Skip to content

REF: window/test_dtypes.py with pytest idioms #35918

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 7 commits into from
Sep 5, 2020
+118 −228
Merged

REF: window/test_dtypes.py with pytest idioms #35918

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
737c9f0
REF: window/test_dtypes.py with pytest idioms
Aug 27, 2020
a3611b0
Lint
Aug 27, 2020
4b5c0bc
Merge remote-tracking branch 'upstream/master' into ref/test_dtype
Aug 29, 2020
eaf30dc
Merge remote-tracking branch 'upstream/master' into ref/test_dtype
Aug 30, 2020
8a3dd0f
Merge remote-tracking branch 'upstream/master' into ref/test_dtype
Sep 1, 2020
99592e1
Merge remote-tracking branch 'upstream/master' into ref/test_dtype
Sep 2, 2020
dc98287
Merge remote-tracking branch 'upstream/master' into ref/test_dtype
Sep 5, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
31 changes: 31 additions & 0 deletions pandas/tests/window/conftest.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -308,3 +308,34 @@ def which(request):
def halflife_with_times(request):
"""Halflife argument for EWM when times is specified."""
return request.param


@pytest.fixture(
Copy link
Contributor

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

it might be worth putting this in pandas/conftest.py (maybe), but can try in a followon

params=[
"object",
"category",
"int8",
"int16",
"int32",
"int64",
"uint8",
"uint16",
"uint32",
"uint64",
"float16",
"float32",
"float64",
"m8[ns]",
"M8[ns]",
pytest.param(
"datetime64[ns, UTC]",
marks=pytest.mark.skip(
"direct creation of extension dtype datetime64[ns, UTC] "
"is not supported ATM"
),
),
]
)
def dtypes(request):
"""Dtypes for window tests"""
return request.param
315 changes: 87 additions & 228 deletions pandas/tests/window/test_dtypes.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,5 +1,3 @@
from itertools import product

import numpy as np
import pytest

Expand All @@ -10,234 +8,95 @@
# gh-12373 : rolling functions error on float32 data
# make sure rolling functions works for different dtypes
#
# NOTE that these are yielded tests and so _create_data
# is explicitly called.
#
# further note that we are only checking rolling for fully dtype
# compliance (though both expanding and ewm inherit)


class Dtype:
window = 2

funcs = {
"count": lambda v: v.count(),
"max": lambda v: v.max(),
"min": lambda v: v.min(),
"sum": lambda v: v.sum(),
"mean": lambda v: v.mean(),
"std": lambda v: v.std(),
"var": lambda v: v.var(),
"median": lambda v: v.median(),
}

def get_expects(self):
expects = {
"sr1": {
"count": Series([1, 2, 2, 2, 2], dtype="float64"),
"max": Series([np.nan, 1, 2, 3, 4], dtype="float64"),
"min": Series([np.nan, 0, 1, 2, 3], dtype="float64"),
"sum": Series([np.nan, 1, 3, 5, 7], dtype="float64"),
"mean": Series([np.nan, 0.5, 1.5, 2.5, 3.5], dtype="float64"),
"std": Series([np.nan] + [np.sqrt(0.5)] * 4, dtype="float64"),
"var": Series([np.nan, 0.5, 0.5, 0.5, 0.5], dtype="float64"),
"median": Series([np.nan, 0.5, 1.5, 2.5, 3.5], dtype="float64"),
def get_dtype(dtype, coerce_int=None):
if coerce_int is False and "int" in dtype:
return None
if dtype != "category":
return np.dtype(dtype)
return dtype


@pytest.mark.parametrize(
"method, data, expected_data, coerce_int",
[
("count", np.arange(5), [1, 2, 2, 2, 2], True),
("count", np.arange(10, 0, -2), [1, 2, 2, 2, 2], True),
("count", [0, 1, 2, np.nan, 4], [1, 2, 2, 1, 1], False),
("max", np.arange(5), [np.nan, 1, 2, 3, 4], True),
("max", np.arange(10, 0, -2), [np.nan, 10, 8, 6, 4], True),
("max", [0, 1, 2, np.nan, 4], [np.nan, 1, 2, np.nan, np.nan], False),
("min", np.arange(5), [np.nan, 0, 1, 2, 3], True),
("min", np.arange(10, 0, -2), [np.nan, 8, 6, 4, 2], True),
("min", [0, 1, 2, np.nan, 4], [np.nan, 0, 1, np.nan, np.nan], False),
("sum", np.arange(5), [np.nan, 1, 3, 5, 7], True),
("sum", np.arange(10, 0, -2), [np.nan, 18, 14, 10, 6], True),
("sum", [0, 1, 2, np.nan, 4], [np.nan, 1, 3, np.nan, np.nan], False),
("mean", np.arange(5), [np.nan, 0.5, 1.5, 2.5, 3.5], True),
("mean", np.arange(10, 0, -2), [np.nan, 9, 7, 5, 3], True),
("mean", [0, 1, 2, np.nan, 4], [np.nan, 0.5, 1.5, np.nan, np.nan], False),
("std", np.arange(5), [np.nan] + [np.sqrt(0.5)] * 4, True),
("std", np.arange(10, 0, -2), [np.nan] + [np.sqrt(2)] * 4, True),
(
"std",
[0, 1, 2, np.nan, 4],
[np.nan] + [np.sqrt(0.5)] * 2 + [np.nan] * 2,
False,
),
("var", np.arange(5), [np.nan, 0.5, 0.5, 0.5, 0.5], True),
("var", np.arange(10, 0, -2), [np.nan, 2, 2, 2, 2], True),
("var", [0, 1, 2, np.nan, 4], [np.nan, 0.5, 0.5, np.nan, np.nan], False),
("median", np.arange(5), [np.nan, 0.5, 1.5, 2.5, 3.5], True),
("median", np.arange(10, 0, -2), [np.nan, 9, 7, 5, 3], True),
("median", [0, 1, 2, np.nan, 4], [np.nan, 0.5, 1.5, np.nan, np.nan], False),
],
)
def test_series_dtypes(method, data, expected_data, coerce_int, dtypes):
s = Series(data, dtype=get_dtype(dtypes, coerce_int=coerce_int))
if dtypes in ("m8[ns]", "M8[ns]") and method != "count":
msg = "No numeric types to aggregate"
with pytest.raises(DataError, match=msg):
getattr(s.rolling(2), method)()
else:
result = getattr(s.rolling(2), method)()
expected = Series(expected_data, dtype="float64")
tm.assert_almost_equal(result, expected)


@pytest.mark.parametrize(
"method, expected_data",
[
("count", {0: Series([1, 2, 2, 2, 2]), 1: Series([1, 2, 2, 2, 2])}),
("max", {0: Series([np.nan, 2, 4, 6, 8]), 1: Series([np.nan, 3, 5, 7, 9])}),
("min", {0: Series([np.nan, 0, 2, 4, 6]), 1: Series([np.nan, 1, 3, 5, 7])}),
(
"sum",
{0: Series([np.nan, 2, 6, 10, 14]), 1: Series([np.nan, 4, 8, 12, 16])},
),
("mean", {0: Series([np.nan, 1, 3, 5, 7]), 1: Series([np.nan, 2, 4, 6, 8])}),
(
"std",
{
0: Series([np.nan] + [np.sqrt(2)] * 4),
1: Series([np.nan] + [np.sqrt(2)] * 4),
},
"sr2": {
"count": Series([1, 2, 2, 2, 2], dtype="float64"),
"max": Series([np.nan, 10, 8, 6, 4], dtype="float64"),
"min": Series([np.nan, 8, 6, 4, 2], dtype="float64"),
"sum": Series([np.nan, 18, 14, 10, 6], dtype="float64"),
"mean": Series([np.nan, 9, 7, 5, 3], dtype="float64"),
"std": Series([np.nan] + [np.sqrt(2)] * 4, dtype="float64"),
"var": Series([np.nan, 2, 2, 2, 2], dtype="float64"),
"median": Series([np.nan, 9, 7, 5, 3], dtype="float64"),
},
"sr3": {
"count": Series([1, 2, 2, 1, 1], dtype="float64"),
"max": Series([np.nan, 1, 2, np.nan, np.nan], dtype="float64"),
"min": Series([np.nan, 0, 1, np.nan, np.nan], dtype="float64"),
"sum": Series([np.nan, 1, 3, np.nan, np.nan], dtype="float64"),
"mean": Series([np.nan, 0.5, 1.5, np.nan, np.nan], dtype="float64"),
"std": Series(
[np.nan] + [np.sqrt(0.5)] * 2 + [np.nan] * 2, dtype="float64"
),
"var": Series([np.nan, 0.5, 0.5, np.nan, np.nan], dtype="float64"),
"median": Series([np.nan, 0.5, 1.5, np.nan, np.nan], dtype="float64"),
},
"df": {
"count": DataFrame(
{0: Series([1, 2, 2, 2, 2]), 1: Series([1, 2, 2, 2, 2])},
dtype="float64",
),
"max": DataFrame(
{0: Series([np.nan, 2, 4, 6, 8]), 1: Series([np.nan, 3, 5, 7, 9])},
dtype="float64",
),
"min": DataFrame(
{0: Series([np.nan, 0, 2, 4, 6]), 1: Series([np.nan, 1, 3, 5, 7])},
dtype="float64",
),
"sum": DataFrame(
{
0: Series([np.nan, 2, 6, 10, 14]),
1: Series([np.nan, 4, 8, 12, 16]),
},
dtype="float64",
),
"mean": DataFrame(
{0: Series([np.nan, 1, 3, 5, 7]), 1: Series([np.nan, 2, 4, 6, 8])},
dtype="float64",
),
"std": DataFrame(
{
0: Series([np.nan] + [np.sqrt(2)] * 4),
1: Series([np.nan] + [np.sqrt(2)] * 4),
},
dtype="float64",
),
"var": DataFrame(
{0: Series([np.nan, 2, 2, 2, 2]), 1: Series([np.nan, 2, 2, 2, 2])},
dtype="float64",
),
"median": DataFrame(
{0: Series([np.nan, 1, 3, 5, 7]), 1: Series([np.nan, 2, 4, 6, 8])},
dtype="float64",
),
},
}
return expects

def _create_dtype_data(self, dtype):
sr1 = Series(np.arange(5), dtype=dtype)
sr2 = Series(np.arange(10, 0, -2), dtype=dtype)
sr3 = sr1.copy()
sr3[3] = np.NaN
df = DataFrame(np.arange(10).reshape((5, 2)), dtype=dtype)

data = {"sr1": sr1, "sr2": sr2, "sr3": sr3, "df": df}

return data

def _create_data(self):
self.data = self._create_dtype_data(self.dtype)
self.expects = self.get_expects()

def test_dtypes(self):
self._create_data()
for f_name, d_name in product(self.funcs.keys(), self.data.keys()):

f = self.funcs[f_name]
d = self.data[d_name]
exp = self.expects[d_name][f_name]
self.check_dtypes(f, f_name, d, d_name, exp)

def check_dtypes(self, f, f_name, d, d_name, exp):
roll = d.rolling(window=self.window)
result = f(roll)
tm.assert_almost_equal(result, exp)


class TestDtype_object(Dtype):
dtype = object


class Dtype_integer(Dtype):
pass


class TestDtype_int8(Dtype_integer):
dtype = np.int8


class TestDtype_int16(Dtype_integer):
dtype = np.int16


class TestDtype_int32(Dtype_integer):
dtype = np.int32


class TestDtype_int64(Dtype_integer):
dtype = np.int64


class Dtype_uinteger(Dtype):
pass


class TestDtype_uint8(Dtype_uinteger):
dtype = np.uint8


class TestDtype_uint16(Dtype_uinteger):
dtype = np.uint16


class TestDtype_uint32(Dtype_uinteger):
dtype = np.uint32


class TestDtype_uint64(Dtype_uinteger):
dtype = np.uint64


class Dtype_float(Dtype):
pass


class TestDtype_float16(Dtype_float):
dtype = np.float16


class TestDtype_float32(Dtype_float):
dtype = np.float32


class TestDtype_float64(Dtype_float):
dtype = np.float64


class TestDtype_category(Dtype):
dtype = "category"
include_df = False

def _create_dtype_data(self, dtype):
sr1 = Series(range(5), dtype=dtype)
sr2 = Series(range(10, 0, -2), dtype=dtype)

data = {"sr1": sr1, "sr2": sr2}

return data


class DatetimeLike(Dtype):
def check_dtypes(self, f, f_name, d, d_name, exp):

roll = d.rolling(window=self.window)
if f_name == "count":
result = f(roll)
tm.assert_almost_equal(result, exp)

else:
msg = "No numeric types to aggregate"
with pytest.raises(DataError, match=msg):
f(roll)


class TestDtype_timedelta(DatetimeLike):
dtype = np.dtype("m8[ns]")


class TestDtype_datetime(DatetimeLike):
dtype = np.dtype("M8[ns]")


class TestDtype_datetime64UTC(DatetimeLike):
dtype = "datetime64[ns, UTC]"

def _create_data(self):
pytest.skip(
"direct creation of extension dtype "
"datetime64[ns, UTC] is not supported ATM"
)
),
("var", {0: Series([np.nan, 2, 2, 2, 2]), 1: Series([np.nan, 2, 2, 2, 2])}),
("median", {0: Series([np.nan, 1, 3, 5, 7]), 1: Series([np.nan, 2, 4, 6, 8])}),
],
)
def test_dataframe_dtypes(method, expected_data, dtypes):
if dtypes == "category":
pytest.skip("Category dataframe testing not implemented.")
df = DataFrame(np.arange(10).reshape((5, 2)), dtype=get_dtype(dtypes))
if dtypes in ("m8[ns]", "M8[ns]") and method != "count":
msg = "No numeric types to aggregate"
with pytest.raises(DataError, match=msg):
getattr(df.rolling(2), method)()
else:
result = getattr(df.rolling(2), method)()
expected = DataFrame(expected_data, dtype="float64")
tm.assert_frame_equal(result, expected)