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| """
| Tests that NA values are properly handled during
| parsing for all of the parsers defined in parsers.py
| """
| from io import StringIO
|
| import numpy as np
| import pytest
|
| from pandas._libs.parsers import STR_NA_VALUES
|
| from pandas import (
| DataFrame,
| Index,
| MultiIndex,
| )
| import pandas._testing as tm
|
| skip_pyarrow = pytest.mark.usefixtures("pyarrow_skip")
| xfail_pyarrow = pytest.mark.usefixtures("pyarrow_xfail")
|
|
| @skip_pyarrow
| def test_string_nas(all_parsers):
| parser = all_parsers
| data = """A,B,C
| a,b,c
| d,,f
| ,g,h
| """
| result = parser.read_csv(StringIO(data))
| expected = DataFrame(
| [["a", "b", "c"], ["d", np.nan, "f"], [np.nan, "g", "h"]],
| columns=["A", "B", "C"],
| )
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| def test_detect_string_na(all_parsers):
| parser = all_parsers
| data = """A,B
| foo,bar
| NA,baz
| NaN,nan
| """
| expected = DataFrame(
| [["foo", "bar"], [np.nan, "baz"], [np.nan, np.nan]], columns=["A", "B"]
| )
| result = parser.read_csv(StringIO(data))
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| @pytest.mark.parametrize(
| "na_values",
| [
| ["-999.0", "-999"],
| [-999, -999.0],
| [-999.0, -999],
| ["-999.0"],
| ["-999"],
| [-999.0],
| [-999],
| ],
| )
| @pytest.mark.parametrize(
| "data",
| [
| """A,B
| -999,1.2
| 2,-999
| 3,4.5
| """,
| """A,B
| -999,1.200
| 2,-999.000
| 3,4.500
| """,
| ],
| )
| def test_non_string_na_values(all_parsers, data, na_values):
| # see gh-3611: with an odd float format, we can't match
| # the string "999.0" exactly but still need float matching
| parser = all_parsers
| expected = DataFrame([[np.nan, 1.2], [2.0, np.nan], [3.0, 4.5]], columns=["A", "B"])
|
| result = parser.read_csv(StringIO(data), na_values=na_values)
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| def test_default_na_values(all_parsers):
| _NA_VALUES = {
| "-1.#IND",
| "1.#QNAN",
| "1.#IND",
| "-1.#QNAN",
| "#N/A",
| "N/A",
| "n/a",
| "NA",
| "<NA>",
| "#NA",
| "NULL",
| "null",
| "NaN",
| "nan",
| "-NaN",
| "-nan",
| "#N/A N/A",
| "",
| "None",
| }
| assert _NA_VALUES == STR_NA_VALUES
|
| parser = all_parsers
| nv = len(_NA_VALUES)
|
| def f(i, v):
| if i == 0:
| buf = ""
| elif i > 0:
| buf = "".join([","] * i)
|
| buf = f"{buf}{v}"
|
| if i < nv - 1:
| joined = "".join([","] * (nv - i - 1))
| buf = f"{buf}{joined}"
|
| return buf
|
| data = StringIO("\n".join([f(i, v) for i, v in enumerate(_NA_VALUES)]))
| expected = DataFrame(np.nan, columns=range(nv), index=range(nv))
|
| result = parser.read_csv(data, header=None)
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| @pytest.mark.parametrize("na_values", ["baz", ["baz"]])
| def test_custom_na_values(all_parsers, na_values):
| parser = all_parsers
| data = """A,B,C
| ignore,this,row
| 1,NA,3
| -1.#IND,5,baz
| 7,8,NaN
| """
| expected = DataFrame(
| [[1.0, np.nan, 3], [np.nan, 5, np.nan], [7, 8, np.nan]], columns=["A", "B", "C"]
| )
| result = parser.read_csv(StringIO(data), na_values=na_values, skiprows=[1])
| tm.assert_frame_equal(result, expected)
|
|
| def test_bool_na_values(all_parsers):
| data = """A,B,C
| True,False,True
| NA,True,False
| False,NA,True"""
| parser = all_parsers
| result = parser.read_csv(StringIO(data))
| expected = DataFrame(
| {
| "A": np.array([True, np.nan, False], dtype=object),
| "B": np.array([False, True, np.nan], dtype=object),
| "C": [True, False, True],
| }
| )
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| def test_na_value_dict(all_parsers):
| data = """A,B,C
| foo,bar,NA
| bar,foo,foo
| foo,bar,NA
| bar,foo,foo"""
| parser = all_parsers
| df = parser.read_csv(StringIO(data), na_values={"A": ["foo"], "B": ["bar"]})
| expected = DataFrame(
| {
| "A": [np.nan, "bar", np.nan, "bar"],
| "B": [np.nan, "foo", np.nan, "foo"],
| "C": [np.nan, "foo", np.nan, "foo"],
| }
| )
| tm.assert_frame_equal(df, expected)
|
|
| @skip_pyarrow
| @pytest.mark.parametrize(
| "index_col,expected",
| [
| (
| [0],
| DataFrame({"b": [np.nan], "c": [1], "d": [5]}, index=Index([0], name="a")),
| ),
| (
| [0, 2],
| DataFrame(
| {"b": [np.nan], "d": [5]},
| index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]),
| ),
| ),
| (
| ["a", "c"],
| DataFrame(
| {"b": [np.nan], "d": [5]},
| index=MultiIndex.from_tuples([(0, 1)], names=["a", "c"]),
| ),
| ),
| ],
| )
| def test_na_value_dict_multi_index(all_parsers, index_col, expected):
| data = """\
| a,b,c,d
| 0,NA,1,5
| """
| parser = all_parsers
| result = parser.read_csv(StringIO(data), na_values=set(), index_col=index_col)
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| @pytest.mark.parametrize(
| "kwargs,expected",
| [
| (
| {},
| DataFrame(
| {
| "A": ["a", "b", np.nan, "d", "e", np.nan, "g"],
| "B": [1, 2, 3, 4, 5, 6, 7],
| "C": ["one", "two", "three", np.nan, "five", np.nan, "seven"],
| }
| ),
| ),
| (
| {"na_values": {"A": [], "C": []}, "keep_default_na": False},
| DataFrame(
| {
| "A": ["a", "b", "", "d", "e", "nan", "g"],
| "B": [1, 2, 3, 4, 5, 6, 7],
| "C": ["one", "two", "three", "nan", "five", "", "seven"],
| }
| ),
| ),
| (
| {"na_values": ["a"], "keep_default_na": False},
| DataFrame(
| {
| "A": [np.nan, "b", "", "d", "e", "nan", "g"],
| "B": [1, 2, 3, 4, 5, 6, 7],
| "C": ["one", "two", "three", "nan", "five", "", "seven"],
| }
| ),
| ),
| (
| {"na_values": {"A": [], "C": []}},
| DataFrame(
| {
| "A": ["a", "b", np.nan, "d", "e", np.nan, "g"],
| "B": [1, 2, 3, 4, 5, 6, 7],
| "C": ["one", "two", "three", np.nan, "five", np.nan, "seven"],
| }
| ),
| ),
| ],
| )
| def test_na_values_keep_default(all_parsers, kwargs, expected):
| data = """\
| A,B,C
| a,1,one
| b,2,two
| ,3,three
| d,4,nan
| e,5,five
| nan,6,
| g,7,seven
| """
| parser = all_parsers
| result = parser.read_csv(StringIO(data), **kwargs)
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| def test_no_na_values_no_keep_default(all_parsers):
| # see gh-4318: passing na_values=None and
| # keep_default_na=False yields 'None" as a na_value
| data = """\
| A,B,C
| a,1,None
| b,2,two
| ,3,None
| d,4,nan
| e,5,five
| nan,6,
| g,7,seven
| """
| parser = all_parsers
| result = parser.read_csv(StringIO(data), keep_default_na=False)
|
| expected = DataFrame(
| {
| "A": ["a", "b", "", "d", "e", "nan", "g"],
| "B": [1, 2, 3, 4, 5, 6, 7],
| "C": ["None", "two", "None", "nan", "five", "", "seven"],
| }
| )
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| def test_no_keep_default_na_dict_na_values(all_parsers):
| # see gh-19227
| data = "a,b\n,2"
| parser = all_parsers
| result = parser.read_csv(
| StringIO(data), na_values={"b": ["2"]}, keep_default_na=False
| )
| expected = DataFrame({"a": [""], "b": [np.nan]})
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| def test_no_keep_default_na_dict_na_scalar_values(all_parsers):
| # see gh-19227
| #
| # Scalar values shouldn't cause the parsing to crash or fail.
| data = "a,b\n1,2"
| parser = all_parsers
| df = parser.read_csv(StringIO(data), na_values={"b": 2}, keep_default_na=False)
| expected = DataFrame({"a": [1], "b": [np.nan]})
| tm.assert_frame_equal(df, expected)
|
|
| @skip_pyarrow
| @pytest.mark.parametrize("col_zero_na_values", [113125, "113125"])
| def test_no_keep_default_na_dict_na_values_diff_reprs(all_parsers, col_zero_na_values):
| # see gh-19227
| data = """\
| 113125,"blah","/blaha",kjsdkj,412.166,225.874,214.008
| 729639,"qwer","",asdfkj,466.681,,252.373
| """
| parser = all_parsers
| expected = DataFrame(
| {
| 0: [np.nan, 729639.0],
| 1: [np.nan, "qwer"],
| 2: ["/blaha", np.nan],
| 3: ["kjsdkj", "asdfkj"],
| 4: [412.166, 466.681],
| 5: ["225.874", ""],
| 6: [np.nan, 252.373],
| }
| )
|
| result = parser.read_csv(
| StringIO(data),
| header=None,
| keep_default_na=False,
| na_values={2: "", 6: "214.008", 1: "blah", 0: col_zero_na_values},
| )
| tm.assert_frame_equal(result, expected)
|
|
| @skip_pyarrow
| @pytest.mark.parametrize(
| "na_filter,row_data",
| [
| (True, [[1, "A"], [np.nan, np.nan], [3, "C"]]),
| (False, [["1", "A"], ["nan", "B"], ["3", "C"]]),
| ],
| )
| def test_na_values_na_filter_override(all_parsers, na_filter, row_data):
| data = """\
| 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)
|
|
