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Merged
merged 7 commits into from
Jun 28, 2019
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Fixturize tests/frame/test_dtypes.py #25636

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5eef290
Fixturize tests/frame/test_dtypes.py
h-vetinari Mar 10, 2019
30c5532
Review (jreback)
h-vetinari Mar 10, 2019
ed047dc
Merge remote-tracking branch 'upstream/master' into fixturize_frame_d…
h-vetinari Mar 11, 2019
f828727
Break out _check_cast into module function (review jreback)
h-vetinari Mar 11, 2019
601f2a0
Merge remote-tracking branch 'upstream/master' into fixturize_frame_d…
h-vetinari Mar 20, 2019
1321d9d
Merge remote-tracking branch 'upstream/master' into fixturize_frame_d…
h-vetinari Jun 28, 2019
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lint
h-vetinari Jun 28, 2019
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12 changes: 12 additions & 0 deletions pandas/tests/frame/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -110,6 +110,18 @@ def mixed_int_frame():
return df


@pytest.fixture
def mixed_type_frame():
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so this fixtures itself is prob ok here. as we know that this is used in many places.

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on 2nd thought, let's see how often this is actually used, so move into the test module

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I'll reiterate from another thread, that with these fixturization PRs, I will:

"""
Fixture for DataFrame of float/int/string columns with RangeIndex
Columns are ['a', 'b', 'c', 'float32', 'int32'].
"""
return DataFrame({'a': 1., 'b': 2, 'c': 'foo',
'float32': np.array([1.] * 10, dtype='float32'),
'int32': np.array([1] * 10, dtype='int32')},
index=np.arange(10))


@pytest.fixture
def timezone_frame():
"""
Expand Down
94 changes: 48 additions & 46 deletions pandas/tests/frame/test_dtypes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,6 @@
Categorical, DataFrame, Series, Timedelta, Timestamp,
_np_version_under1p14, compat, concat, date_range, option_context)
from pandas.core.arrays import integer_array
from pandas.tests.frame.common import TestData
import pandas.util.testing as tm
from pandas.util.testing import (
assert_frame_equal, assert_series_equal, makeCustomDataframe as mkdf)
Expand All @@ -28,7 +27,7 @@ def text_dtype(request):
return request.param


class TestDataFrameDataTypes(TestData):
class TestDataFrameDataTypes():

def test_concat_empty_dataframe_dtypes(self):
df = DataFrame(columns=list("abc"))
Expand Down Expand Up @@ -396,11 +395,11 @@ def test_select_dtypes_typecodes(self):
FLOAT_TYPES = list(np.typecodes['AllFloat'])
assert_frame_equal(df.select_dtypes(FLOAT_TYPES), expected)

def test_dtypes_gh8722(self):
self.mixed_frame['bool'] = self.mixed_frame['A'] > 0
result = self.mixed_frame.dtypes
def test_dtypes_gh8722(self, float_string_frame):
float_string_frame['bool'] = float_string_frame['A'] > 0
result = float_string_frame.dtypes
expected = Series({k: v.dtype
for k, v in compat.iteritems(self.mixed_frame)},
for k, v in compat.iteritems(float_string_frame)},
index=result.index)
assert_series_equal(result, expected)

Expand All @@ -410,41 +409,41 @@ def test_dtypes_gh8722(self):
result = df.dtypes
assert_series_equal(result, Series({0: np.dtype('int64')}))

def test_ftypes(self):
frame = self.mixed_float
def test_ftypes(self, mixed_float_frame):
frame = mixed_float_frame
expected = Series(dict(A='float32:dense',
B='float32:dense',
C='float16:dense',
D='float64:dense')).sort_values()
result = frame.ftypes.sort_values()
assert_series_equal(result, expected)

def test_astype(self):
casted = self.frame.astype(int)
expected = DataFrame(self.frame.values.astype(int),
index=self.frame.index,
columns=self.frame.columns)
def test_astype(self, float_frame, mixed_float_frame, mixed_type_frame):
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see my comments elsewhere. don't use this pattern.

casted = float_frame.astype(int)
expected = DataFrame(float_frame.values.astype(int),
index=float_frame.index,
columns=float_frame.columns)
assert_frame_equal(casted, expected)

casted = self.frame.astype(np.int32)
expected = DataFrame(self.frame.values.astype(np.int32),
index=self.frame.index,
columns=self.frame.columns)
casted = float_frame.astype(np.int32)
expected = DataFrame(float_frame.values.astype(np.int32),
index=float_frame.index,
columns=float_frame.columns)
assert_frame_equal(casted, expected)

self.frame['foo'] = '5'
casted = self.frame.astype(int)
expected = DataFrame(self.frame.values.astype(int),
index=self.frame.index,
columns=self.frame.columns)
float_frame['foo'] = '5'
casted = float_frame.astype(int)
expected = DataFrame(float_frame.values.astype(int),
index=float_frame.index,
columns=float_frame.columns)
assert_frame_equal(casted, expected)

# mixed casting
def _check_cast(df, v):
assert (list({s.dtype.name for
_, s in compat.iteritems(df)})[0] == v)

mn = self.all_mixed._get_numeric_data().copy()
mn = mixed_type_frame._get_numeric_data().copy()
mn['little_float'] = np.array(12345., dtype='float16')
mn['big_float'] = np.array(123456789101112., dtype='float64')

Expand All @@ -454,13 +453,15 @@ def _check_cast(df, v):
casted = mn.astype('int64')
_check_cast(casted, 'int64')

casted = self.mixed_float.reindex(columns=['A', 'B']).astype('float32')
casted = mixed_float_frame.reindex(
columns=['A', 'B']).astype('float32')
_check_cast(casted, 'float32')

casted = mn.reindex(columns=['little_float']).astype('float16')
_check_cast(casted, 'float16')

casted = self.mixed_float.reindex(columns=['A', 'B']).astype('float16')
casted = mixed_float_frame.reindex(
columns=['A', 'B']).astype('float16')
_check_cast(casted, 'float16')

casted = mn.astype('float32')
Expand All @@ -473,37 +474,37 @@ def _check_cast(df, v):
casted = mn.astype('O')
_check_cast(casted, 'object')

def test_astype_with_exclude_string(self):
df = self.frame.copy()
expected = self.frame.astype(int)
def test_astype_with_exclude_string(self, float_frame):
df = float_frame.copy()
expected = float_frame.astype(int)
df['string'] = 'foo'
casted = df.astype(int, errors='ignore')

expected['string'] = 'foo'
assert_frame_equal(casted, expected)

df = self.frame.copy()
expected = self.frame.astype(np.int32)
df = float_frame.copy()
expected = float_frame.astype(np.int32)
df['string'] = 'foo'
casted = df.astype(np.int32, errors='ignore')

expected['string'] = 'foo'
assert_frame_equal(casted, expected)

def test_astype_with_view(self):
def test_astype_with_view(self, float_frame, mixed_float_frame):
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same


tf = self.mixed_float.reindex(columns=['A', 'B', 'C'])
tf = mixed_float_frame.reindex(columns=['A', 'B', 'C'])

casted = tf.astype(np.int64)

casted = tf.astype(np.float32)

# this is the only real reason to do it this way
tf = np.round(self.frame).astype(np.int32)
tf = np.round(float_frame).astype(np.int32)
casted = tf.astype(np.float32, copy=False)

# TODO(wesm): verification?
tf = self.frame.astype(np.float64)
tf = float_frame.astype(np.float64)
casted = tf.astype(np.int64, copy=False) # noqa

@pytest.mark.parametrize("dtype", [np.int32, np.int64])
Expand Down Expand Up @@ -904,12 +905,12 @@ def test_asarray_homogenous(self):
tm.assert_numpy_array_equal(result, expected)


class TestDataFrameDatetimeWithTZ(TestData):
class TestDataFrameDatetimeWithTZ():

def test_interleave(self):
def test_interleave(self, timezone_frame):

# interleave with object
result = self.tzframe.assign(D='foo').values
result = timezone_frame.assign(D='foo').values
expected = np.array([[Timestamp('2013-01-01 00:00:00'),
Timestamp('2013-01-02 00:00:00'),
Timestamp('2013-01-03 00:00:00')],
Expand All @@ -925,7 +926,7 @@ def test_interleave(self):
tm.assert_numpy_array_equal(result, expected)

# interleave with only datetime64[ns]
result = self.tzframe.values
result = timezone_frame.values
expected = np.array([[Timestamp('2013-01-01 00:00:00'),
Timestamp('2013-01-02 00:00:00'),
Timestamp('2013-01-03 00:00:00')],
Expand All @@ -940,7 +941,7 @@ def test_interleave(self):
tz='CET')]], dtype=object).T
tm.assert_numpy_array_equal(result, expected)

def test_astype(self):
def test_astype(self, timezone_frame):
# astype
expected = np.array([[Timestamp('2013-01-01 00:00:00'),
Timestamp('2013-01-02 00:00:00'),
Expand All @@ -955,11 +956,12 @@ def test_astype(self):
Timestamp('2013-01-03 00:00:00+0100',
tz='CET')]],
dtype=object).T
result = self.tzframe.astype(object)
result = timezone_frame.astype(object)
assert_frame_equal(result, DataFrame(
expected, index=self.tzframe.index, columns=self.tzframe.columns))
expected, index=timezone_frame.index,
columns=timezone_frame.columns))

result = self.tzframe.astype('datetime64[ns]')
result = timezone_frame.astype('datetime64[ns]')
expected = DataFrame({'A': date_range('20130101', periods=3),
'B': (date_range('20130101', periods=3,
tz='US/Eastern')
Expand All @@ -973,19 +975,19 @@ def test_astype(self):
expected.iloc[1, 2] = pd.NaT
assert_frame_equal(result, expected)

def test_astype_str(self):
def test_astype_str(self, timezone_frame):
# str formatting
result = self.tzframe.astype(str)
result = timezone_frame.astype(str)
expected = DataFrame([['2013-01-01', '2013-01-01 00:00:00-05:00',
'2013-01-01 00:00:00+01:00'],
['2013-01-02', 'NaT', 'NaT'],
['2013-01-03', '2013-01-03 00:00:00-05:00',
'2013-01-03 00:00:00+01:00']],
columns=self.tzframe.columns)
columns=timezone_frame.columns)
tm.assert_frame_equal(result, expected)

with option_context('display.max_columns', 20):
result = str(self.tzframe)
result = str(timezone_frame)
assert ('0 2013-01-01 2013-01-01 00:00:00-05:00 '
'2013-01-01 00:00:00+01:00') in result
assert ('1 2013-01-02 '
Expand Down