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Apr 21, 2020
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CLN: Split dtype inference tests #33690

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CLN: Split dtype inference tests
dsaxton Apr 21, 2020
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dsaxton Apr 21, 2020
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dsaxton Apr 21, 2020
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166 changes: 75 additions & 91 deletions pandas/tests/dtypes/test_inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -782,108 +782,91 @@ def test_datetime(self):
index = Index(dates)
assert index.inferred_type == "datetime64"

def test_infer_dtype_datetime(self):

arr = np.array([Timestamp("2011-01-01"), Timestamp("2011-01-02")])
assert lib.infer_dtype(arr, skipna=True) == "datetime"

def test_infer_dtype_datetime64(self):
arr = np.array(
[np.datetime64("2011-01-01"), np.datetime64("2011-01-01")], dtype=object
)
assert lib.infer_dtype(arr, skipna=True) == "datetime64"

arr = np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)])
assert lib.infer_dtype(arr, skipna=True) == "datetime"

@pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
def test_infer_dtype_datetime64_with_na(self, na_value):
# starts with nan
for n in [pd.NaT, np.nan]:
arr = np.array([n, pd.Timestamp("2011-01-02")])
assert lib.infer_dtype(arr, skipna=True) == "datetime"

arr = np.array([n, np.datetime64("2011-01-02")])
assert lib.infer_dtype(arr, skipna=True) == "datetime64"

arr = np.array([n, datetime(2011, 1, 1)])
assert lib.infer_dtype(arr, skipna=True) == "datetime"

arr = np.array([n, pd.Timestamp("2011-01-02"), n])
assert lib.infer_dtype(arr, skipna=True) == "datetime"

arr = np.array([n, np.datetime64("2011-01-02"), n])
assert lib.infer_dtype(arr, skipna=True) == "datetime64"

arr = np.array([n, datetime(2011, 1, 1), n])
assert lib.infer_dtype(arr, skipna=True) == "datetime"
arr = np.array([na_value, np.datetime64("2011-01-02")])
assert lib.infer_dtype(arr, skipna=True) == "datetime64"

# different type of nat
arr = np.array(
[np.timedelta64("nat"), np.datetime64("2011-01-02")], dtype=object
)
assert lib.infer_dtype(arr, skipna=False) == "mixed"
arr = np.array([na_value, np.datetime64("2011-01-02"), na_value])
assert lib.infer_dtype(arr, skipna=True) == "datetime64"

arr = np.array(
[np.datetime64("2011-01-02"), np.timedelta64("nat")], dtype=object
)
@pytest.mark.parametrize(
"arr",
[
np.array(
[np.timedelta64("nat"), np.datetime64("2011-01-02")], dtype=object
),
np.array(
[np.datetime64("2011-01-02"), np.timedelta64("nat")], dtype=object
),
np.array([np.datetime64("2011-01-01"), pd.Timestamp("2011-01-02")]),
np.array([pd.Timestamp("2011-01-02"), np.datetime64("2011-01-01")]),
np.array([np.nan, pd.Timestamp("2011-01-02"), 1.1]),
np.array([np.nan, "2011-01-01", pd.Timestamp("2011-01-02")]),
np.array([np.datetime64("nat"), np.timedelta64(1, "D")], dtype=object),
np.array([np.timedelta64(1, "D"), np.datetime64("nat")], dtype=object),
],
)
def test_infer_datetimelike_dtype_mixed(self, arr):
assert lib.infer_dtype(arr, skipna=False) == "mixed"

# mixed datetime
arr = np.array([datetime(2011, 1, 1), pd.Timestamp("2011-01-02")])
assert lib.infer_dtype(arr, skipna=True) == "datetime"

# should be datetime?
arr = np.array([np.datetime64("2011-01-01"), pd.Timestamp("2011-01-02")])
assert lib.infer_dtype(arr, skipna=True) == "mixed"

arr = np.array([pd.Timestamp("2011-01-02"), np.datetime64("2011-01-01")])
assert lib.infer_dtype(arr, skipna=True) == "mixed"

def test_infer_dtype_mixed_integer(self):
arr = np.array([np.nan, pd.Timestamp("2011-01-02"), 1])
assert lib.infer_dtype(arr, skipna=True) == "mixed-integer"

arr = np.array([np.nan, pd.Timestamp("2011-01-02"), 1.1])
assert lib.infer_dtype(arr, skipna=True) == "mixed"
@pytest.mark.parametrize(
"arr",
[
np.array([Timestamp("2011-01-01"), Timestamp("2011-01-02")]),
np.array([datetime(2011, 1, 1), datetime(2012, 2, 1)]),
np.array([datetime(2011, 1, 1), pd.Timestamp("2011-01-02")]),
],
)
def test_infer_dtype_datetime(self, arr):
assert lib.infer_dtype(arr, skipna=True) == "datetime"

arr = np.array([np.nan, "2011-01-01", pd.Timestamp("2011-01-02")])
assert lib.infer_dtype(arr, skipna=True) == "mixed"
@pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
@pytest.mark.parametrize(
"time_stamp", [pd.Timestamp("2011-01-01"), datetime(2011, 1, 1)]
)
def test_infer_dtype_datetime_with_na(self, na_value, time_stamp):
# starts with nan
arr = np.array([na_value, time_stamp])
assert lib.infer_dtype(arr, skipna=True) == "datetime"

def test_infer_dtype_timedelta(self):
arr = np.array([na_value, time_stamp, na_value])
assert lib.infer_dtype(arr, skipna=True) == "datetime"

arr = np.array([pd.Timedelta("1 days"), pd.Timedelta("2 days")])
@pytest.mark.parametrize(
"arr",
[
np.array([pd.Timedelta("1 days"), pd.Timedelta("2 days")]),
np.array([np.timedelta64(1, "D"), np.timedelta64(2, "D")], dtype=object),
np.array([timedelta(1), timedelta(2)]),
],
)
def test_infer_dtype_timedelta(self, arr):
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

arr = np.array([np.timedelta64(1, "D"), np.timedelta64(2, "D")], dtype=object)
@pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
@pytest.mark.parametrize(
"delta", [Timedelta("1 days"), np.timedelta64(1, "D"), timedelta(1)]
)
def test_infer_dtype_timedelta_with_na(self, na_value, delta):
# starts with nan
arr = np.array([na_value, delta])
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

arr = np.array([timedelta(1), timedelta(2)])
arr = np.array([na_value, delta, na_value])
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

# starts with nan
for n in [pd.NaT, np.nan]:
arr = np.array([n, Timedelta("1 days")])
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

arr = np.array([n, np.timedelta64(1, "D")])
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

arr = np.array([n, timedelta(1)])
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

arr = np.array([n, pd.Timedelta("1 days"), n])
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

arr = np.array([n, np.timedelta64(1, "D"), n])
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

arr = np.array([n, timedelta(1), n])
assert lib.infer_dtype(arr, skipna=True) == "timedelta"

# different type of nat
arr = np.array([np.datetime64("nat"), np.timedelta64(1, "D")], dtype=object)
assert lib.infer_dtype(arr, skipna=False) == "mixed"

arr = np.array([np.timedelta64(1, "D"), np.datetime64("nat")], dtype=object)
assert lib.infer_dtype(arr, skipna=False) == "mixed"

def test_infer_dtype_period(self):
# GH 13664
arr = np.array([pd.Period("2011-01", freq="D"), pd.Period("2011-02", freq="D")])
Expand All @@ -892,25 +875,26 @@ def test_infer_dtype_period(self):
arr = np.array([pd.Period("2011-01", freq="D"), pd.Period("2011-02", freq="M")])
assert lib.infer_dtype(arr, skipna=True) == "period"

# starts with nan
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IIRC this comment was related to needing a test specifically for the NaN coming before the period. can you add a comment in the appropriate place to that effect

for n in [pd.NaT, np.nan]:
arr = np.array([n, pd.Period("2011-01", freq="D")])
assert lib.infer_dtype(arr, skipna=True) == "period"

arr = np.array([n, pd.Period("2011-01", freq="D"), n])
assert lib.infer_dtype(arr, skipna=True) == "period"

# different type of nat
def test_infer_dtype_period_mixed(self):
arr = np.array(
[np.datetime64("nat"), pd.Period("2011-01", freq="M")], dtype=object
[pd.Period("2011-01", freq="M"), np.datetime64("nat")], dtype=object
)
assert lib.infer_dtype(arr, skipna=False) == "mixed"

arr = np.array(
[pd.Period("2011-01", freq="M"), np.datetime64("nat")], dtype=object
[np.datetime64("nat"), pd.Period("2011-01", freq="M")], dtype=object
)
assert lib.infer_dtype(arr, skipna=False) == "mixed"

@pytest.mark.parametrize("na_value", [pd.NaT, np.nan])
def test_infer_dtype_period_with_na(self, na_value):
# starts with nan
arr = np.array([na_value, pd.Period("2011-01", freq="D")])
assert lib.infer_dtype(arr, skipna=True) == "period"

arr = np.array([na_value, pd.Period("2011-01", freq="D"), na_value])
assert lib.infer_dtype(arr, skipna=True) == "period"

@pytest.mark.parametrize(
"data",
[
Expand Down