341 lines
11 KiB
Python
341 lines
11 KiB
Python
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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 subprocess
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import sys
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import tarfile
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import textwrap
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import time
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import zipfile
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import pytest
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from pandas.compat import is_platform_windows
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import pandas as pd
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import pandas._testing as tm
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import pandas.io.common as icom
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_compression_to_extension = {
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value: key for key, value in icom.extension_to_compression.items()
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}
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@pytest.mark.parametrize(
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"obj",
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[
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pd.DataFrame(
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100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
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columns=["X", "Y", "Z"],
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),
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pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
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],
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)
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@pytest.mark.parametrize("method", ["to_pickle", "to_json", "to_csv"])
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def test_compression_size(obj, method, compression_only):
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if compression_only == "tar":
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compression_only = {"method": "tar", "mode": "w:gz"}
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with tm.ensure_clean() as path:
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getattr(obj, method)(path, compression=compression_only)
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compressed_size = os.path.getsize(path)
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getattr(obj, method)(path, compression=None)
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uncompressed_size = os.path.getsize(path)
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assert uncompressed_size > compressed_size
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@pytest.mark.parametrize(
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"obj",
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[
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pd.DataFrame(
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100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
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columns=["X", "Y", "Z"],
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),
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pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
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],
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)
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@pytest.mark.parametrize("method", ["to_csv", "to_json"])
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def test_compression_size_fh(obj, method, compression_only):
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with tm.ensure_clean() as path:
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with icom.get_handle(
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path,
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"w:gz" if compression_only == "tar" else "w",
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compression=compression_only,
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) as handles:
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getattr(obj, method)(handles.handle)
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assert not handles.handle.closed
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compressed_size = os.path.getsize(path)
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with tm.ensure_clean() as path:
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with icom.get_handle(path, "w", compression=None) as handles:
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getattr(obj, method)(handles.handle)
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assert not handles.handle.closed
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uncompressed_size = os.path.getsize(path)
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assert uncompressed_size > compressed_size
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@pytest.mark.parametrize(
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"write_method, write_kwargs, read_method",
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[
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("to_csv", {"index": False}, pd.read_csv),
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("to_json", {}, pd.read_json),
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("to_pickle", {}, pd.read_pickle),
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],
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)
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def test_dataframe_compression_defaults_to_infer(
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write_method, write_kwargs, read_method, compression_only
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):
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# GH22004
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input = pd.DataFrame([[1.0, 0, -4], [3.4, 5, 2]], columns=["X", "Y", "Z"])
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extension = _compression_to_extension[compression_only]
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with tm.ensure_clean("compressed" + extension) as path:
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getattr(input, write_method)(path, **write_kwargs)
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output = read_method(path, compression=compression_only)
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tm.assert_frame_equal(output, input)
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@pytest.mark.parametrize(
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"write_method,write_kwargs,read_method,read_kwargs",
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[
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("to_csv", {"index": False, "header": True}, pd.read_csv, {"squeeze": True}),
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("to_json", {}, pd.read_json, {"typ": "series"}),
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("to_pickle", {}, pd.read_pickle, {}),
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],
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)
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def test_series_compression_defaults_to_infer(
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write_method, write_kwargs, read_method, read_kwargs, compression_only
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):
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# GH22004
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input = pd.Series([0, 5, -2, 10], name="X")
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extension = _compression_to_extension[compression_only]
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with tm.ensure_clean("compressed" + extension) as path:
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getattr(input, write_method)(path, **write_kwargs)
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if "squeeze" in read_kwargs:
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kwargs = read_kwargs.copy()
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del kwargs["squeeze"]
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output = read_method(path, compression=compression_only, **kwargs).squeeze(
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"columns"
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)
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else:
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output = read_method(path, compression=compression_only, **read_kwargs)
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tm.assert_series_equal(output, input, check_names=False)
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def test_compression_warning(compression_only):
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# Assert that passing a file object to to_csv while explicitly specifying a
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# compression protocol triggers a RuntimeWarning, as per GH21227.
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df = pd.DataFrame(
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100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
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columns=["X", "Y", "Z"],
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)
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with tm.ensure_clean() as path:
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with icom.get_handle(path, "w", compression=compression_only) as handles:
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with tm.assert_produces_warning(RuntimeWarning):
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df.to_csv(handles.handle, compression=compression_only)
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def test_compression_binary(compression_only):
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"""
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Binary file handles support compression.
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GH22555
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"""
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df = tm.makeDataFrame()
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# with a file
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with tm.ensure_clean() as path:
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with open(path, mode="wb") as file:
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df.to_csv(file, mode="wb", compression=compression_only)
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file.seek(0) # file shouldn't be closed
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tm.assert_frame_equal(
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df, pd.read_csv(path, index_col=0, compression=compression_only)
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)
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# with BytesIO
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file = io.BytesIO()
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df.to_csv(file, mode="wb", compression=compression_only)
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file.seek(0) # file shouldn't be closed
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tm.assert_frame_equal(
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df, pd.read_csv(file, index_col=0, compression=compression_only)
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)
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def test_gzip_reproducibility_file_name():
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"""
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Gzip should create reproducible archives with mtime.
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Note: Archives created with different filenames will still be different!
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GH 28103
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"""
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df = tm.makeDataFrame()
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compression_options = {"method": "gzip", "mtime": 1}
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# test for filename
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with tm.ensure_clean() as path:
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path = Path(path)
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df.to_csv(path, compression=compression_options)
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time.sleep(2)
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output = path.read_bytes()
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df.to_csv(path, compression=compression_options)
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assert output == path.read_bytes()
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def test_gzip_reproducibility_file_object():
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"""
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Gzip should create reproducible archives with mtime.
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GH 28103
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"""
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df = tm.makeDataFrame()
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compression_options = {"method": "gzip", "mtime": 1}
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# test for file object
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buffer = io.BytesIO()
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df.to_csv(buffer, compression=compression_options, mode="wb")
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output = buffer.getvalue()
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time.sleep(2)
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buffer = io.BytesIO()
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df.to_csv(buffer, compression=compression_options, mode="wb")
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assert output == buffer.getvalue()
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def test_with_missing_lzma():
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"""Tests if import pandas works when lzma is not present."""
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# https://github.com/pandas-dev/pandas/issues/27575
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code = textwrap.dedent(
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"""\
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import sys
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sys.modules['lzma'] = None
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import pandas
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"""
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)
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subprocess.check_output([sys.executable, "-c", code], stderr=subprocess.PIPE)
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def test_with_missing_lzma_runtime():
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"""Tests if RuntimeError is hit when calling lzma without
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having the module available.
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"""
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code = textwrap.dedent(
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"""
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import sys
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import pytest
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sys.modules['lzma'] = None
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import pandas as pd
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df = pd.DataFrame()
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with pytest.raises(RuntimeError, match='lzma module'):
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df.to_csv('foo.csv', compression='xz')
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"""
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)
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subprocess.check_output([sys.executable, "-c", code], stderr=subprocess.PIPE)
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@pytest.mark.parametrize(
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"obj",
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[
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pd.DataFrame(
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100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
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columns=["X", "Y", "Z"],
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),
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pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
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],
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)
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@pytest.mark.parametrize("method", ["to_pickle", "to_json", "to_csv"])
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def test_gzip_compression_level(obj, method):
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# GH33196
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with tm.ensure_clean() as path:
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getattr(obj, method)(path, compression="gzip")
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compressed_size_default = os.path.getsize(path)
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getattr(obj, method)(path, compression={"method": "gzip", "compresslevel": 1})
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compressed_size_fast = os.path.getsize(path)
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assert compressed_size_default < compressed_size_fast
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@pytest.mark.parametrize(
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"obj",
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[
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pd.DataFrame(
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100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
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columns=["X", "Y", "Z"],
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),
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pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
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],
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)
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@pytest.mark.parametrize("method", ["to_pickle", "to_json", "to_csv"])
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def test_bzip_compression_level(obj, method):
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"""GH33196 bzip needs file size > 100k to show a size difference between
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compression levels, so here we just check if the call works when
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compression is passed as a dict.
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"""
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with tm.ensure_clean() as path:
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getattr(obj, method)(path, compression={"method": "bz2", "compresslevel": 1})
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@pytest.mark.parametrize(
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"suffix,archive",
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[
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(".zip", zipfile.ZipFile),
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(".tar", tarfile.TarFile),
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],
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)
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def test_empty_archive_zip(suffix, archive):
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with tm.ensure_clean(filename=suffix) as path:
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with archive(path, "w"):
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pass
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with pytest.raises(ValueError, match="Zero files found"):
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pd.read_csv(path)
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def test_ambiguous_archive_zip():
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with tm.ensure_clean(filename=".zip") as path:
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with zipfile.ZipFile(path, "w") as file:
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file.writestr("a.csv", "foo,bar")
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file.writestr("b.csv", "foo,bar")
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with pytest.raises(ValueError, match="Multiple files found in ZIP file"):
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pd.read_csv(path)
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def test_ambiguous_archive_tar(tmp_path):
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csvAPath = tmp_path / "a.csv"
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with open(csvAPath, "w") as a:
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a.write("foo,bar\n")
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csvBPath = tmp_path / "b.csv"
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with open(csvBPath, "w") as b:
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b.write("foo,bar\n")
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tarpath = tmp_path / "archive.tar"
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with tarfile.TarFile(tarpath, "w") as tar:
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tar.add(csvAPath, "a.csv")
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tar.add(csvBPath, "b.csv")
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with pytest.raises(ValueError, match="Multiple files found in TAR archive"):
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pd.read_csv(tarpath)
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def test_tar_gz_to_different_filename():
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with tm.ensure_clean(filename=".foo") as file:
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pd.DataFrame(
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[["1", "2"]],
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columns=["foo", "bar"],
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).to_csv(file, compression={"method": "tar", "mode": "w:gz"}, index=False)
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with gzip.open(file) as uncompressed:
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with tarfile.TarFile(fileobj=uncompressed) as archive:
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members = archive.getmembers()
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assert len(members) == 1
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content = archive.extractfile(members[0]).read().decode("utf8")
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if is_platform_windows():
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expected = "foo,bar\r\n1,2\r\n"
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else:
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expected = "foo,bar\n1,2\n"
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assert content == expected
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def test_tar_no_error_on_close():
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with io.BytesIO() as buffer:
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with icom._BytesTarFile(fileobj=buffer, mode="w"):
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pass
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