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[TST] Fixturize frame/test_rank.py #26733

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Merged
merged 1 commit into from
Jun 8, 2019
Merged

[TST] Fixturize frame/test_rank.py #26733

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76 changes: 38 additions & 38 deletions pandas/tests/frame/test_rank.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,19 +29,19 @@ def method(self, request):
"""
return request.param

def test_rank(self):
def test_rank(self, float_frame):
rankdata = pytest.importorskip('scipy.stats.rankdata')

self.frame['A'][::2] = np.nan
self.frame['B'][::3] = np.nan
self.frame['C'][::4] = np.nan
self.frame['D'][::5] = np.nan
float_frame['A'][::2] = np.nan
float_frame['B'][::3] = np.nan
float_frame['C'][::4] = np.nan
float_frame['D'][::5] = np.nan

ranks0 = self.frame.rank()
ranks1 = self.frame.rank(1)
mask = np.isnan(self.frame.values)
ranks0 = float_frame.rank()
ranks1 = float_frame.rank(1)
mask = np.isnan(float_frame.values)

fvals = self.frame.fillna(np.inf).values
fvals = float_frame.fillna(np.inf).values

exp0 = np.apply_along_axis(rankdata, 0, fvals)
exp0[mask] = np.nan
Expand Down Expand Up @@ -109,32 +109,32 @@ def test_rank2(self):
result = df.rank(1, numeric_only=False, ascending=False)
tm.assert_frame_equal(result, expected)

# mixed-type frames
self.mixed_frame['datetime'] = datetime.now()
self.mixed_frame['timedelta'] = timedelta(days=1, seconds=1)

result = self.mixed_frame.rank(1)
expected = self.mixed_frame.rank(1, numeric_only=True)
tm.assert_frame_equal(result, expected)

df = DataFrame({"a": [1e-20, -5, 1e-20 + 1e-40, 10,
1e60, 1e80, 1e-30]})
exp = DataFrame({"a": [3.5, 1., 3.5, 5., 6., 7., 2.]})
tm.assert_frame_equal(df.rank(), exp)

def test_rank_na_option(self):
def test_rank_mixed_frame(self, float_string_frame):
float_string_frame['datetime'] = datetime.now()
float_string_frame['timedelta'] = timedelta(days=1, seconds=1)

result = float_string_frame.rank(1)
expected = float_string_frame.rank(1, numeric_only=True)
tm.assert_frame_equal(result, expected)

def test_rank_na_option(self, float_frame):
rankdata = pytest.importorskip('scipy.stats.rankdata')

self.frame['A'][::2] = np.nan
self.frame['B'][::3] = np.nan
self.frame['C'][::4] = np.nan
self.frame['D'][::5] = np.nan
float_frame['A'][::2] = np.nan
float_frame['B'][::3] = np.nan
float_frame['C'][::4] = np.nan
float_frame['D'][::5] = np.nan

# bottom
ranks0 = self.frame.rank(na_option='bottom')
ranks1 = self.frame.rank(1, na_option='bottom')
ranks0 = float_frame.rank(na_option='bottom')
ranks1 = float_frame.rank(1, na_option='bottom')

fvals = self.frame.fillna(np.inf).values
fvals = float_frame.fillna(np.inf).values

exp0 = np.apply_along_axis(rankdata, 0, fvals)
exp1 = np.apply_along_axis(rankdata, 1, fvals)
Expand All @@ -143,11 +143,11 @@ def test_rank_na_option(self):
tm.assert_almost_equal(ranks1.values, exp1)

# top
ranks0 = self.frame.rank(na_option='top')
ranks1 = self.frame.rank(1, na_option='top')
ranks0 = float_frame.rank(na_option='top')
ranks1 = float_frame.rank(1, na_option='top')

fval0 = self.frame.fillna((self.frame.min() - 1).to_dict()).values
fval1 = self.frame.T
fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values
fval1 = float_frame.T
fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T
fval1 = fval1.fillna(np.inf).values

Expand All @@ -160,10 +160,10 @@ def test_rank_na_option(self):
# descending

# bottom
ranks0 = self.frame.rank(na_option='top', ascending=False)
ranks1 = self.frame.rank(1, na_option='top', ascending=False)
ranks0 = float_frame.rank(na_option='top', ascending=False)
ranks1 = float_frame.rank(1, na_option='top', ascending=False)

fvals = self.frame.fillna(np.inf).values
fvals = float_frame.fillna(np.inf).values

exp0 = np.apply_along_axis(rankdata, 0, -fvals)
exp1 = np.apply_along_axis(rankdata, 1, -fvals)
Expand All @@ -174,11 +174,11 @@ def test_rank_na_option(self):
# descending

# top
ranks0 = self.frame.rank(na_option='bottom', ascending=False)
ranks1 = self.frame.rank(1, na_option='bottom', ascending=False)
ranks0 = float_frame.rank(na_option='bottom', ascending=False)
ranks1 = float_frame.rank(1, na_option='bottom', ascending=False)

fval0 = self.frame.fillna((self.frame.min() - 1).to_dict()).values
fval1 = self.frame.T
fval0 = float_frame.fillna((float_frame.min() - 1).to_dict()).values
fval1 = float_frame.T
fval1 = fval1.fillna((fval1.min() - 1).to_dict()).T
fval1 = fval1.fillna(np.inf).values

Expand All @@ -192,11 +192,11 @@ def test_rank_na_option(self):
msg = "na_option must be one of 'keep', 'top', or 'bottom'"

with pytest.raises(ValueError, match=msg):
self.frame.rank(na_option='bad', ascending=False)
float_frame.rank(na_option='bad', ascending=False)

# invalid type
with pytest.raises(ValueError, match=msg):
self.frame.rank(na_option=True, ascending=False)
float_frame.rank(na_option=True, ascending=False)

def test_rank_axis(self):
# check if using axes' names gives the same result
Expand Down