658 lines
16 KiB
Python
658 lines
16 KiB
Python
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"""
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Tests that NA values are properly handled during
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parsing for all of the parsers defined in parsers.py
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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._libs.parsers import STR_NA_VALUES
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from pandas import (
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DataFrame,
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Index,
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MultiIndex,
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)
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import pandas._testing as tm
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skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
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xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
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@skip_pyarrow
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def test_string_nas(all_parsers):
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parser = all_parsers
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data = """A,B,C
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a,b,c
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d,,f
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,g,h
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"""
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result = parser.read_csv(StringIO(data))
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expected = DataFrame(
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[["a", "b", "c"], ["d", np.nan, "f"], [np.nan, "g", "h"]],
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columns=["A", "B", "C"],
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)
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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def test_detect_string_na(all_parsers):
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parser = all_parsers
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data = """A,B
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foo,bar
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NA,baz
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NaN,nan
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"""
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expected = DataFrame(
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[["foo", "bar"], [np.nan, "baz"], [np.nan, np.nan]], columns=["A", "B"]
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)
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result = parser.read_csv(StringIO(data))
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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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"na_values",
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[
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["-999.0", "-999"],
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[-999, -999.0],
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[-999.0, -999],
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["-999.0"],
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["-999"],
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[-999.0],
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[-999],
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],
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)
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@pytest.mark.parametrize(
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"data",
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[
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"""A,B
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-999,1.2
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2,-999
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3,4.5
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""",
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"""A,B
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-999,1.200
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2,-999.000
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3,4.500
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""",
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],
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)
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def test_non_string_na_values(all_parsers, data, na_values):
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# see gh-3611: with an odd float format, we can't match
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# the string "999.0" exactly but still need float matching
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parser = all_parsers
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expected = DataFrame([[np.nan, 1.2], [2.0, np.nan], [3.0, 4.5]], columns=["A", "B"])
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result = parser.read_csv(StringIO(data), na_values=na_values)
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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def test_default_na_values(all_parsers):
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_NA_VALUES = {
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"-1.#IND",
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"1.#QNAN",
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"1.#IND",
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"-1.#QNAN",
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"#N/A",
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"N/A",
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"n/a",
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"NA",
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"<NA>",
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"#NA",
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"NULL",
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"null",
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"NaN",
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"nan",
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"-NaN",
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"-nan",
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"#N/A N/A",
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"",
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"None",
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}
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assert _NA_VALUES == STR_NA_VALUES
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parser = all_parsers
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nv = len(_NA_VALUES)
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def f(i, v):
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if i == 0:
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buf = ""
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elif i > 0:
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buf = "".join([","] * i)
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buf = f"{buf}{v}"
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if i < nv - 1:
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joined = "".join([","] * (nv - i - 1))
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buf = f"{buf}{joined}"
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return buf
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data = StringIO("\n".join([f(i, v) for i, v in enumerate(_NA_VALUES)]))
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expected = DataFrame(np.nan, columns=range(nv), index=range(nv))
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result = parser.read_csv(data, header=None)
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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@pytest.mark.parametrize("na_values", ["baz", ["baz"]])
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def test_custom_na_values(all_parsers, na_values):
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parser = all_parsers
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data = """A,B,C
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ignore,this,row
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1,NA,3
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-1.#IND,5,baz
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7,8,NaN
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"""
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expected = DataFrame(
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[[1.0, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]], columns=["A", "B", "C"]
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)
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result = parser.read_csv(StringIO(data), na_values=na_values, skiprows=[1])
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tm.assert_frame_equal(result, expected)
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def test_bool_na_values(all_parsers):
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data = """A,B,C
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True,False,True
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NA,True,False
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False,NA,True"""
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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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"A": np.array([True, np.nan, False], dtype=object),
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"B": np.array([False, True, np.nan], dtype=object),
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"C": [True, False, True],
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}
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)
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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def test_na_value_dict(all_parsers):
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data = """A,B,C
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foo,bar,NA
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bar,foo,foo
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foo,bar,NA
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bar,foo,foo"""
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parser = all_parsers
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df = parser.read_csv(StringIO(data), na_values={"A": ["foo"], "B": ["bar"]})
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expected = DataFrame(
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{
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"A": [np.nan, "bar", np.nan, "bar"],
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"B": [np.nan, "foo", np.nan, "foo"],
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"C": [np.nan, "foo", np.nan, "foo"],
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}
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)
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tm.assert_frame_equal(df, expected)
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@skip_pyarrow
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@pytest.mark.parametrize(
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"index_col,expected",
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[
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(
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[0],
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DataFrame({"b": [np.nan], "c": [1], "d": [5]}, index=Index([0], name="a")),
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),
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(
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[0, 2],
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DataFrame(
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{"b": [np.nan], "d": [5]},
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index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]),
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),
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),
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(
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["a", "c"],
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DataFrame(
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{"b": [np.nan], "d": [5]},
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index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]),
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),
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),
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],
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)
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def test_na_value_dict_multi_index(all_parsers, index_col, expected):
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data = """\
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a,b,c,d
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0,NA,1,5
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"""
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parser = all_parsers
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result = parser.read_csv(StringIO(data), na_values=set(), index_col=index_col)
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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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"kwargs,expected",
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[
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(
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{},
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DataFrame(
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{
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"A": ["a", "b", np.nan, "d", "e", np.nan, "g"],
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"B": [1, 2, 3, 4, 5, 6, 7],
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"C": ["one", "two", "three", np.nan, "five", np.nan, "seven"],
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}
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),
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),
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(
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{"na_values": {"A": [], "C": []}, "keep_default_na": False},
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DataFrame(
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{
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"A": ["a", "b", "", "d", "e", "nan", "g"],
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"B": [1, 2, 3, 4, 5, 6, 7],
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"C": ["one", "two", "three", "nan", "five", "", "seven"],
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}
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),
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),
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(
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{"na_values": ["a"], "keep_default_na": False},
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DataFrame(
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{
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"A": [np.nan, "b", "", "d", "e", "nan", "g"],
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"B": [1, 2, 3, 4, 5, 6, 7],
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"C": ["one", "two", "three", "nan", "five", "", "seven"],
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}
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),
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),
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(
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{"na_values": {"A": [], "C": []}},
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DataFrame(
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{
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"A": ["a", "b", np.nan, "d", "e", np.nan, "g"],
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"B": [1, 2, 3, 4, 5, 6, 7],
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"C": ["one", "two", "three", np.nan, "five", np.nan, "seven"],
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}
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),
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),
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],
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)
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def test_na_values_keep_default(all_parsers, kwargs, expected):
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data = """\
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A,B,C
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a,1,one
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b,2,two
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,3,three
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d,4,nan
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e,5,five
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nan,6,
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g,7,seven
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"""
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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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@skip_pyarrow
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def test_no_na_values_no_keep_default(all_parsers):
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# see gh-4318: passing na_values=None and
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# keep_default_na=False yields 'None" as a na_value
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data = """\
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A,B,C
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a,1,None
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b,2,two
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,3,None
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d,4,nan
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e,5,five
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nan,6,
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g,7,seven
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"""
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parser = all_parsers
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result = parser.read_csv(StringIO(data), keep_default_na=False)
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expected = DataFrame(
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{
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"A": ["a", "b", "", "d", "e", "nan", "g"],
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"B": [1, 2, 3, 4, 5, 6, 7],
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"C": ["None", "two", "None", "nan", "five", "", "seven"],
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}
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)
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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def test_no_keep_default_na_dict_na_values(all_parsers):
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# see gh-19227
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data = "a,b\n,2"
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parser = all_parsers
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result = parser.read_csv(
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StringIO(data), na_values={"b": ["2"]}, keep_default_na=False
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)
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expected = DataFrame({"a": [""], "b": [np.nan]})
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tm.assert_frame_equal(result, expected)
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@skip_pyarrow
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def test_no_keep_default_na_dict_na_scalar_values(all_parsers):
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# see gh-19227
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#
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# Scalar values shouldn't cause the parsing to crash or fail.
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data = "a,b\n1,2"
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parser = all_parsers
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df = parser.read_csv(StringIO(data), na_values={"b": 2}, keep_default_na=False)
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expected = DataFrame({"a": [1], "b": [np.nan]})
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tm.assert_frame_equal(df, expected)
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@skip_pyarrow
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@pytest.mark.parametrize("col_zero_na_values", [113125, "113125"])
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def test_no_keep_default_na_dict_na_values_diff_reprs(all_parsers, col_zero_na_values):
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# see gh-19227
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data = """\
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113125,"blah","/blaha",kjsdkj,412.166,225.874,214.008
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729639,"qwer","",asdfkj,466.681,,252.373
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"""
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parser = all_parsers
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expected = DataFrame(
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{
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0: [np.nan, 729639.0],
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1: [np.nan, "qwer"],
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2: ["/blaha", np.nan],
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3: ["kjsdkj", "asdfkj"],
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4: [412.166, 466.681],
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5: ["225.874", ""],
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6: [np.nan, 252.373],
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}
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)
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result = parser.read_csv(
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StringIO(data),
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header=None,
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keep_default_na=False,
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na_values={2: "", 6: "214.008", 1: "blah", 0: col_zero_na_values},
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)
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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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"na_filter,row_data",
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[
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(True, [[1, "A"], [np.nan, np.nan], [3, "C"]]),
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(False, [["1", "A"], ["nan", "B"], ["3", "C"]]),
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],
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)
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def test_na_values_na_filter_override(all_parsers, na_filter, row_data):
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data = """\
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|
A,B
|
||
|
|
1,A
|
||
|
|
nan,B
|
||
|
|
3,C
|
||
|
|
"""
|
||
|
|
parser = all_parsers
|
||
|
|
result = parser.read_csv(StringIO(data), na_values=["B"], na_filter=na_filter)
|
||
|
|
|
||
|
|
expected = DataFrame(row_data, columns=["A", "B"])
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
def test_na_trailing_columns(all_parsers):
|
||
|
|
parser = all_parsers
|
||
|
|
data = """Date,Currency,Symbol,Type,Units,UnitPrice,Cost,Tax
|
||
|
|
2012-03-14,USD,AAPL,BUY,1000
|
||
|
|
2012-05-12,USD,SBUX,SELL,500"""
|
||
|
|
|
||
|
|
# Trailing columns should be all NaN.
|
||
|
|
result = parser.read_csv(StringIO(data))
|
||
|
|
expected = DataFrame(
|
||
|
|
[
|
||
|
|
["2012-03-14", "USD", "AAPL", "BUY", 1000, np.nan, np.nan, np.nan],
|
||
|
|
["2012-05-12", "USD", "SBUX", "SELL", 500, np.nan, np.nan, np.nan],
|
||
|
|
],
|
||
|
|
columns=[
|
||
|
|
"Date",
|
||
|
|
"Currency",
|
||
|
|
"Symbol",
|
||
|
|
"Type",
|
||
|
|
"Units",
|
||
|
|
"UnitPrice",
|
||
|
|
"Cost",
|
||
|
|
"Tax",
|
||
|
|
],
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"na_values,row_data",
|
||
|
|
[
|
||
|
|
(1, [[np.nan, 2.0], [2.0, np.nan]]),
|
||
|
|
({"a": 2, "b": 1}, [[1.0, 2.0], [np.nan, np.nan]]),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_na_values_scalar(all_parsers, na_values, row_data):
|
||
|
|
# see gh-12224
|
||
|
|
parser = all_parsers
|
||
|
|
names = ["a", "b"]
|
||
|
|
data = "1,2\n2,1"
|
||
|
|
|
||
|
|
result = parser.read_csv(StringIO(data), names=names, na_values=na_values)
|
||
|
|
expected = DataFrame(row_data, columns=names)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
def test_na_values_dict_aliasing(all_parsers):
|
||
|
|
parser = all_parsers
|
||
|
|
na_values = {"a": 2, "b": 1}
|
||
|
|
na_values_copy = na_values.copy()
|
||
|
|
|
||
|
|
names = ["a", "b"]
|
||
|
|
data = "1,2\n2,1"
|
||
|
|
|
||
|
|
expected = DataFrame([[1.0, 2.0], [np.nan, np.nan]], columns=names)
|
||
|
|
result = parser.read_csv(StringIO(data), names=names, na_values=na_values)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
tm.assert_dict_equal(na_values, na_values_copy)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
def test_na_values_dict_col_index(all_parsers):
|
||
|
|
# see gh-14203
|
||
|
|
data = "a\nfoo\n1"
|
||
|
|
parser = all_parsers
|
||
|
|
na_values = {0: "foo"}
|
||
|
|
|
||
|
|
result = parser.read_csv(StringIO(data), na_values=na_values)
|
||
|
|
expected = DataFrame({"a": [np.nan, 1]})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data,kwargs,expected",
|
||
|
|
[
|
||
|
|
(
|
||
|
|
str(2**63) + "\n" + str(2**63 + 1),
|
||
|
|
{"na_values": [2**63]},
|
||
|
|
DataFrame([str(2**63), str(2**63 + 1)]),
|
||
|
|
),
|
||
|
|
(str(2**63) + ",1" + "\n,2", {}, DataFrame([[str(2**63), 1], ["", 2]])),
|
||
|
|
(str(2**63) + "\n1", {"na_values": [2**63]}, DataFrame([np.nan, 1])),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_na_values_uint64(all_parsers, data, kwargs, expected):
|
||
|
|
# see gh-14983
|
||
|
|
parser = all_parsers
|
||
|
|
result = parser.read_csv(StringIO(data), header=None, **kwargs)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_empty_na_values_no_default_with_index(all_parsers):
|
||
|
|
# see gh-15835
|
||
|
|
data = "a,1\nb,2"
|
||
|
|
parser = all_parsers
|
||
|
|
expected = DataFrame({"1": [2]}, index=Index(["b"], name="a"))
|
||
|
|
|
||
|
|
result = parser.read_csv(StringIO(data), index_col=0, keep_default_na=False)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"na_filter,index_data", [(False, ["", "5"]), (True, [np.nan, 5.0])]
|
||
|
|
)
|
||
|
|
def test_no_na_filter_on_index(all_parsers, na_filter, index_data):
|
||
|
|
# see gh-5239
|
||
|
|
#
|
||
|
|
# Don't parse NA-values in index unless na_filter=True
|
||
|
|
parser = all_parsers
|
||
|
|
data = "a,b,c\n1,,3\n4,5,6"
|
||
|
|
|
||
|
|
expected = DataFrame({"a": [1, 4], "c": [3, 6]}, index=Index(index_data, name="b"))
|
||
|
|
result = parser.read_csv(StringIO(data), index_col=[1], na_filter=na_filter)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
def test_inf_na_values_with_int_index(all_parsers):
|
||
|
|
# see gh-17128
|
||
|
|
parser = all_parsers
|
||
|
|
data = "idx,col1,col2\n1,3,4\n2,inf,-inf"
|
||
|
|
|
||
|
|
# Don't fail with OverflowError with inf's and integer index column.
|
||
|
|
out = parser.read_csv(StringIO(data), index_col=[0], na_values=["inf", "-inf"])
|
||
|
|
expected = DataFrame(
|
||
|
|
{"col1": [3, np.nan], "col2": [4, np.nan]}, index=Index([1, 2], name="idx")
|
||
|
|
)
|
||
|
|
tm.assert_frame_equal(out, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
@pytest.mark.parametrize("na_filter", [True, False])
|
||
|
|
def test_na_values_with_dtype_str_and_na_filter(all_parsers, na_filter):
|
||
|
|
# see gh-20377
|
||
|
|
parser = all_parsers
|
||
|
|
data = "a,b,c\n1,,3\n4,5,6"
|
||
|
|
|
||
|
|
# na_filter=True --> missing value becomes NaN.
|
||
|
|
# na_filter=False --> missing value remains empty string.
|
||
|
|
empty = np.nan if na_filter else ""
|
||
|
|
expected = DataFrame({"a": ["1", "4"], "b": [empty, "5"], "c": ["3", "6"]})
|
||
|
|
|
||
|
|
result = parser.read_csv(StringIO(data), na_filter=na_filter, dtype=str)
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
@pytest.mark.parametrize(
|
||
|
|
"data, na_values",
|
||
|
|
[
|
||
|
|
("false,1\n,1\ntrue", None),
|
||
|
|
("false,1\nnull,1\ntrue", None),
|
||
|
|
("false,1\nnan,1\ntrue", None),
|
||
|
|
("false,1\nfoo,1\ntrue", "foo"),
|
||
|
|
("false,1\nfoo,1\ntrue", ["foo"]),
|
||
|
|
("false,1\nfoo,1\ntrue", {"a": "foo"}),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_cast_NA_to_bool_raises_error(all_parsers, data, na_values):
|
||
|
|
parser = all_parsers
|
||
|
|
msg = (
|
||
|
|
"(Bool column has NA values in column [0a])|"
|
||
|
|
"(cannot safely convert passed user dtype of "
|
||
|
|
"bool for object dtyped data in column 0)"
|
||
|
|
)
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
parser.read_csv(
|
||
|
|
StringIO(data),
|
||
|
|
header=None,
|
||
|
|
names=["a", "b"],
|
||
|
|
dtype={"a": "bool"},
|
||
|
|
na_values=na_values,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
def test_str_nan_dropped(all_parsers):
|
||
|
|
# see gh-21131
|
||
|
|
parser = all_parsers
|
||
|
|
|
||
|
|
data = """File: small.csv,,
|
||
|
|
10010010233,0123,654
|
||
|
|
foo,,bar
|
||
|
|
01001000155,4530,898"""
|
||
|
|
|
||
|
|
result = parser.read_csv(
|
||
|
|
StringIO(data),
|
||
|
|
header=None,
|
||
|
|
names=["col1", "col2", "col3"],
|
||
|
|
dtype={"col1": str, "col2": str, "col3": str},
|
||
|
|
).dropna()
|
||
|
|
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
"col1": ["10010010233", "01001000155"],
|
||
|
|
"col2": ["0123", "4530"],
|
||
|
|
"col3": ["654", "898"],
|
||
|
|
},
|
||
|
|
index=[1, 3],
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@skip_pyarrow
|
||
|
|
def test_nan_multi_index(all_parsers):
|
||
|
|
# GH 42446
|
||
|
|
parser = all_parsers
|
||
|
|
data = "A,B,B\nX,Y,Z\n1,2,inf"
|
||
|
|
|
||
|
|
result = parser.read_csv(
|
||
|
|
StringIO(data), header=list(range(2)), na_values={("B", "Z"): "inf"}
|
||
|
|
)
|
||
|
|
|
||
|
|
expected = DataFrame(
|
||
|
|
{
|
||
|
|
("A", "X"): [1],
|
||
|
|
("B", "Y"): [2],
|
||
|
|
("B", "Z"): [np.nan],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
|
||
|
|
@xfail_pyarrow
|
||
|
|
def test_bool_and_nan_to_bool(all_parsers):
|
||
|
|
# GH#42808
|
||
|
|
parser = all_parsers
|
||
|
|
data = """0
|
||
|
|
NaN
|
||
|
|
True
|
||
|
|
False
|
||
|
|
"""
|
||
|
|
with pytest.raises(ValueError, match="NA values"):
|
||
|
|
parser.read_csv(StringIO(data), dtype="bool")
|
||
|
|
|
||
|
|
|
||
|
|
def test_bool_and_nan_to_int(all_parsers):
|
||
|
|
# GH#42808
|
||
|
|
parser = all_parsers
|
||
|
|
data = """0
|
||
|
|
NaN
|
||
|
|
True
|
||
|
|
False
|
||
|
|
"""
|
||
|
|
with pytest.raises(ValueError, match="convert|NoneType"):
|
||
|
|
parser.read_csv(StringIO(data), dtype="int")
|
||
|
|
|
||
|
|
|
||
|
|
def test_bool_and_nan_to_float(all_parsers):
|
||
|
|
# GH#42808
|
||
|
|
parser = all_parsers
|
||
|
|
data = """0
|
||
|
|
NaN
|
||
|
|
True
|
||
|
|
False
|
||
|
|
"""
|
||
|
|
result = parser.read_csv(StringIO(data), dtype="float")
|
||
|
|
expected = DataFrame.from_dict({"0": [np.nan, 1.0, 0.0]})
|
||
|
|
tm.assert_frame_equal(result, expected)
|