":
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if expected is pd.NaT:
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assert result is pd.NaT
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else:
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assert result == expected
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else:
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comparator = getattr(tm, f"assert_{typ}_equal", tm.assert_almost_equal)
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comparator(result, expected)
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# ---------------------
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# tests
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# ---------------------
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@pytest.mark.parametrize(
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"data",
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[
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b"123",
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b"123456",
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bytearray(b"123"),
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memoryview(b"123"),
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pickle.PickleBuffer(b"123"),
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array("I", [1, 2, 3]),
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memoryview(b"123456").cast("B", (3, 2)),
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memoryview(b"123456").cast("B", (3, 2))[::2],
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np.arange(12).reshape((3, 4), order="C"),
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np.arange(12).reshape((3, 4), order="F"),
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np.arange(12).reshape((3, 4), order="C")[:, ::2],
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],
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)
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def test_flatten_buffer(data):
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result = flatten_buffer(data)
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expected = memoryview(data).tobytes("A")
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assert result == expected
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if isinstance(data, (bytes, bytearray)):
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assert result is data
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elif isinstance(result, memoryview):
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assert result.ndim == 1
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assert result.format == "B"
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assert result.contiguous
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assert result.shape == (result.nbytes,)
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def test_pickles(datapath):
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if not is_platform_little_endian():
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pytest.skip("known failure on non-little endian")
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# For loop for compat with --strict-data-files
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for legacy_pickle in Path(__file__).parent.glob("data/legacy_pickle/*/*.p*kl*"):
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legacy_pickle = datapath(legacy_pickle)
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data = pd.read_pickle(legacy_pickle)
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for typ, dv in data.items():
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for dt, result in dv.items():
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expected = data[typ][dt]
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if typ == "series" and dt == "ts":
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# GH 7748
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tm.assert_series_equal(result, expected)
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assert result.index.freq == expected.index.freq
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assert not result.index.freq.normalize
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tm.assert_series_equal(result > 0, expected > 0)
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# GH 9291
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freq = result.index.freq
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assert freq + Day(1) == Day(2)
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res = freq + pd.Timedelta(hours=1)
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assert isinstance(res, pd.Timedelta)
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assert res == pd.Timedelta(days=1, hours=1)
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res = freq + pd.Timedelta(nanoseconds=1)
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assert isinstance(res, pd.Timedelta)
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assert res == pd.Timedelta(days=1, nanoseconds=1)
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elif typ == "index" and dt == "period":
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tm.assert_index_equal(result, expected)
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assert isinstance(result.freq, MonthEnd)
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assert result.freq == MonthEnd()
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assert result.freqstr == "M"
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tm.assert_index_equal(result.shift(2), expected.shift(2))
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elif typ == "series" and dt in ("dt_tz", "cat"):
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tm.assert_series_equal(result, expected)
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elif typ == "frame" and dt in (
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"dt_mixed_tzs",
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"cat_onecol",
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"cat_and_float",
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):
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tm.assert_frame_equal(result, expected)
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else:
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compare_element(result, expected, typ)
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def python_pickler(obj, path):
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with open(path, "wb") as fh:
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pickle.dump(obj, fh, protocol=-1)
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def python_unpickler(path):
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with open(path, "rb") as fh:
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fh.seek(0)
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return pickle.load(fh)
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def flatten(data: dict) -> list[tuple[str, Any]]:
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"""Flatten create_pickle_data"""
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return [
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(typ, example)
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for typ, examples in data.items()
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for example in examples.values()
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]
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@pytest.mark.parametrize(
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"pickle_writer",
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[
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pytest.param(python_pickler, id="python"),
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pytest.param(pd.to_pickle, id="pandas_proto_default"),
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pytest.param(
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functools.partial(pd.to_pickle, protocol=pickle.HIGHEST_PROTOCOL),
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id="pandas_proto_highest",
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),
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pytest.param(functools.partial(pd.to_pickle, protocol=4), id="pandas_proto_4"),
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pytest.param(
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functools.partial(pd.to_pickle, protocol=5),
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id="pandas_proto_5",
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),
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],
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)
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@pytest.mark.parametrize("writer", [pd.to_pickle, python_pickler])
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@pytest.mark.parametrize("typ, expected", flatten(create_pickle_data()))
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def test_round_trip_current(typ, expected, pickle_writer, writer):
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with tm.ensure_clean() as path:
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# test writing with each pickler
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pickle_writer(expected, path)
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# test reading with each unpickler
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result = pd.read_pickle(path)
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compare_element(result, expected, typ)
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result = python_unpickler(path)
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compare_element(result, expected, typ)
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# and the same for file objects (GH 35679)
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with open(path, mode="wb") as handle:
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writer(expected, path)
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handle.seek(0) # shouldn't close file handle
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with open(path, mode="rb") as handle:
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result = pd.read_pickle(handle)
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handle.seek(0) # shouldn't close file handle
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compare_element(result, expected, typ)
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def test_pickle_path_pathlib():
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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result = tm.round_trip_pathlib(df.to_pickle, pd.read_pickle)
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tm.assert_frame_equal(df, result)
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def test_pickle_path_localpath():
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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result = tm.round_trip_localpath(df.to_pickle, pd.read_pickle)
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tm.assert_frame_equal(df, result)
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# ---------------------
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# test pickle compression
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# ---------------------
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@pytest.fixture
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def get_random_path():
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return f"__{uuid.uuid4()}__.pickle"
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class TestCompression:
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_extension_to_compression = icom.extension_to_compression
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def compress_file(self, src_path, dest_path, compression):
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if compression is None:
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shutil.copyfile(src_path, dest_path)
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return
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if compression == "gzip":
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f = gzip.open(dest_path, "w")
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elif compression == "bz2":
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f = bz2.BZ2File(dest_path, "w")
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elif compression == "zip":
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with zipfile.ZipFile(dest_path, "w", compression=zipfile.ZIP_DEFLATED) as f:
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f.write(src_path, os.path.basename(src_path))
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elif compression == "tar":
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with open(src_path, "rb") as fh:
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with tarfile.open(dest_path, mode="w") as tar:
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tarinfo = tar.gettarinfo(src_path, os.path.basename(src_path))
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tar.addfile(tarinfo, fh)
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elif compression == "xz":
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f = get_lzma_file()(dest_path, "w")
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elif compression == "zstd":
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f = import_optional_dependency("zstandard").open(dest_path, "wb")
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else:
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msg = f"Unrecognized compression type: {compression}"
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raise ValueError(msg)
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if compression not in ["zip", "tar"]:
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with open(src_path, "rb") as fh:
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with f:
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f.write(fh.read())
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def test_write_explicit(self, compression, get_random_path):
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base = get_random_path
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path1 = base + ".compressed"
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path2 = base + ".raw"
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with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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# write to compressed file
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df.to_pickle(p1, compression=compression)
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# decompress
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with tm.decompress_file(p1, compression=compression) as f:
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with open(p2, "wb") as fh:
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fh.write(f.read())
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# read decompressed file
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df2 = pd.read_pickle(p2, compression=None)
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tm.assert_frame_equal(df, df2)
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@pytest.mark.parametrize("compression", ["", "None", "bad", "7z"])
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def test_write_explicit_bad(self, compression, get_random_path):
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with pytest.raises(ValueError, match="Unrecognized compression type"):
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with tm.ensure_clean(get_random_path) as path:
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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df.to_pickle(path, compression=compression)
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def test_write_infer(self, compression_ext, get_random_path):
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base = get_random_path
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path1 = base + compression_ext
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path2 = base + ".raw"
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compression = self._extension_to_compression.get(compression_ext.lower())
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with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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# write to compressed file by inferred compression method
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df.to_pickle(p1)
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# decompress
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with tm.decompress_file(p1, compression=compression) as f:
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with open(p2, "wb") as fh:
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fh.write(f.read())
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# read decompressed file
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df2 = pd.read_pickle(p2, compression=None)
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tm.assert_frame_equal(df, df2)
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def test_read_explicit(self, compression, get_random_path):
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base = get_random_path
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path1 = base + ".raw"
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path2 = base + ".compressed"
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with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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# write to uncompressed file
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df.to_pickle(p1, compression=None)
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# compress
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self.compress_file(p1, p2, compression=compression)
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# read compressed file
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df2 = pd.read_pickle(p2, compression=compression)
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tm.assert_frame_equal(df, df2)
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def test_read_infer(self, compression_ext, get_random_path):
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base = get_random_path
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path1 = base + ".raw"
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path2 = base + compression_ext
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compression = self._extension_to_compression.get(compression_ext.lower())
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with tm.ensure_clean(path1) as p1, tm.ensure_clean(path2) as p2:
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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# write to uncompressed file
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df.to_pickle(p1, compression=None)
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# compress
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self.compress_file(p1, p2, compression=compression)
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# read compressed file by inferred compression method
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df2 = pd.read_pickle(p2)
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tm.assert_frame_equal(df, df2)
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# ---------------------
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# test pickle compression
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# ---------------------
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class TestProtocol:
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@pytest.mark.parametrize("protocol", [-1, 0, 1, 2])
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def test_read(self, protocol, get_random_path):
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with tm.ensure_clean(get_random_path) as path:
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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df.to_pickle(path, protocol=protocol)
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df2 = pd.read_pickle(path)
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tm.assert_frame_equal(df, df2)
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@pytest.mark.parametrize(
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["pickle_file", "excols"],
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[
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("test_py27.pkl", Index(["a", "b", "c"])),
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(
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"test_mi_py27.pkl",
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pd.MultiIndex.from_arrays([["a", "b", "c"], ["A", "B", "C"]]),
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),
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],
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)
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def test_unicode_decode_error(datapath, pickle_file, excols):
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# pickle file written with py27, should be readable without raising
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# UnicodeDecodeError, see GH#28645 and GH#31988
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path = datapath("io", "data", "pickle", pickle_file)
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df = pd.read_pickle(path)
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# just test the columns are correct since the values are random
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tm.assert_index_equal(df.columns, excols)
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# ---------------------
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# tests for buffer I/O
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# ---------------------
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def test_pickle_buffer_roundtrip():
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with tm.ensure_clean() as path:
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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with open(path, "wb") as fh:
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df.to_pickle(fh)
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with open(path, "rb") as fh:
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result = pd.read_pickle(fh)
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tm.assert_frame_equal(df, result)
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# ---------------------
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# tests for URL I/O
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# ---------------------
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@pytest.mark.parametrize(
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"mockurl", ["http://url.com", "ftp://test.com", "http://gzip.com"]
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)
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def test_pickle_generalurl_read(monkeypatch, mockurl):
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def python_pickler(obj, path):
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with open(path, "wb") as fh:
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pickle.dump(obj, fh, protocol=-1)
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class MockReadResponse:
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def __init__(self, path) -> None:
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self.file = open(path, "rb")
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if "gzip" in path:
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self.headers = {"Content-Encoding": "gzip"}
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else:
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self.headers = {"Content-Encoding": ""}
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def __enter__(self):
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return self
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def __exit__(self, *args):
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self.close()
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def read(self):
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return self.file.read()
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def close(self):
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return self.file.close()
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with tm.ensure_clean() as path:
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def mock_urlopen_read(*args, **kwargs):
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return MockReadResponse(path)
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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python_pickler(df, path)
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monkeypatch.setattr("urllib.request.urlopen", mock_urlopen_read)
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result = pd.read_pickle(mockurl)
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tm.assert_frame_equal(df, result)
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def test_pickle_fsspec_roundtrip():
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pytest.importorskip("fsspec")
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with tm.ensure_clean():
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mockurl = "memory://mockfile"
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df = DataFrame(
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1.1 * np.arange(120).reshape((30, 4)),
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columns=Index(list("ABCD"), dtype=object),
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index=Index([f"i-{i}" for i in range(30)], dtype=object),
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)
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df.to_pickle(mockurl)
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|
result = pd.read_pickle(mockurl)
|
|
tm.assert_frame_equal(df, result)
|
|
|
|
|
|
class MyTz(datetime.tzinfo):
|
|
def __init__(self) -> None:
|
|
pass
|
|
|
|
|
|
def test_read_pickle_with_subclass():
|
|
# GH 12163
|
|
expected = Series(dtype=object), MyTz()
|
|
result = tm.round_trip_pickle(expected)
|
|
|
|
tm.assert_series_equal(result[0], expected[0])
|
|
assert isinstance(result[1], MyTz)
|
|
|
|
|
|
def test_pickle_binary_object_compression(compression):
|
|
"""
|
|
Read/write from binary file-objects w/wo compression.
|
|
|
|
GH 26237, GH 29054, and GH 29570
|
|
"""
|
|
df = DataFrame(
|
|
1.1 * np.arange(120).reshape((30, 4)),
|
|
columns=Index(list("ABCD"), dtype=object),
|
|
index=Index([f"i-{i}" for i in range(30)], dtype=object),
|
|
)
|
|
|
|
# reference for compression
|
|
with tm.ensure_clean() as path:
|
|
df.to_pickle(path, compression=compression)
|
|
reference = Path(path).read_bytes()
|
|
|
|
# write
|
|
buffer = io.BytesIO()
|
|
df.to_pickle(buffer, compression=compression)
|
|
buffer.seek(0)
|
|
|
|
# gzip and zip safe the filename: cannot compare the compressed content
|
|
assert buffer.getvalue() == reference or compression in ("gzip", "zip", "tar")
|
|
|
|
# read
|
|
read_df = pd.read_pickle(buffer, compression=compression)
|
|
buffer.seek(0)
|
|
tm.assert_frame_equal(df, read_df)
|
|
|
|
|
|
def test_pickle_dataframe_with_multilevel_index(
|
|
multiindex_year_month_day_dataframe_random_data,
|
|
multiindex_dataframe_random_data,
|
|
):
|
|
ymd = multiindex_year_month_day_dataframe_random_data
|
|
frame = multiindex_dataframe_random_data
|
|
|
|
def _test_roundtrip(frame):
|
|
unpickled = tm.round_trip_pickle(frame)
|
|
tm.assert_frame_equal(frame, unpickled)
|
|
|
|
_test_roundtrip(frame)
|
|
_test_roundtrip(frame.T)
|
|
_test_roundtrip(ymd)
|
|
_test_roundtrip(ymd.T)
|
|
|
|
|
|
def test_pickle_timeseries_periodindex():
|
|
# GH#2891
|
|
prng = period_range("1/1/2011", "1/1/2012", freq="M")
|
|
ts = Series(np.random.default_rng(2).standard_normal(len(prng)), prng)
|
|
new_ts = tm.round_trip_pickle(ts)
|
|
assert new_ts.index.freqstr == "M"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"name", [777, 777.0, "name", datetime.datetime(2001, 11, 11), (1, 2)]
|
|
)
|
|
def test_pickle_preserve_name(name):
|
|
unpickled = tm.round_trip_pickle(Series(np.arange(10, dtype=np.float64), name=name))
|
|
assert unpickled.name == name
|
|
|
|
|
|
def test_pickle_datetimes(datetime_series):
|
|
unp_ts = tm.round_trip_pickle(datetime_series)
|
|
tm.assert_series_equal(unp_ts, datetime_series)
|
|
|
|
|
|
def test_pickle_strings(string_series):
|
|
unp_series = tm.round_trip_pickle(string_series)
|
|
tm.assert_series_equal(unp_series, string_series)
|
|
|
|
|
|
@td.skip_array_manager_invalid_test
|
|
def test_pickle_preserves_block_ndim():
|
|
# GH#37631
|
|
ser = Series(list("abc")).astype("category").iloc[[0]]
|
|
res = tm.round_trip_pickle(ser)
|
|
|
|
assert res._mgr.blocks[0].ndim == 1
|
|
assert res._mgr.blocks[0].shape == (1,)
|
|
|
|
# GH#37631 OP issue was about indexing, underlying problem was pickle
|
|
tm.assert_series_equal(res[[True]], ser)
|
|
|
|
|
|
@pytest.mark.parametrize("protocol", [pickle.DEFAULT_PROTOCOL, pickle.HIGHEST_PROTOCOL])
|
|
def test_pickle_big_dataframe_compression(protocol, compression):
|
|
# GH#39002
|
|
df = DataFrame(range(100000))
|
|
result = tm.round_trip_pathlib(
|
|
partial(df.to_pickle, protocol=protocol, compression=compression),
|
|
partial(pd.read_pickle, compression=compression),
|
|
)
|
|
tm.assert_frame_equal(df, result)
|
|
|
|
|
|
def test_pickle_frame_v124_unpickle_130(datapath):
|
|
# GH#42345 DataFrame created in 1.2.x, unpickle in 1.3.x
|
|
path = datapath(
|
|
Path(__file__).parent,
|
|
"data",
|
|
"legacy_pickle",
|
|
"1.2.4",
|
|
"empty_frame_v1_2_4-GH#42345.pkl",
|
|
)
|
|
with open(path, "rb") as fd:
|
|
df = pickle.load(fd)
|
|
|
|
expected = DataFrame(index=[], columns=[])
|
|
tm.assert_frame_equal(df, expected)
|
|
|
|
|
|
def test_pickle_pos_args_deprecation():
|
|
# GH-54229
|
|
df = DataFrame({"a": [1, 2, 3]})
|
|
msg = (
|
|
r"Starting with pandas version 3.0 all arguments of to_pickle except for the "
|
|
r"argument 'path' will be keyword-only."
|
|
)
|
|
with tm.assert_produces_warning(FutureWarning, match=msg):
|
|
buffer = io.BytesIO()
|
|
df.to_pickle(buffer, "infer")
|