Sindbad~EG File Manager
import io
import os
import sys
from zipfile import ZipFile
from _csv import Error
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
compat,
)
import pandas._testing as tm
class TestToCSV:
def test_to_csv_with_single_column(self):
# see gh-18676, https://bugs.python.org/issue32255
#
# Python's CSV library adds an extraneous '""'
# before the newline when the NaN-value is in
# the first row. Otherwise, only the newline
# character is added. This behavior is inconsistent
# and was patched in https://bugs.python.org/pull_request4672.
df1 = DataFrame([None, 1])
expected1 = """\
""
1.0
"""
with tm.ensure_clean("test.csv") as path:
df1.to_csv(path, header=None, index=None)
with open(path, encoding="utf-8") as f:
assert f.read() == expected1
df2 = DataFrame([1, None])
expected2 = """\
1.0
""
"""
with tm.ensure_clean("test.csv") as path:
df2.to_csv(path, header=None, index=None)
with open(path, encoding="utf-8") as f:
assert f.read() == expected2
def test_to_csv_default_encoding(self):
# GH17097
df = DataFrame({"col": ["AAAAA", "ÄÄÄÄÄ", "ßßßßß", "聞聞聞聞聞"]})
with tm.ensure_clean("test.csv") as path:
# the default to_csv encoding is uft-8.
df.to_csv(path)
tm.assert_frame_equal(pd.read_csv(path, index_col=0), df)
def test_to_csv_quotechar(self):
df = DataFrame({"col": [1, 2]})
expected = """\
"","col"
"0","1"
"1","2"
"""
with tm.ensure_clean("test.csv") as path:
df.to_csv(path, quoting=1) # 1=QUOTE_ALL
with open(path, encoding="utf-8") as f:
assert f.read() == expected
expected = """\
$$,$col$
$0$,$1$
$1$,$2$
"""
with tm.ensure_clean("test.csv") as path:
df.to_csv(path, quoting=1, quotechar="$")
with open(path, encoding="utf-8") as f:
assert f.read() == expected
with tm.ensure_clean("test.csv") as path:
with pytest.raises(TypeError, match="quotechar"):
df.to_csv(path, quoting=1, quotechar=None)
def test_to_csv_doublequote(self):
df = DataFrame({"col": ['a"a', '"bb"']})
expected = '''\
"","col"
"0","a""a"
"1","""bb"""
'''
with tm.ensure_clean("test.csv") as path:
df.to_csv(path, quoting=1, doublequote=True) # QUOTE_ALL
with open(path, encoding="utf-8") as f:
assert f.read() == expected
with tm.ensure_clean("test.csv") as path:
with pytest.raises(Error, match="escapechar"):
df.to_csv(path, doublequote=False) # no escapechar set
def test_to_csv_escapechar(self):
df = DataFrame({"col": ['a"a', '"bb"']})
expected = """\
"","col"
"0","a\\"a"
"1","\\"bb\\""
"""
with tm.ensure_clean("test.csv") as path: # QUOTE_ALL
df.to_csv(path, quoting=1, doublequote=False, escapechar="\\")
with open(path, encoding="utf-8") as f:
assert f.read() == expected
df = DataFrame({"col": ["a,a", ",bb,"]})
expected = """\
,col
0,a\\,a
1,\\,bb\\,
"""
with tm.ensure_clean("test.csv") as path:
df.to_csv(path, quoting=3, escapechar="\\") # QUOTE_NONE
with open(path, encoding="utf-8") as f:
assert f.read() == expected
def test_csv_to_string(self):
df = DataFrame({"col": [1, 2]})
expected_rows = [",col", "0,1", "1,2"]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.to_csv() == expected
def test_to_csv_decimal(self):
# see gh-781
df = DataFrame({"col1": [1], "col2": ["a"], "col3": [10.1]})
expected_rows = [",col1,col2,col3", "0,1,a,10.1"]
expected_default = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.to_csv() == expected_default
expected_rows = [";col1;col2;col3", "0;1;a;10,1"]
expected_european_excel = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.to_csv(decimal=",", sep=";") == expected_european_excel
expected_rows = [",col1,col2,col3", "0,1,a,10.10"]
expected_float_format_default = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.to_csv(float_format="%.2f") == expected_float_format_default
expected_rows = [";col1;col2;col3", "0;1;a;10,10"]
expected_float_format = tm.convert_rows_list_to_csv_str(expected_rows)
assert (
df.to_csv(decimal=",", sep=";", float_format="%.2f")
== expected_float_format
)
# see gh-11553: testing if decimal is taken into account for '0.0'
df = DataFrame({"a": [0, 1.1], "b": [2.2, 3.3], "c": 1})
expected_rows = ["a,b,c", "0^0,2^2,1", "1^1,3^3,1"]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.to_csv(index=False, decimal="^") == expected
# same but for an index
assert df.set_index("a").to_csv(decimal="^") == expected
# same for a multi-index
assert df.set_index(["a", "b"]).to_csv(decimal="^") == expected
def test_to_csv_float_format(self):
# testing if float_format is taken into account for the index
# GH 11553
df = DataFrame({"a": [0, 1], "b": [2.2, 3.3], "c": 1})
expected_rows = ["a,b,c", "0,2.20,1", "1,3.30,1"]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.set_index("a").to_csv(float_format="%.2f") == expected
# same for a multi-index
assert df.set_index(["a", "b"]).to_csv(float_format="%.2f") == expected
def test_to_csv_na_rep(self):
# see gh-11553
#
# Testing if NaN values are correctly represented in the index.
df = DataFrame({"a": [0, np.nan], "b": [0, 1], "c": [2, 3]})
expected_rows = ["a,b,c", "0.0,0,2", "_,1,3"]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.set_index("a").to_csv(na_rep="_") == expected
assert df.set_index(["a", "b"]).to_csv(na_rep="_") == expected
# now with an index containing only NaNs
df = DataFrame({"a": np.nan, "b": [0, 1], "c": [2, 3]})
expected_rows = ["a,b,c", "_,0,2", "_,1,3"]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.set_index("a").to_csv(na_rep="_") == expected
assert df.set_index(["a", "b"]).to_csv(na_rep="_") == expected
# check if na_rep parameter does not break anything when no NaN
df = DataFrame({"a": 0, "b": [0, 1], "c": [2, 3]})
expected_rows = ["a,b,c", "0,0,2", "0,1,3"]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.set_index("a").to_csv(na_rep="_") == expected
assert df.set_index(["a", "b"]).to_csv(na_rep="_") == expected
csv = pd.Series(["a", pd.NA, "c"]).to_csv(na_rep="ZZZZZ")
expected = tm.convert_rows_list_to_csv_str([",0", "0,a", "1,ZZZZZ", "2,c"])
assert expected == csv
def test_to_csv_na_rep_nullable_string(self, nullable_string_dtype):
# GH 29975
# Make sure full na_rep shows up when a dtype is provided
expected = tm.convert_rows_list_to_csv_str([",0", "0,a", "1,ZZZZZ", "2,c"])
csv = pd.Series(["a", pd.NA, "c"], dtype=nullable_string_dtype).to_csv(
na_rep="ZZZZZ"
)
assert expected == csv
def test_to_csv_date_format(self):
# GH 10209
df_sec = DataFrame({"A": pd.date_range("20130101", periods=5, freq="s")})
df_day = DataFrame({"A": pd.date_range("20130101", periods=5, freq="d")})
expected_rows = [
",A",
"0,2013-01-01 00:00:00",
"1,2013-01-01 00:00:01",
"2,2013-01-01 00:00:02",
"3,2013-01-01 00:00:03",
"4,2013-01-01 00:00:04",
]
expected_default_sec = tm.convert_rows_list_to_csv_str(expected_rows)
assert df_sec.to_csv() == expected_default_sec
expected_rows = [
",A",
"0,2013-01-01 00:00:00",
"1,2013-01-02 00:00:00",
"2,2013-01-03 00:00:00",
"3,2013-01-04 00:00:00",
"4,2013-01-05 00:00:00",
]
expected_ymdhms_day = tm.convert_rows_list_to_csv_str(expected_rows)
assert df_day.to_csv(date_format="%Y-%m-%d %H:%M:%S") == expected_ymdhms_day
expected_rows = [
",A",
"0,2013-01-01",
"1,2013-01-01",
"2,2013-01-01",
"3,2013-01-01",
"4,2013-01-01",
]
expected_ymd_sec = tm.convert_rows_list_to_csv_str(expected_rows)
assert df_sec.to_csv(date_format="%Y-%m-%d") == expected_ymd_sec
expected_rows = [
",A",
"0,2013-01-01",
"1,2013-01-02",
"2,2013-01-03",
"3,2013-01-04",
"4,2013-01-05",
]
expected_default_day = tm.convert_rows_list_to_csv_str(expected_rows)
assert df_day.to_csv() == expected_default_day
assert df_day.to_csv(date_format="%Y-%m-%d") == expected_default_day
# see gh-7791
#
# Testing if date_format parameter is taken into account
# for multi-indexed DataFrames.
df_sec["B"] = 0
df_sec["C"] = 1
expected_rows = ["A,B,C", "2013-01-01,0,1.0"]
expected_ymd_sec = tm.convert_rows_list_to_csv_str(expected_rows)
df_sec_grouped = df_sec.groupby([pd.Grouper(key="A", freq="1h"), "B"])
assert df_sec_grouped.mean().to_csv(date_format="%Y-%m-%d") == expected_ymd_sec
def test_to_csv_different_datetime_formats(self):
# GH#21734
df = DataFrame(
{
"date": pd.to_datetime("1970-01-01"),
"datetime": pd.date_range("1970-01-01", periods=2, freq="h"),
}
)
expected_rows = [
"date,datetime",
"1970-01-01,1970-01-01 00:00:00",
"1970-01-01,1970-01-01 01:00:00",
]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert df.to_csv(index=False) == expected
def test_to_csv_date_format_in_categorical(self):
# GH#40754
ser = pd.Series(pd.to_datetime(["2021-03-27", pd.NaT], format="%Y-%m-%d"))
ser = ser.astype("category")
expected = tm.convert_rows_list_to_csv_str(["0", "2021-03-27", '""'])
assert ser.to_csv(index=False) == expected
ser = pd.Series(
pd.date_range(
start="2021-03-27", freq="D", periods=1, tz="Europe/Berlin"
).append(pd.DatetimeIndex([pd.NaT]))
)
ser = ser.astype("category")
assert ser.to_csv(index=False, date_format="%Y-%m-%d") == expected
def test_to_csv_float_ea_float_format(self):
# GH#45991
df = DataFrame({"a": [1.1, 2.02, pd.NA, 6.000006], "b": "c"})
df["a"] = df["a"].astype("Float64")
result = df.to_csv(index=False, float_format="%.5f")
expected = tm.convert_rows_list_to_csv_str(
["a,b", "1.10000,c", "2.02000,c", ",c", "6.00001,c"]
)
assert result == expected
def test_to_csv_float_ea_no_float_format(self):
# GH#45991
df = DataFrame({"a": [1.1, 2.02, pd.NA, 6.000006], "b": "c"})
df["a"] = df["a"].astype("Float64")
result = df.to_csv(index=False)
expected = tm.convert_rows_list_to_csv_str(
["a,b", "1.1,c", "2.02,c", ",c", "6.000006,c"]
)
assert result == expected
def test_to_csv_multi_index(self):
# see gh-6618
df = DataFrame([1], columns=pd.MultiIndex.from_arrays([[1], [2]]))
exp_rows = [",1", ",2", "0,1"]
exp = tm.convert_rows_list_to_csv_str(exp_rows)
assert df.to_csv() == exp
exp_rows = ["1", "2", "1"]
exp = tm.convert_rows_list_to_csv_str(exp_rows)
assert df.to_csv(index=False) == exp
df = DataFrame(
[1],
columns=pd.MultiIndex.from_arrays([[1], [2]]),
index=pd.MultiIndex.from_arrays([[1], [2]]),
)
exp_rows = [",,1", ",,2", "1,2,1"]
exp = tm.convert_rows_list_to_csv_str(exp_rows)
assert df.to_csv() == exp
exp_rows = ["1", "2", "1"]
exp = tm.convert_rows_list_to_csv_str(exp_rows)
assert df.to_csv(index=False) == exp
df = DataFrame([1], columns=pd.MultiIndex.from_arrays([["foo"], ["bar"]]))
exp_rows = [",foo", ",bar", "0,1"]
exp = tm.convert_rows_list_to_csv_str(exp_rows)
assert df.to_csv() == exp
exp_rows = ["foo", "bar", "1"]
exp = tm.convert_rows_list_to_csv_str(exp_rows)
assert df.to_csv(index=False) == exp
@pytest.mark.parametrize(
"ind,expected",
[
(
pd.MultiIndex(levels=[[1.0]], codes=[[0]], names=["x"]),
"x,data\n1.0,1\n",
),
(
pd.MultiIndex(
levels=[[1.0], [2.0]], codes=[[0], [0]], names=["x", "y"]
),
"x,y,data\n1.0,2.0,1\n",
),
],
)
def test_to_csv_single_level_multi_index(self, ind, expected, frame_or_series):
# see gh-19589
obj = frame_or_series(pd.Series([1], ind, name="data"))
result = obj.to_csv(lineterminator="\n", header=True)
assert result == expected
def test_to_csv_string_array_ascii(self):
# GH 10813
str_array = [{"names": ["foo", "bar"]}, {"names": ["baz", "qux"]}]
df = DataFrame(str_array)
expected_ascii = """\
,names
0,"['foo', 'bar']"
1,"['baz', 'qux']"
"""
with tm.ensure_clean("str_test.csv") as path:
df.to_csv(path, encoding="ascii")
with open(path, encoding="utf-8") as f:
assert f.read() == expected_ascii
def test_to_csv_string_array_utf8(self):
# GH 10813
str_array = [{"names": ["foo", "bar"]}, {"names": ["baz", "qux"]}]
df = DataFrame(str_array)
expected_utf8 = """\
,names
0,"['foo', 'bar']"
1,"['baz', 'qux']"
"""
with tm.ensure_clean("unicode_test.csv") as path:
df.to_csv(path, encoding="utf-8")
with open(path, encoding="utf-8") as f:
assert f.read() == expected_utf8
def test_to_csv_string_with_lf(self):
# GH 20353
data = {"int": [1, 2, 3], "str_lf": ["abc", "d\nef", "g\nh\n\ni"]}
df = DataFrame(data)
with tm.ensure_clean("lf_test.csv") as path:
# case 1: The default line terminator(=os.linesep)(PR 21406)
os_linesep = os.linesep.encode("utf-8")
expected_noarg = (
b"int,str_lf"
+ os_linesep
+ b"1,abc"
+ os_linesep
+ b'2,"d\nef"'
+ os_linesep
+ b'3,"g\nh\n\ni"'
+ os_linesep
)
df.to_csv(path, index=False)
with open(path, "rb") as f:
assert f.read() == expected_noarg
with tm.ensure_clean("lf_test.csv") as path:
# case 2: LF as line terminator
expected_lf = b'int,str_lf\n1,abc\n2,"d\nef"\n3,"g\nh\n\ni"\n'
df.to_csv(path, lineterminator="\n", index=False)
with open(path, "rb") as f:
assert f.read() == expected_lf
with tm.ensure_clean("lf_test.csv") as path:
# case 3: CRLF as line terminator
# 'lineterminator' should not change inner element
expected_crlf = b'int,str_lf\r\n1,abc\r\n2,"d\nef"\r\n3,"g\nh\n\ni"\r\n'
df.to_csv(path, lineterminator="\r\n", index=False)
with open(path, "rb") as f:
assert f.read() == expected_crlf
def test_to_csv_string_with_crlf(self):
# GH 20353
data = {"int": [1, 2, 3], "str_crlf": ["abc", "d\r\nef", "g\r\nh\r\n\r\ni"]}
df = DataFrame(data)
with tm.ensure_clean("crlf_test.csv") as path:
# case 1: The default line terminator(=os.linesep)(PR 21406)
os_linesep = os.linesep.encode("utf-8")
expected_noarg = (
b"int,str_crlf"
+ os_linesep
+ b"1,abc"
+ os_linesep
+ b'2,"d\r\nef"'
+ os_linesep
+ b'3,"g\r\nh\r\n\r\ni"'
+ os_linesep
)
df.to_csv(path, index=False)
with open(path, "rb") as f:
assert f.read() == expected_noarg
with tm.ensure_clean("crlf_test.csv") as path:
# case 2: LF as line terminator
expected_lf = b'int,str_crlf\n1,abc\n2,"d\r\nef"\n3,"g\r\nh\r\n\r\ni"\n'
df.to_csv(path, lineterminator="\n", index=False)
with open(path, "rb") as f:
assert f.read() == expected_lf
with tm.ensure_clean("crlf_test.csv") as path:
# case 3: CRLF as line terminator
# 'lineterminator' should not change inner element
expected_crlf = (
b"int,str_crlf\r\n"
b"1,abc\r\n"
b'2,"d\r\nef"\r\n'
b'3,"g\r\nh\r\n\r\ni"\r\n'
)
df.to_csv(path, lineterminator="\r\n", index=False)
with open(path, "rb") as f:
assert f.read() == expected_crlf
def test_to_csv_stdout_file(self, capsys):
# GH 21561
df = DataFrame([["foo", "bar"], ["baz", "qux"]], columns=["name_1", "name_2"])
expected_rows = [",name_1,name_2", "0,foo,bar", "1,baz,qux"]
expected_ascii = tm.convert_rows_list_to_csv_str(expected_rows)
df.to_csv(sys.stdout, encoding="ascii")
captured = capsys.readouterr()
assert captured.out == expected_ascii
assert not sys.stdout.closed
@pytest.mark.xfail(
compat.is_platform_windows(),
reason=(
"Especially in Windows, file stream should not be passed"
"to csv writer without newline='' option."
"(https://docs.python.org/3/library/csv.html#csv.writer)"
),
)
def test_to_csv_write_to_open_file(self):
# GH 21696
df = DataFrame({"a": ["x", "y", "z"]})
expected = """\
manual header
x
y
z
"""
with tm.ensure_clean("test.txt") as path:
with open(path, "w", encoding="utf-8") as f:
f.write("manual header\n")
df.to_csv(f, header=None, index=None)
with open(path, encoding="utf-8") as f:
assert f.read() == expected
def test_to_csv_write_to_open_file_with_newline_py3(self):
# see gh-21696
# see gh-20353
df = DataFrame({"a": ["x", "y", "z"]})
expected_rows = ["x", "y", "z"]
expected = "manual header\n" + tm.convert_rows_list_to_csv_str(expected_rows)
with tm.ensure_clean("test.txt") as path:
with open(path, "w", newline="", encoding="utf-8") as f:
f.write("manual header\n")
df.to_csv(f, header=None, index=None)
with open(path, "rb") as f:
assert f.read() == bytes(expected, "utf-8")
@pytest.mark.parametrize("to_infer", [True, False])
@pytest.mark.parametrize("read_infer", [True, False])
def test_to_csv_compression(
self, compression_only, read_infer, to_infer, compression_to_extension
):
# see gh-15008
compression = compression_only
# We'll complete file extension subsequently.
filename = "test."
filename += compression_to_extension[compression]
df = DataFrame({"A": [1]})
to_compression = "infer" if to_infer else compression
read_compression = "infer" if read_infer else compression
with tm.ensure_clean(filename) as path:
df.to_csv(path, compression=to_compression)
result = pd.read_csv(path, index_col=0, compression=read_compression)
tm.assert_frame_equal(result, df)
def test_to_csv_compression_dict(self, compression_only):
# GH 26023
method = compression_only
df = DataFrame({"ABC": [1]})
filename = "to_csv_compress_as_dict."
extension = {
"gzip": "gz",
"zstd": "zst",
}.get(method, method)
filename += extension
with tm.ensure_clean(filename) as path:
df.to_csv(path, compression={"method": method})
read_df = pd.read_csv(path, index_col=0)
tm.assert_frame_equal(read_df, df)
def test_to_csv_compression_dict_no_method_raises(self):
# GH 26023
df = DataFrame({"ABC": [1]})
compression = {"some_option": True}
msg = "must have key 'method'"
with tm.ensure_clean("out.zip") as path:
with pytest.raises(ValueError, match=msg):
df.to_csv(path, compression=compression)
@pytest.mark.parametrize("compression", ["zip", "infer"])
@pytest.mark.parametrize("archive_name", ["test_to_csv.csv", "test_to_csv.zip"])
def test_to_csv_zip_arguments(self, compression, archive_name):
# GH 26023
df = DataFrame({"ABC": [1]})
with tm.ensure_clean("to_csv_archive_name.zip") as path:
df.to_csv(
path, compression={"method": compression, "archive_name": archive_name}
)
with ZipFile(path) as zp:
assert len(zp.filelist) == 1
archived_file = zp.filelist[0].filename
assert archived_file == archive_name
@pytest.mark.parametrize(
"filename,expected_arcname",
[
("archive.csv", "archive.csv"),
("archive.tsv", "archive.tsv"),
("archive.csv.zip", "archive.csv"),
("archive.tsv.zip", "archive.tsv"),
("archive.zip", "archive"),
],
)
def test_to_csv_zip_infer_name(self, tmp_path, filename, expected_arcname):
# GH 39465
df = DataFrame({"ABC": [1]})
path = tmp_path / filename
df.to_csv(path, compression="zip")
with ZipFile(path) as zp:
assert len(zp.filelist) == 1
archived_file = zp.filelist[0].filename
assert archived_file == expected_arcname
@pytest.mark.parametrize("df_new_type", ["Int64"])
def test_to_csv_na_rep_long_string(self, df_new_type):
# see gh-25099
df = DataFrame({"c": [float("nan")] * 3})
df = df.astype(df_new_type)
expected_rows = ["c", "mynull", "mynull", "mynull"]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
result = df.to_csv(index=False, na_rep="mynull", encoding="ascii")
assert expected == result
def test_to_csv_timedelta_precision(self):
# GH 6783
s = pd.Series([1, 1]).astype("timedelta64[ns]")
buf = io.StringIO()
s.to_csv(buf)
result = buf.getvalue()
expected_rows = [
",0",
"0,0 days 00:00:00.000000001",
"1,0 days 00:00:00.000000001",
]
expected = tm.convert_rows_list_to_csv_str(expected_rows)
assert result == expected
def test_na_rep_truncated(self):
# https://github.com/pandas-dev/pandas/issues/31447
result = pd.Series(range(8, 12)).to_csv(na_rep="-")
expected = tm.convert_rows_list_to_csv_str([",0", "0,8", "1,9", "2,10", "3,11"])
assert result == expected
result = pd.Series([True, False]).to_csv(na_rep="nan")
expected = tm.convert_rows_list_to_csv_str([",0", "0,True", "1,False"])
assert result == expected
result = pd.Series([1.1, 2.2]).to_csv(na_rep=".")
expected = tm.convert_rows_list_to_csv_str([",0", "0,1.1", "1,2.2"])
assert result == expected
@pytest.mark.parametrize("errors", ["surrogatepass", "ignore", "replace"])
def test_to_csv_errors(self, errors):
# GH 22610
data = ["\ud800foo"]
ser = pd.Series(data, index=Index(data, dtype=object), dtype=object)
with tm.ensure_clean("test.csv") as path:
ser.to_csv(path, errors=errors)
# No use in reading back the data as it is not the same anymore
# due to the error handling
@pytest.mark.parametrize("mode", ["wb", "w"])
def test_to_csv_binary_handle(self, mode):
"""
Binary file objects should work (if 'mode' contains a 'b') or even without
it in most cases.
GH 35058 and GH 19827
"""
df = DataFrame(
1.1 * np.arange(120).reshape((30, 4)),
columns=Index(list("ABCD")),
index=Index([f"i-{i}" for i in range(30)]),
)
with tm.ensure_clean() as path:
with open(path, mode="w+b") as handle:
df.to_csv(handle, mode=mode)
tm.assert_frame_equal(df, pd.read_csv(path, index_col=0))
@pytest.mark.parametrize("mode", ["wb", "w"])
def test_to_csv_encoding_binary_handle(self, mode):
"""
Binary file objects should honor a specified encoding.
GH 23854 and GH 13068 with binary handles
"""
# example from GH 23854
content = "a, b, 🐟".encode("utf-8-sig")
buffer = io.BytesIO(content)
df = pd.read_csv(buffer, encoding="utf-8-sig")
buffer = io.BytesIO()
df.to_csv(buffer, mode=mode, encoding="utf-8-sig", index=False)
buffer.seek(0) # tests whether file handle wasn't closed
assert buffer.getvalue().startswith(content)
# example from GH 13068
with tm.ensure_clean() as path:
with open(path, "w+b") as handle:
DataFrame().to_csv(handle, mode=mode, encoding="utf-8-sig")
handle.seek(0)
assert handle.read().startswith(b'\xef\xbb\xbf""')
def test_to_csv_iterative_compression_name(compression):
# GH 38714
df = DataFrame(
1.1 * np.arange(120).reshape((30, 4)),
columns=Index(list("ABCD")),
index=Index([f"i-{i}" for i in range(30)]),
)
with tm.ensure_clean() as path:
df.to_csv(path, compression=compression, chunksize=1)
tm.assert_frame_equal(
pd.read_csv(path, compression=compression, index_col=0), df
)
def test_to_csv_iterative_compression_buffer(compression):
# GH 38714
df = DataFrame(
1.1 * np.arange(120).reshape((30, 4)),
columns=Index(list("ABCD")),
index=Index([f"i-{i}" for i in range(30)]),
)
with io.BytesIO() as buffer:
df.to_csv(buffer, compression=compression, chunksize=1)
buffer.seek(0)
tm.assert_frame_equal(
pd.read_csv(buffer, compression=compression, index_col=0), df
)
assert not buffer.closed
def test_to_csv_pos_args_deprecation():
# GH-54229
df = DataFrame({"a": [1, 2, 3]})
msg = (
r"Starting with pandas version 3.0 all arguments of to_csv except for the "
r"argument 'path_or_buf' will be keyword-only."
)
with tm.assert_produces_warning(FutureWarning, match=msg):
buffer = io.BytesIO()
df.to_csv(buffer, ";")
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