"""
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Tests that work on both the Python and C engines but do not have a
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specific classification into the other test modules.
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"""
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from io import StringIO
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import numpy as np
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import pytest
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from pandas import (
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DataFrame,
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Series,
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)
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import pandas._testing as tm
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# GH#43650: Some expected failures with the pyarrow engine can occasionally
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# cause a deadlock instead, so we skip these instead of xfailing
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skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
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def test_int_conversion(all_parsers):
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data = """A,B
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1.0,1
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2.0,2
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3.0,3
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"""
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parser = all_parsers
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result = parser.read_csv(StringIO(data))
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expected = DataFrame([[1.0, 1], [2.0, 2], [3.0, 3]], columns=["A", "B"])
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tm.assert_frame_equal(result, expected)
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@pytest.mark.parametrize(
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"data,kwargs,expected",
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[
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(
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"A,B\nTrue,1\nFalse,2\nTrue,3",
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{},
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DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
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),
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(
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"A,B\nYES,1\nno,2\nyes,3\nNo,3\nYes,3",
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{"true_values": ["yes", "Yes", "YES"], "false_values": ["no", "NO", "No"]},
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DataFrame(
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[[True, 1], [False, 2], [True, 3], [False, 3], [True, 3]],
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columns=["A", "B"],
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),
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),
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(
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"A,B\nTRUE,1\nFALSE,2\nTRUE,3",
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{},
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DataFrame([[True, 1], [False, 2], [True, 3]], columns=["A", "B"]),
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),
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(
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"A,B\nfoo,bar\nbar,foo",
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{"true_values": ["foo"], "false_values": ["bar"]},
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DataFrame([[True, False], [False, True]], columns=["A", "B"]),
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),
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],
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)
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def test_parse_bool(all_parsers, data, kwargs, expected):
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parser = all_parsers
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result = parser.read_csv(StringIO(data), **kwargs)
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tm.assert_frame_equal(result, expected)
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def test_parse_integers_above_fp_precision(all_parsers):
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data = """Numbers
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17007000002000191
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17007000002000191
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17007000002000191
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17007000002000191
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17007000002000192
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17007000002000192
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17007000002000192
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17007000002000192
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17007000002000192
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17007000002000194"""
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parser = all_parsers
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result = parser.read_csv(StringIO(data))
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expected = DataFrame(
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{
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"Numbers": [
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17007000002000191,
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17007000002000191,
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17007000002000191,
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17007000002000191,
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17007000002000192,
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17007000002000192,
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17007000002000192,
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17007000002000192,
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17007000002000192,
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17007000002000194,
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]
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}
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)
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow # Flaky
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@pytest.mark.parametrize("sep", [" ", r"\s+"])
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def test_integer_overflow_bug(all_parsers, sep):
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# see gh-2601
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data = "65248E10 11\n55555E55 22\n"
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parser = all_parsers
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result = parser.read_csv(StringIO(data), header=None, sep=sep)
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expected = DataFrame([[6.5248e14, 11], [5.5555e59, 22]])
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tm.assert_frame_equal(result, expected)
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def test_int64_min_issues(all_parsers):
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# see gh-2599
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parser = all_parsers
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data = "A,B\n0,0\n0,"
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result = parser.read_csv(StringIO(data))
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expected = DataFrame({"A": [0, 0], "B": [0, np.nan]})
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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@pytest.mark.parametrize("conv", [None, np.int64, np.uint64])
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def test_int64_overflow(all_parsers, conv):
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data = """ID
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00013007854817840016671868
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00013007854817840016749251
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00013007854817840016754630
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00013007854817840016781876
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00013007854817840017028824
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00013007854817840017963235
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00013007854817840018860166"""
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parser = all_parsers
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if conv is None:
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# 13007854817840016671868 > UINT64_MAX, so this
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# will overflow and return object as the dtype.
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result = parser.read_csv(StringIO(data))
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expected = DataFrame(
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[
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"00013007854817840016671868",
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"00013007854817840016749251",
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"00013007854817840016754630",
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"00013007854817840016781876",
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"00013007854817840017028824",
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"00013007854817840017963235",
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"00013007854817840018860166",
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],
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columns=["ID"],
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)
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tm.assert_frame_equal(result, expected)
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else:
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# 13007854817840016671868 > UINT64_MAX, so attempts
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# to cast to either int64 or uint64 will result in
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# an OverflowError being raised.
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msg = (
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"(Python int too large to convert to C long)|"
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"(long too big to convert)|"
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"(int too big to convert)"
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)
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with pytest.raises(OverflowError, match=msg):
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parser.read_csv(StringIO(data), converters={"ID": conv})
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@skip_pyarrow
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@pytest.mark.parametrize(
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"val", [np.iinfo(np.uint64).max, np.iinfo(np.int64).max, np.iinfo(np.int64).min]
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)
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def test_int64_uint64_range(all_parsers, val):
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# These numbers fall right inside the int64-uint64
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# range, so they should be parsed as string.
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parser = all_parsers
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result = parser.read_csv(StringIO(str(val)), header=None)
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expected = DataFrame([val])
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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@pytest.mark.parametrize(
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"val", [np.iinfo(np.uint64).max + 1, np.iinfo(np.int64).min - 1]
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)
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def test_outside_int64_uint64_range(all_parsers, val):
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# These numbers fall just outside the int64-uint64
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# range, so they should be parsed as string.
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parser = all_parsers
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result = parser.read_csv(StringIO(str(val)), header=None)
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expected = DataFrame([str(val)])
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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@pytest.mark.parametrize("exp_data", [[str(-1), str(2**63)], [str(2**63), str(-1)]])
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def test_numeric_range_too_wide(all_parsers, exp_data):
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# No numerical dtype can hold both negative and uint64
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# values, so they should be cast as string.
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parser = all_parsers
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data = "\n".join(exp_data)
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expected = DataFrame(exp_data)
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result = parser.read_csv(StringIO(data), header=None)
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tm.assert_frame_equal(result, expected)
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def test_integer_precision(all_parsers):
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# Gh 7072
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s = """1,1;0;0;0;1;1;3844;3844;3844;1;1;1;1;1;1;0;0;1;1;0;0,,,4321583677327450765
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5,1;0;0;0;1;1;843;843;843;1;1;1;1;1;1;0;0;1;1;0;0,64.0,;,4321113141090630389"""
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parser = all_parsers
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result = parser.read_csv(StringIO(s), header=None)[4]
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expected = Series([4321583677327450765, 4321113141090630389], name=4)
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tm.assert_series_equal(result, expected)
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