def test_date_columns_equal_with_ignore_spaces_and_case():
data = """a|b|expected
2017-01-01|2017-01-01 |True
2017-01-02 |2017-01-02|True
2017-10-01 |2017-10-10 |False
2017-01-01||False
|2017-01-01|False
||True"""
df = pd.read_csv(six.StringIO(data), sep="|")
# First compare just the strings
actual_out = datacompy.columns_equal(
df.a, df.b, rel_tol=0.2, ignore_spaces=True, ignore_case=True
)
expect_out = df["expected"]
assert_series_equal(expect_out, actual_out, check_names=False)
# Then compare converted to datetime objects
df["a"] = pd.to_datetime(df["a"])
df["b"] = pd.to_datetime(df["b"])
actual_out = datacompy.columns_equal(df.a, df.b, rel_tol=0.2, ignore_spaces=True)
expect_out = df["expected"]
assert_series_equal(expect_out, actual_out, check_names=False)
# and reverse
actual_out_rev = datacompy.columns_equal(df.b, df.a, rel_tol=0.2, ignore_spaces=True)
assert_series_equal(expect_out, actual_out_rev, check_names=False)
def test_mixed_column():
df = pd.DataFrame([{
'a': 'hi',
'b': 'hi',
'expected': True
}, {
'a': 1,
'b': 1,
'expected': True
}, {
'a': np.inf,
'b': np.inf,
'expected': True
}, {
'a': Decimal('1'),
'b': Decimal('1'),
'expected': True
}, {
'a': 1,
'b': '1',
'expected': False
}, {
'a': 1,
'b': 'yo',
'expected': False
}])
actual_out = datacompy.columns_equal(df.a, df.b)
expect_out = df['expected']
assert_series_equal(expect_out, actual_out, check_names=False)
def test_rounded_date_columns():
"""If strings can't be coerced into dates then it should be false for the
whole column.
"""
df = pd.DataFrame([
{
"a": "2017-01-01",
"b": "2017-01-01 00:00:00.000000",
"exp": True
},
{
"a": "2017-01-01",
"b": "2017-01-01 00:00:00.123456",
"exp": False
},
{
"a": "2017-01-01",
"b": "2017-01-01 00:00:01.000000",
"exp": False
},
{
"a": "2017-01-01",
"b": "2017-01-01 00:00:00",
"exp": True
},
])
df["a_dt"] = pd.to_datetime(df["a"])
actual = datacompy.columns_equal(df.a_dt, df.b)
expected = df["exp"]
assert_series_equal(actual, expected, check_names=False)
def test_infinity_and_beyond():
df = pd.DataFrame([{
'a': np.inf,
'b': np.inf,
'expected': True
}, {
'a': -np.inf,
'b': -np.inf,
'expected': True
}, {
'a': -np.inf,
'b': np.inf,
'expected': False
}, {
'a': np.inf,
'b': -np.inf,
'expected': False
}, {
'a': 1,
'b': 1,
'expected': True
}, {
'a': 1,
'b': 0,
'expected': False
}])
actual_out = datacompy.columns_equal(df.a, df.b)
expect_out = df['expected']
assert_series_equal(expect_out, actual_out, check_names=False)
def test_rounded_date_columns():
"""If strings can't be coerced into dates then it should be false for the
whole column.
"""
df = pd.DataFrame([{
'a': '2017-01-01',
'b': '2017-01-01 00:00:00.000000',
'exp': True
}, {
'a': '2017-01-01',
'b': '2017-01-01 00:00:00.123456',
'exp': False
}, {
'a': '2017-01-01',
'b': '2017-01-01 00:00:01.000000',
'exp': False
}, {
'a': '2017-01-01',
'b': '2017-01-01 00:00:00',
'exp': True
}])
df['a_dt'] = pd.to_datetime(df['a'])
actual = datacompy.columns_equal(df.a_dt, df.b)
expected = df['exp']
assert_series_equal(actual, expected, check_names=False)
def test_bad_date_columns():
"""If strings can't be coerced into dates then it should be false for the
whole column.
"""
df = pd.DataFrame(
[{"a": "2017-01-01", "b": "2017-01-01"}, {"a": "2017-01-01", "b": "217-01-01"}]
)
df["a_dt"] = pd.to_datetime(df["a"])
assert not datacompy.columns_equal(df.a_dt, df.b).any()
def test_mixed_column_with_ignore_spaces_and_case():
df = pd.DataFrame([
{
"a": "hi",
"b": "hi ",
"expected": True
},
{
"a": 1,
"b": 1,
"expected": True
},
{
"a": np.inf,
"b": np.inf,
"expected": True
},
{
"a": Decimal("1"),
"b": Decimal("1"),
"expected": True
},
{
"a": 1,
"b": "1 ",
"expected": False
},
{
"a": 1,
"b": "yo ",
"expected": False
},
{
"a": "Hi",
"b": "hI ",
"expected": True
},
{
"a": "HI",
"b": "HI ",
"expected": True
},
{
"a": "hi",
"b": "hi ",
"expected": True
},
])
actual_out = datacompy.columns_equal(df.a,
df.b,
ignore_spaces=True,
ignore_case=True)
expect_out = df["expected"]
assert_series_equal(expect_out, actual_out, check_names=False)
def test_numeric_columns_equal_abs():
data = '''a|b|expected
1|1|True
2|2.1|True
3|4|False
4|NULL|False
NULL|4|False
NULL|NULL|True'''
df = pd.read_csv(six.StringIO(data), sep='|')
actual_out = datacompy.columns_equal(df.a, df.b, abs_tol=0.2)
expect_out = df['expected']
assert_series_equal(expect_out, actual_out, check_names=False)
def test_numeric_columns_equal_rel():
data = """a|b|expected
1|1|True
2|2.1|True
3|4|False
4|NULL|False
NULL|4|False
NULL|NULL|True"""
df = pd.read_csv(six.StringIO(data), sep="|")
actual_out = datacompy.columns_equal(df.a, df.b, rel_tol=0.2)
expect_out = df["expected"]
assert_series_equal(expect_out, actual_out, check_names=False)
def test_date_columns_unequal():
"""I want datetime fields to match with dates stored as strings
"""
df = pd.DataFrame([{"a": "2017-01-01", "b": "2017-01-02"}, {"a": "2017-01-01"}])
df["a_dt"] = pd.to_datetime(df["a"])
df["b_dt"] = pd.to_datetime(df["b"])
assert datacompy.columns_equal(df.a, df.a_dt).all()
assert datacompy.columns_equal(df.b, df.b_dt).all()
assert datacompy.columns_equal(df.a_dt, df.a).all()
assert datacompy.columns_equal(df.b_dt, df.b).all()
assert not datacompy.columns_equal(df.b_dt, df.a).any()
assert not datacompy.columns_equal(df.a_dt, df.b).any()
assert not datacompy.columns_equal(df.a, df.b_dt).any()
assert not datacompy.columns_equal(df.b, df.a_dt).any()
def test_bad_date_columns():
"""If strings can't be coerced into dates then it should be false for the
whole column.
"""
df = pd.DataFrame([{
'a': '2017-01-01',
'b': '2017-01-01'
}, {
'a': '2017-01-01',
'b': '217-01-01'
}])
df['a_dt'] = pd.to_datetime(df['a'])
assert not datacompy.columns_equal(df.a_dt, df.b).any()
def test_date_columns_equal():
data = '''a|b|expected
2017-01-01|2017-01-01|True
2017-01-02|2017-01-02|True
2017-10-01|2017-10-10|False
2017-01-01||False
|2017-01-01|False
||True'''
df = pd.read_csv(six.StringIO(data), sep='|')
#First compare just the strings
actual_out = datacompy.columns_equal(df.a, df.b, rel_tol=0.2)
expect_out = df['expected']
assert_series_equal(expect_out, actual_out, check_names=False)
#Then compare converted to datetime objects
df['a'] = pd.to_datetime(df['a'])
df['b'] = pd.to_datetime(df['b'])
actual_out = datacompy.columns_equal(df.a, df.b, rel_tol=0.2)
expect_out = df['expected']
assert_series_equal(expect_out, actual_out, check_names=False)
#and reverse
actual_out_rev = datacompy.columns_equal(df.b, df.a, rel_tol=0.2)
assert_series_equal(expect_out, actual_out_rev, check_names=False)
def test_mixed_column():
df = pd.DataFrame(
[
{"a": "hi", "b": "hi", "expected": True},
{"a": 1, "b": 1, "expected": True},
{"a": np.inf, "b": np.inf, "expected": True},
{"a": Decimal("1"), "b": Decimal("1"), "expected": True},
{"a": 1, "b": "1", "expected": False},
{"a": 1, "b": "yo", "expected": False},
]
)
actual_out = datacompy.columns_equal(df.a, df.b)
expect_out = df["expected"]
assert_series_equal(expect_out, actual_out, check_names=False)
def test_infinity_and_beyond():
df = pd.DataFrame(
[
{"a": np.inf, "b": np.inf, "expected": True},
{"a": -np.inf, "b": -np.inf, "expected": True},
{"a": -np.inf, "b": np.inf, "expected": False},
{"a": np.inf, "b": -np.inf, "expected": False},
{"a": 1, "b": 1, "expected": True},
{"a": 1, "b": 0, "expected": False},
]
)
actual_out = datacompy.columns_equal(df.a, df.b)
expect_out = df["expected"]
assert_series_equal(expect_out, actual_out, check_names=False)
def test_decimal_columns_equal_rel():
df = pd.DataFrame(
[
{"a": Decimal("1"), "b": Decimal("1"), "expected": True},
{"a": Decimal("1.3"), "b": Decimal("1.3"), "expected": True},
{"a": Decimal("1.000003"), "b": Decimal("1.000003"), "expected": True},
{"a": Decimal("1.000000004"), "b": Decimal("1.000000003"), "expected": True},
{"a": Decimal("1.3"), "b": Decimal("1.2"), "expected": False},
{"a": np.nan, "b": np.nan, "expected": True},
{"a": np.nan, "b": Decimal("1"), "expected": False},
{"a": Decimal("1"), "b": np.nan, "expected": False},
]
)
actual_out = datacompy.columns_equal(df.a, df.b, abs_tol=0.001)
expect_out = df["expected"]
assert_series_equal(expect_out, actual_out, check_names=False)
def test_date_columns_unequal():
"""I want datetime fields to match with dates stored as strings
"""
df = pd.DataFrame([{
'a': '2017-01-01',
'b': '2017-01-02'
}, {
'a': '2017-01-01'
}])
df['a_dt'] = pd.to_datetime(df['a'])
df['b_dt'] = pd.to_datetime(df['b'])
assert datacompy.columns_equal(df.a, df.a_dt).all()
assert datacompy.columns_equal(df.b, df.b_dt).all()
assert datacompy.columns_equal(df.a_dt, df.a).all()
assert datacompy.columns_equal(df.b_dt, df.b).all()
assert not datacompy.columns_equal(df.b_dt, df.a).any()
assert not datacompy.columns_equal(df.a_dt, df.b).any()
assert not datacompy.columns_equal(df.a, df.b_dt).any()
assert not datacompy.columns_equal(df.b, df.a_dt).any()
def test_string_columns_equal_with_ignore_spaces():
data = """a|b|expected
Hi|Hi|True
Yo|Yo|True
Hey|Hey |True
résumé|resume|False
résumé|résumé|True
💩|💩|True
💩|🤔|False
| |True
| |True
datacompy|DataComPy|False
something||False
|something|False
||True"""
df = pd.read_csv(six.StringIO(data), sep="|")
actual_out = datacompy.columns_equal(df.a, df.b, rel_tol=0.2, ignore_spaces=True)
expect_out = df["expected"]
assert_series_equal(expect_out, actual_out, check_names=False)
def test_string_columns_equal():
data = '''a|b|expected
Hi|Hi|True
Yo|Yo|True
Hey|Hey |False
résumé|resume|False
résumé|résumé|True
💩|💩|True
💩|🤔|False
| |True
| |False
datacompy|DataComPy|False
something||False
|something|False
||True'''
df = pd.read_csv(six.StringIO(data), sep='|')
actual_out = datacompy.columns_equal(df.a, df.b, rel_tol=0.2)
expect_out = df['expected']
assert_series_equal(expect_out, actual_out, check_names=False)
def test_decimal_columns_equal_rel():
df = pd.DataFrame([{
'a': Decimal('1'),
'b': Decimal('1'),
'expected': True
}, {
'a': Decimal('1.3'),
'b': Decimal('1.3'),
'expected': True
}, {
'a': Decimal('1.000003'),
'b': Decimal('1.000003'),
'expected': True
}, {
'a': Decimal('1.000000004'),
'b': Decimal('1.000000003'),
'expected': True
}, {
'a': Decimal('1.3'),
'b': Decimal('1.2'),
'expected': False
}, {
'a': np.nan,
'b': np.nan,
'expected': True
}, {
'a': np.nan,
'b': Decimal('1'),
'expected': False
}, {
'a': Decimal('1'),
'b': np.nan,
'expected': False
}])
actual_out = datacompy.columns_equal(df.a, df.b, abs_tol=0.001)
expect_out = df['expected']
assert_series_equal(expect_out, actual_out, check_names=False)
