You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
365 lines
12 KiB
365 lines
12 KiB
import gzip
|
|
import io
|
|
import os
|
|
from pathlib import Path
|
|
import subprocess
|
|
import sys
|
|
import tarfile
|
|
import textwrap
|
|
import time
|
|
import zipfile
|
|
|
|
import pytest
|
|
|
|
from pandas.compat import is_platform_windows
|
|
|
|
import pandas as pd
|
|
import pandas._testing as tm
|
|
|
|
import pandas.io.common as icom
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
pd.DataFrame(
|
|
100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
|
|
columns=["X", "Y", "Z"],
|
|
),
|
|
pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("method", ["to_pickle", "to_json", "to_csv"])
|
|
def test_compression_size(obj, method, compression_only):
|
|
if compression_only == "tar":
|
|
compression_only = {"method": "tar", "mode": "w:gz"}
|
|
|
|
with tm.ensure_clean() as path:
|
|
getattr(obj, method)(path, compression=compression_only)
|
|
compressed_size = os.path.getsize(path)
|
|
getattr(obj, method)(path, compression=None)
|
|
uncompressed_size = os.path.getsize(path)
|
|
assert uncompressed_size > compressed_size
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
pd.DataFrame(
|
|
100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
|
|
columns=["X", "Y", "Z"],
|
|
),
|
|
pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("method", ["to_csv", "to_json"])
|
|
def test_compression_size_fh(obj, method, compression_only):
|
|
with tm.ensure_clean() as path:
|
|
with icom.get_handle(
|
|
path,
|
|
"w:gz" if compression_only == "tar" else "w",
|
|
compression=compression_only,
|
|
) as handles:
|
|
getattr(obj, method)(handles.handle)
|
|
assert not handles.handle.closed
|
|
compressed_size = os.path.getsize(path)
|
|
with tm.ensure_clean() as path:
|
|
with icom.get_handle(path, "w", compression=None) as handles:
|
|
getattr(obj, method)(handles.handle)
|
|
assert not handles.handle.closed
|
|
uncompressed_size = os.path.getsize(path)
|
|
assert uncompressed_size > compressed_size
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"write_method, write_kwargs, read_method",
|
|
[
|
|
("to_csv", {"index": False}, pd.read_csv),
|
|
("to_json", {}, pd.read_json),
|
|
("to_pickle", {}, pd.read_pickle),
|
|
],
|
|
)
|
|
def test_dataframe_compression_defaults_to_infer(
|
|
write_method, write_kwargs, read_method, compression_only, compression_to_extension
|
|
):
|
|
# GH22004
|
|
input = pd.DataFrame([[1.0, 0, -4], [3.4, 5, 2]], columns=["X", "Y", "Z"])
|
|
extension = compression_to_extension[compression_only]
|
|
with tm.ensure_clean("compressed" + extension) as path:
|
|
getattr(input, write_method)(path, **write_kwargs)
|
|
output = read_method(path, compression=compression_only)
|
|
tm.assert_frame_equal(output, input)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"write_method,write_kwargs,read_method,read_kwargs",
|
|
[
|
|
("to_csv", {"index": False, "header": True}, pd.read_csv, {"squeeze": True}),
|
|
("to_json", {}, pd.read_json, {"typ": "series"}),
|
|
("to_pickle", {}, pd.read_pickle, {}),
|
|
],
|
|
)
|
|
def test_series_compression_defaults_to_infer(
|
|
write_method,
|
|
write_kwargs,
|
|
read_method,
|
|
read_kwargs,
|
|
compression_only,
|
|
compression_to_extension,
|
|
):
|
|
# GH22004
|
|
input = pd.Series([0, 5, -2, 10], name="X")
|
|
extension = compression_to_extension[compression_only]
|
|
with tm.ensure_clean("compressed" + extension) as path:
|
|
getattr(input, write_method)(path, **write_kwargs)
|
|
if "squeeze" in read_kwargs:
|
|
kwargs = read_kwargs.copy()
|
|
del kwargs["squeeze"]
|
|
output = read_method(path, compression=compression_only, **kwargs).squeeze(
|
|
"columns"
|
|
)
|
|
else:
|
|
output = read_method(path, compression=compression_only, **read_kwargs)
|
|
tm.assert_series_equal(output, input, check_names=False)
|
|
|
|
|
|
def test_compression_warning(compression_only):
|
|
# Assert that passing a file object to to_csv while explicitly specifying a
|
|
# compression protocol triggers a RuntimeWarning, as per GH21227.
|
|
df = pd.DataFrame(
|
|
100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
|
|
columns=["X", "Y", "Z"],
|
|
)
|
|
with tm.ensure_clean() as path:
|
|
with icom.get_handle(path, "w", compression=compression_only) as handles:
|
|
with tm.assert_produces_warning(RuntimeWarning):
|
|
df.to_csv(handles.handle, compression=compression_only)
|
|
|
|
|
|
def test_compression_binary(compression_only):
|
|
"""
|
|
Binary file handles support compression.
|
|
|
|
GH22555
|
|
"""
|
|
df = tm.makeDataFrame()
|
|
|
|
# with a file
|
|
with tm.ensure_clean() as path:
|
|
with open(path, mode="wb") as file:
|
|
df.to_csv(file, mode="wb", compression=compression_only)
|
|
file.seek(0) # file shouldn't be closed
|
|
tm.assert_frame_equal(
|
|
df, pd.read_csv(path, index_col=0, compression=compression_only)
|
|
)
|
|
|
|
# with BytesIO
|
|
file = io.BytesIO()
|
|
df.to_csv(file, mode="wb", compression=compression_only)
|
|
file.seek(0) # file shouldn't be closed
|
|
tm.assert_frame_equal(
|
|
df, pd.read_csv(file, index_col=0, compression=compression_only)
|
|
)
|
|
|
|
|
|
def test_gzip_reproducibility_file_name():
|
|
"""
|
|
Gzip should create reproducible archives with mtime.
|
|
|
|
Note: Archives created with different filenames will still be different!
|
|
|
|
GH 28103
|
|
"""
|
|
df = tm.makeDataFrame()
|
|
compression_options = {"method": "gzip", "mtime": 1}
|
|
|
|
# test for filename
|
|
with tm.ensure_clean() as path:
|
|
path = Path(path)
|
|
df.to_csv(path, compression=compression_options)
|
|
time.sleep(0.1)
|
|
output = path.read_bytes()
|
|
df.to_csv(path, compression=compression_options)
|
|
assert output == path.read_bytes()
|
|
|
|
|
|
def test_gzip_reproducibility_file_object():
|
|
"""
|
|
Gzip should create reproducible archives with mtime.
|
|
|
|
GH 28103
|
|
"""
|
|
df = tm.makeDataFrame()
|
|
compression_options = {"method": "gzip", "mtime": 1}
|
|
|
|
# test for file object
|
|
buffer = io.BytesIO()
|
|
df.to_csv(buffer, compression=compression_options, mode="wb")
|
|
output = buffer.getvalue()
|
|
time.sleep(0.1)
|
|
buffer = io.BytesIO()
|
|
df.to_csv(buffer, compression=compression_options, mode="wb")
|
|
assert output == buffer.getvalue()
|
|
|
|
|
|
@pytest.mark.single_cpu
|
|
def test_with_missing_lzma():
|
|
"""Tests if import pandas works when lzma is not present."""
|
|
# https://github.com/pandas-dev/pandas/issues/27575
|
|
code = textwrap.dedent(
|
|
"""\
|
|
import sys
|
|
sys.modules['lzma'] = None
|
|
import pandas
|
|
"""
|
|
)
|
|
subprocess.check_output([sys.executable, "-c", code], stderr=subprocess.PIPE)
|
|
|
|
|
|
@pytest.mark.single_cpu
|
|
def test_with_missing_lzma_runtime():
|
|
"""Tests if RuntimeError is hit when calling lzma without
|
|
having the module available.
|
|
"""
|
|
code = textwrap.dedent(
|
|
"""
|
|
import sys
|
|
import pytest
|
|
sys.modules['lzma'] = None
|
|
import pandas as pd
|
|
df = pd.DataFrame()
|
|
with pytest.raises(RuntimeError, match='lzma module'):
|
|
df.to_csv('foo.csv', compression='xz')
|
|
"""
|
|
)
|
|
subprocess.check_output([sys.executable, "-c", code], stderr=subprocess.PIPE)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
pd.DataFrame(
|
|
100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
|
|
columns=["X", "Y", "Z"],
|
|
),
|
|
pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("method", ["to_pickle", "to_json", "to_csv"])
|
|
def test_gzip_compression_level(obj, method):
|
|
# GH33196
|
|
with tm.ensure_clean() as path:
|
|
getattr(obj, method)(path, compression="gzip")
|
|
compressed_size_default = os.path.getsize(path)
|
|
getattr(obj, method)(path, compression={"method": "gzip", "compresslevel": 1})
|
|
compressed_size_fast = os.path.getsize(path)
|
|
assert compressed_size_default < compressed_size_fast
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
pd.DataFrame(
|
|
100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
|
|
columns=["X", "Y", "Z"],
|
|
),
|
|
pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("method", ["to_pickle", "to_json", "to_csv"])
|
|
def test_xz_compression_level_read(obj, method):
|
|
with tm.ensure_clean() as path:
|
|
getattr(obj, method)(path, compression="xz")
|
|
compressed_size_default = os.path.getsize(path)
|
|
getattr(obj, method)(path, compression={"method": "xz", "preset": 1})
|
|
compressed_size_fast = os.path.getsize(path)
|
|
assert compressed_size_default < compressed_size_fast
|
|
if method == "to_csv":
|
|
pd.read_csv(path, compression="xz")
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"obj",
|
|
[
|
|
pd.DataFrame(
|
|
100 * [[0.123456, 0.234567, 0.567567], [12.32112, 123123.2, 321321.2]],
|
|
columns=["X", "Y", "Z"],
|
|
),
|
|
pd.Series(100 * [0.123456, 0.234567, 0.567567], name="X"),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("method", ["to_pickle", "to_json", "to_csv"])
|
|
def test_bzip_compression_level(obj, method):
|
|
"""GH33196 bzip needs file size > 100k to show a size difference between
|
|
compression levels, so here we just check if the call works when
|
|
compression is passed as a dict.
|
|
"""
|
|
with tm.ensure_clean() as path:
|
|
getattr(obj, method)(path, compression={"method": "bz2", "compresslevel": 1})
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"suffix,archive",
|
|
[
|
|
(".zip", zipfile.ZipFile),
|
|
(".tar", tarfile.TarFile),
|
|
],
|
|
)
|
|
def test_empty_archive_zip(suffix, archive):
|
|
with tm.ensure_clean(filename=suffix) as path:
|
|
with archive(path, "w"):
|
|
pass
|
|
with pytest.raises(ValueError, match="Zero files found"):
|
|
pd.read_csv(path)
|
|
|
|
|
|
def test_ambiguous_archive_zip():
|
|
with tm.ensure_clean(filename=".zip") as path:
|
|
with zipfile.ZipFile(path, "w") as file:
|
|
file.writestr("a.csv", "foo,bar")
|
|
file.writestr("b.csv", "foo,bar")
|
|
with pytest.raises(ValueError, match="Multiple files found in ZIP file"):
|
|
pd.read_csv(path)
|
|
|
|
|
|
def test_ambiguous_archive_tar(tmp_path):
|
|
csvAPath = tmp_path / "a.csv"
|
|
with open(csvAPath, "w", encoding="utf-8") as a:
|
|
a.write("foo,bar\n")
|
|
csvBPath = tmp_path / "b.csv"
|
|
with open(csvBPath, "w", encoding="utf-8") as b:
|
|
b.write("foo,bar\n")
|
|
|
|
tarpath = tmp_path / "archive.tar"
|
|
with tarfile.TarFile(tarpath, "w") as tar:
|
|
tar.add(csvAPath, "a.csv")
|
|
tar.add(csvBPath, "b.csv")
|
|
|
|
with pytest.raises(ValueError, match="Multiple files found in TAR archive"):
|
|
pd.read_csv(tarpath)
|
|
|
|
|
|
def test_tar_gz_to_different_filename():
|
|
with tm.ensure_clean(filename=".foo") as file:
|
|
pd.DataFrame(
|
|
[["1", "2"]],
|
|
columns=["foo", "bar"],
|
|
).to_csv(file, compression={"method": "tar", "mode": "w:gz"}, index=False)
|
|
with gzip.open(file) as uncompressed:
|
|
with tarfile.TarFile(fileobj=uncompressed) as archive:
|
|
members = archive.getmembers()
|
|
assert len(members) == 1
|
|
content = archive.extractfile(members[0]).read().decode("utf8")
|
|
|
|
if is_platform_windows():
|
|
expected = "foo,bar\r\n1,2\r\n"
|
|
else:
|
|
expected = "foo,bar\n1,2\n"
|
|
|
|
assert content == expected
|
|
|
|
|
|
def test_tar_no_error_on_close():
|
|
with io.BytesIO() as buffer:
|
|
with icom._BytesTarFile(fileobj=buffer, mode="w"):
|
|
pass
|
|
|