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"""
Tests that work on both the Python and C engines but do not have a
specific classification into the other test modules.
"""
from io import StringIO
import numpy as np
import pytest
from pandas.compat import is_platform_linux
from pandas import DataFrame
import pandas._testing as tm
pytestmark = pytest.mark.filterwarnings(
"ignore:Passing a BlockManager to DataFrame:DeprecationWarning"
)
xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
@skip_pyarrow # ParserError: CSV parse error: Empty CSV file or block
def test_float_parser(all_parsers):
# see gh-9565
parser = all_parsers
data = "45e-1,4.5,45.,inf,-inf"
result = parser.read_csv(StringIO(data), header=None)
expected = DataFrame([[float(s) for s in data.split(",")]])
tm.assert_frame_equal(result, expected)
def test_scientific_no_exponent(all_parsers_all_precisions):
# see gh-12215
df = DataFrame.from_dict({"w": ["2e"], "x": ["3E"], "y": ["42e"], "z": ["632E"]})
data = df.to_csv(index=False)
parser, precision = all_parsers_all_precisions
df_roundtrip = parser.read_csv(StringIO(data), float_precision=precision)
tm.assert_frame_equal(df_roundtrip, df)
@pytest.mark.parametrize(
"neg_exp",
[
-617,
-100000,
pytest.param(-99999999999999999, marks=pytest.mark.skip_ubsan),
],
)
def test_very_negative_exponent(all_parsers_all_precisions, neg_exp):
# GH#38753
parser, precision = all_parsers_all_precisions
data = f"data\n10E{neg_exp}"
result = parser.read_csv(StringIO(data), float_precision=precision)
expected = DataFrame({"data": [0.0]})
tm.assert_frame_equal(result, expected)
@pytest.mark.skip_ubsan
@xfail_pyarrow # AssertionError: Attributes of DataFrame.iloc[:, 0] are different
@pytest.mark.parametrize("exp", [999999999999999999, -999999999999999999])
def test_too_many_exponent_digits(all_parsers_all_precisions, exp, request):
# GH#38753
parser, precision = all_parsers_all_precisions
data = f"data\n10E{exp}"
result = parser.read_csv(StringIO(data), float_precision=precision)
if precision == "round_trip":
if exp == 999999999999999999:
mark = pytest.mark.xfail(reason="GH38794, on Linux gives object result")
request.applymarker(mark)
value = np.inf if exp > 0 else 0.0
expected = DataFrame({"data": [value]})
else:
expected = DataFrame({"data": [f"10E{exp}"]})
tm.assert_frame_equal(result, expected)
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