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Jun 28, 2018
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TST: Use fixtures in dtypes/test_cast.py #21661

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12 changes: 12 additions & 0 deletions pandas/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -245,6 +245,18 @@ def float_dtype(request):
return request.param


@pytest.fixture(params=["complex64", "complex128"])
def complex_dtype(request):
"""
Parameterized fixture for complex dtypes.

* complex64
* complex128
"""

return request.param


UNSIGNED_INT_DTYPES = ["uint8", "uint16", "uint32", "uint64"]
SIGNED_INT_DTYPES = ["int8", "int16", "int32", "int64"]
ALL_INT_DTYPES = UNSIGNED_INT_DTYPES + SIGNED_INT_DTYPES
Expand Down
249 changes: 126 additions & 123 deletions pandas/tests/dtypes/test_cast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@

class TestMaybeDowncast(object):

def test_downcast_conv(self):
def test_downcast(self):
# test downcasting

arr = np.array([8.5, 8.6, 8.7, 8.8, 8.9999999999995])
Expand All @@ -53,33 +53,34 @@ def test_downcast_conv(self):
expected = np.array([8, 8, 8, 8, 9], dtype=np.int64)
tm.assert_numpy_array_equal(result, expected)

# GH16875 coercing of bools
# see gh-16875: coercing of booleans.
ser = Series([True, True, False])
result = maybe_downcast_to_dtype(ser, np.dtype(np.float64))
expected = ser
tm.assert_series_equal(result, expected)

# conversions

@pytest.mark.parametrize("dtype", [np.float64, object, np.int64])
def test_downcast_conversion_no_nan(self, dtype):
expected = np.array([1, 2])
for dtype in [np.float64, object, np.int64]:
arr = np.array([1.0, 2.0], dtype=dtype)
result = maybe_downcast_to_dtype(arr, 'infer')
tm.assert_almost_equal(result, expected, check_dtype=False)

for dtype in [np.float64, object]:
expected = np.array([1.0, 2.0, np.nan], dtype=dtype)
arr = np.array([1.0, 2.0, np.nan], dtype=dtype)
result = maybe_downcast_to_dtype(arr, 'infer')
tm.assert_almost_equal(result, expected)

# empties
for dtype in [np.int32, np.float64, np.float32, np.bool_,
np.int64, object]:
arr = np.array([], dtype=dtype)
result = maybe_downcast_to_dtype(arr, 'int64')
tm.assert_almost_equal(result, np.array([], dtype=np.int64))
assert result.dtype == np.int64
arr = np.array([1.0, 2.0], dtype=dtype)

result = maybe_downcast_to_dtype(arr, "infer")
tm.assert_almost_equal(result, expected, check_dtype=False)

@pytest.mark.parametrize("dtype", [np.float64, object])
def test_downcast_conversion_nan(self, dtype):
expected = np.array([1.0, 2.0, np.nan], dtype=dtype)
arr = np.array([1.0, 2.0, np.nan], dtype=dtype)

result = maybe_downcast_to_dtype(arr, "infer")
tm.assert_almost_equal(result, expected)

@pytest.mark.parametrize("dtype", [np.int32, np.float64, np.float32,
np.bool_, np.int64, object])
def test_downcast_conversion_empty(self, dtype):
arr = np.array([], dtype=dtype)
result = maybe_downcast_to_dtype(arr, "int64")
tm.assert_numpy_array_equal(result, np.array([], dtype=np.int64))

def test_datetimelikes_nan(self):
arr = np.array([1, 2, np.nan])
Expand All @@ -104,66 +105,71 @@ def test_datetime_with_timezone(self):

class TestInferDtype(object):

def testinfer_dtype_from_scalar(self):
# Test that infer_dtype_from_scalar is returning correct dtype for int
# and float.
def test_infer_dtype_from_int_scalar(self, any_int_dtype):
# Test that infer_dtype_from_scalar is
# returning correct dtype for int and float.
data = np.dtype(any_int_dtype).type(12)
dtype, val = infer_dtype_from_scalar(data)
assert dtype == type(data)

def test_infer_dtype_from_float_scalar(self, float_dtype):
float_dtype = np.dtype(float_dtype).type
data = float_dtype(12)

for dtypec in [np.uint8, np.int8, np.uint16, np.int16, np.uint32,
np.int32, np.uint64, np.int64]:
data = dtypec(12)
dtype, val = infer_dtype_from_scalar(data)
assert dtype == type(data)
dtype, val = infer_dtype_from_scalar(data)
assert dtype == float_dtype

def test_infer_dtype_from_python_scalar(self):
data = 12
dtype, val = infer_dtype_from_scalar(data)
assert dtype == np.int64

for dtypec in [np.float16, np.float32, np.float64]:
data = dtypec(12)
dtype, val = infer_dtype_from_scalar(data)
assert dtype == dtypec

data = np.float(12)
dtype, val = infer_dtype_from_scalar(data)
assert dtype == np.float64

for data in [True, False]:
dtype, val = infer_dtype_from_scalar(data)
assert dtype == np.bool_
@pytest.mark.parametrize("bool_val", [True, False])
def test_infer_dtype_from_boolean(self, bool_val):
dtype, val = infer_dtype_from_scalar(bool_val)
assert dtype == np.bool_

for data in [np.complex64(1), np.complex128(1)]:
dtype, val = infer_dtype_from_scalar(data)
assert dtype == np.complex_
def test_infer_dtype_from_complex(self, complex_dtype):
data = np.dtype(complex_dtype).type(1)
dtype, val = infer_dtype_from_scalar(data)
assert dtype == np.complex_

for data in [np.datetime64(1, 'ns'), Timestamp(1),
datetime(2000, 1, 1, 0, 0)]:
dtype, val = infer_dtype_from_scalar(data)
assert dtype == 'M8[ns]'
@pytest.mark.parametrize("data", [np.datetime64(1, "ns"), Timestamp(1),
datetime(2000, 1, 1, 0, 0)])
def test_infer_dtype_from_datetime(self, data):
dtype, val = infer_dtype_from_scalar(data)
assert dtype == "M8[ns]"

for data in [np.timedelta64(1, 'ns'), Timedelta(1),
timedelta(1)]:
dtype, val = infer_dtype_from_scalar(data)
assert dtype == 'm8[ns]'
@pytest.mark.parametrize("data", [np.timedelta64(1, "ns"), Timedelta(1),
timedelta(1)])
def test_infer_dtype_from_timedelta(self, data):
dtype, val = infer_dtype_from_scalar(data)
assert dtype == "m8[ns]"

for freq in ['M', 'D']:
p = Period('2011-01-01', freq=freq)
dtype, val = infer_dtype_from_scalar(p, pandas_dtype=True)
assert dtype == 'period[{0}]'.format(freq)
assert val == p.ordinal
@pytest.mark.parametrize("freq", ["M", "D"])
def test_infer_dtype_from_period(self, freq):
p = Period("2011-01-01", freq=freq)
dtype, val = infer_dtype_from_scalar(p, pandas_dtype=True)

dtype, val = infer_dtype_from_scalar(p)
dtype == np.object_
assert val == p
assert dtype == "period[{0}]".format(freq)
assert val == p.ordinal

# misc
for data in [date(2000, 1, 1),
Timestamp(1, tz='US/Eastern'), 'foo']:
dtype, val = infer_dtype_from_scalar(p)
assert dtype == np.object_
assert val == p

dtype, val = infer_dtype_from_scalar(data)
assert dtype == np.object_
@pytest.mark.parametrize("data", [date(2000, 1, 1), "foo",
Timestamp(1, tz="US/Eastern")])
def test_infer_dtype_misc(self, data):
dtype, val = infer_dtype_from_scalar(data)
assert dtype == np.object_

@pytest.mark.parametrize('tz', ['UTC', 'US/Eastern', 'Asia/Tokyo'])
def testinfer_from_scalar_tz(self, tz):
def test_infer_from_scalar_tz(self, tz):
dt = Timestamp(1, tz=tz)
dtype, val = infer_dtype_from_scalar(dt, pandas_dtype=True)
assert dtype == 'datetime64[ns, {0}]'.format(tz)
Expand All @@ -173,7 +179,7 @@ def testinfer_from_scalar_tz(self, tz):
assert dtype == np.object_
assert val == dt

def testinfer_dtype_from_scalar_errors(self):
def test_infer_dtype_from_scalar_errors(self):
with pytest.raises(ValueError):
infer_dtype_from_scalar(np.array([1]))

Expand Down Expand Up @@ -329,66 +335,63 @@ def test_maybe_convert_objects_copy(self):

class TestCommonTypes(object):

def test_numpy_dtypes(self):
# (source_types, destination_type)
testcases = (
# identity
((np.int64,), np.int64),
((np.uint64,), np.uint64),
((np.float32,), np.float32),
((np.object,), np.object),

# into ints
((np.int16, np.int64), np.int64),
((np.int32, np.uint32), np.int64),
((np.uint16, np.uint64), np.uint64),

# into floats
((np.float16, np.float32), np.float32),
((np.float16, np.int16), np.float32),
((np.float32, np.int16), np.float32),
((np.uint64, np.int64), np.float64),
((np.int16, np.float64), np.float64),
((np.float16, np.int64), np.float64),

# into others
((np.complex128, np.int32), np.complex128),
((np.object, np.float32), np.object),
((np.object, np.int16), np.object),

# bool with int
((np.dtype('bool'), np.int64), np.object),
((np.dtype('bool'), np.int32), np.object),
((np.dtype('bool'), np.int16), np.object),
((np.dtype('bool'), np.int8), np.object),
((np.dtype('bool'), np.uint64), np.object),
((np.dtype('bool'), np.uint32), np.object),
((np.dtype('bool'), np.uint16), np.object),
((np.dtype('bool'), np.uint8), np.object),

# bool with float
((np.dtype('bool'), np.float64), np.object),
((np.dtype('bool'), np.float32), np.object),

((np.dtype('datetime64[ns]'), np.dtype('datetime64[ns]')),
np.dtype('datetime64[ns]')),
((np.dtype('timedelta64[ns]'), np.dtype('timedelta64[ns]')),
np.dtype('timedelta64[ns]')),

((np.dtype('datetime64[ns]'), np.dtype('datetime64[ms]')),
np.dtype('datetime64[ns]')),
((np.dtype('timedelta64[ms]'), np.dtype('timedelta64[ns]')),
np.dtype('timedelta64[ns]')),

((np.dtype('datetime64[ns]'), np.dtype('timedelta64[ns]')),
np.object),
((np.dtype('datetime64[ns]'), np.int64), np.object)
)
for src, common in testcases:
assert find_common_type(src) == common

@pytest.mark.parametrize("source_dtypes,expected_common_dtype", [
((np.int64,), np.int64),
((np.uint64,), np.uint64),
((np.float32,), np.float32),
((np.object,), np.object),

# into ints
((np.int16, np.int64), np.int64),
((np.int32, np.uint32), np.int64),
((np.uint16, np.uint64), np.uint64),

# into floats
((np.float16, np.float32), np.float32),
((np.float16, np.int16), np.float32),
((np.float32, np.int16), np.float32),
((np.uint64, np.int64), np.float64),
((np.int16, np.float64), np.float64),
((np.float16, np.int64), np.float64),

# into others
((np.complex128, np.int32), np.complex128),
((np.object, np.float32), np.object),
((np.object, np.int16), np.object),

# bool with int
((np.dtype('bool'), np.int64), np.object),
((np.dtype('bool'), np.int32), np.object),
((np.dtype('bool'), np.int16), np.object),
((np.dtype('bool'), np.int8), np.object),
((np.dtype('bool'), np.uint64), np.object),
((np.dtype('bool'), np.uint32), np.object),
((np.dtype('bool'), np.uint16), np.object),
((np.dtype('bool'), np.uint8), np.object),

# bool with float
((np.dtype('bool'), np.float64), np.object),
((np.dtype('bool'), np.float32), np.object),

((np.dtype('datetime64[ns]'), np.dtype('datetime64[ns]')),
np.dtype('datetime64[ns]')),
((np.dtype('timedelta64[ns]'), np.dtype('timedelta64[ns]')),
np.dtype('timedelta64[ns]')),

((np.dtype('datetime64[ns]'), np.dtype('datetime64[ms]')),
np.dtype('datetime64[ns]')),
((np.dtype('timedelta64[ms]'), np.dtype('timedelta64[ns]')),
np.dtype('timedelta64[ns]')),

((np.dtype('datetime64[ns]'), np.dtype('timedelta64[ns]')),
np.object),
((np.dtype('datetime64[ns]'), np.int64), np.object)
])
def test_numpy_dtypes(self, source_dtypes, expected_common_dtype):
assert find_common_type(source_dtypes) == expected_common_dtype

def test_raises_empty_input(self):
with pytest.raises(ValueError):
# empty
find_common_type([])

def test_categorical_dtype(self):
Expand Down