forked from pandas-dev/pandas
-
Notifications
You must be signed in to change notification settings - Fork 0
/
test_groupby_subclass.py
117 lines (95 loc) · 3.75 KB
/
test_groupby_subclass.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
from datetime import datetime
import numpy as np
import pytest
from pandas import (
DataFrame,
Index,
Series,
)
import pandas._testing as tm
from pandas.tests.groupby import get_groupby_method_args
pytestmark = pytest.mark.filterwarnings(
"ignore:Passing a BlockManager|Passing a SingleBlockManager:DeprecationWarning"
)
@pytest.mark.parametrize(
"obj",
[
tm.SubclassedDataFrame({"A": np.arange(0, 10)}),
tm.SubclassedSeries(np.arange(0, 10), name="A"),
],
)
def test_groupby_preserves_subclass(obj, groupby_func):
# GH28330 -- preserve subclass through groupby operations
if isinstance(obj, Series) and groupby_func in {"corrwith"}:
pytest.skip(f"Not applicable for Series and {groupby_func}")
grouped = obj.groupby(np.arange(0, 10))
# Groups should preserve subclass type
assert isinstance(grouped.get_group(0), type(obj))
args = get_groupby_method_args(groupby_func, obj)
result1 = getattr(grouped, groupby_func)(*args)
result2 = grouped.agg(groupby_func, *args)
# Reduction or transformation kernels should preserve type
slices = {"ngroup", "cumcount", "size"}
if isinstance(obj, DataFrame) and groupby_func in slices:
assert isinstance(result1, tm.SubclassedSeries)
else:
assert isinstance(result1, type(obj))
# Confirm .agg() groupby operations return same results
if isinstance(result1, DataFrame):
tm.assert_frame_equal(result1, result2)
else:
tm.assert_series_equal(result1, result2)
def test_groupby_preserves_metadata():
# GH-37343
custom_df = tm.SubclassedDataFrame({"a": [1, 2, 3], "b": [1, 1, 2], "c": [7, 8, 9]})
assert "testattr" in custom_df._metadata
custom_df.testattr = "hello"
for _, group_df in custom_df.groupby("c"):
assert group_df.testattr == "hello"
# GH-45314
def func(group):
assert isinstance(group, tm.SubclassedDataFrame)
assert hasattr(group, "testattr")
return group.testattr
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(
FutureWarning, match=msg, raise_on_extra_warnings=False
):
result = custom_df.groupby("c").apply(func)
expected = tm.SubclassedSeries(["hello"] * 3, index=Index([7, 8, 9], name="c"))
tm.assert_series_equal(result, expected)
def func2(group):
assert isinstance(group, tm.SubclassedSeries)
assert hasattr(group, "testattr")
return group.testattr
custom_series = tm.SubclassedSeries([1, 2, 3])
custom_series.testattr = "hello"
result = custom_series.groupby(custom_df["c"]).apply(func2)
tm.assert_series_equal(result, expected)
result = custom_series.groupby(custom_df["c"]).agg(func2)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("obj", [DataFrame, tm.SubclassedDataFrame])
def test_groupby_resample_preserves_subclass(obj):
# GH28330 -- preserve subclass through groupby.resample()
df = obj(
{
"Buyer": "Carl Carl Carl Carl Joe Carl".split(),
"Quantity": [18, 3, 5, 1, 9, 3],
"Date": [
datetime(2013, 9, 1, 13, 0),
datetime(2013, 9, 1, 13, 5),
datetime(2013, 10, 1, 20, 0),
datetime(2013, 10, 3, 10, 0),
datetime(2013, 12, 2, 12, 0),
datetime(2013, 9, 2, 14, 0),
],
}
)
df = df.set_index("Date")
# Confirm groupby.resample() preserves dataframe type
msg = "DataFrameGroupBy.resample operated on the grouping columns"
with tm.assert_produces_warning(
FutureWarning, match=msg, raise_on_extra_warnings=False
):
result = df.groupby("Buyer").resample("5D").sum()
assert isinstance(result, obj)