Old engine for Continuous Time Bayesian Networks. Superseded by reCTBN. 🐍
https://github.com/madlabunimib/PyCTBN
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
92 lines
3.4 KiB
92 lines
3.4 KiB
4 years ago
|
import pytest
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
from .base import BaseExtensionTests
|
||
|
|
|
||
|
|
|
||
|
|
class BaseGroupbyTests(BaseExtensionTests):
|
||
|
|
"""Groupby-specific tests."""
|
||
|
|
|
||
|
|
def test_grouping_grouper(self, data_for_grouping):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{"A": ["B", "B", None, None, "A", "A", "B", "C"], "B": data_for_grouping}
|
||
|
|
)
|
||
|
|
gr1 = df.groupby("A").grouper.groupings[0]
|
||
|
|
gr2 = df.groupby("B").grouper.groupings[0]
|
||
|
|
|
||
|
|
tm.assert_numpy_array_equal(gr1.grouper, df.A.values)
|
||
|
|
tm.assert_extension_array_equal(gr2.grouper, data_for_grouping)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("as_index", [True, False])
|
||
|
|
def test_groupby_extension_agg(self, as_index, data_for_grouping):
|
||
|
|
df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
|
||
|
|
result = df.groupby("B", as_index=as_index).A.mean()
|
||
|
|
_, index = pd.factorize(data_for_grouping, sort=True)
|
||
|
|
|
||
|
|
index = pd.Index(index, name="B")
|
||
|
|
expected = pd.Series([3, 1, 4], index=index, name="A")
|
||
|
|
if as_index:
|
||
|
|
self.assert_series_equal(result, expected)
|
||
|
|
else:
|
||
|
|
expected = expected.reset_index()
|
||
|
|
self.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_groupby_extension_no_sort(self, data_for_grouping):
|
||
|
|
df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
|
||
|
|
result = df.groupby("B", sort=False).A.mean()
|
||
|
|
_, index = pd.factorize(data_for_grouping, sort=False)
|
||
|
|
|
||
|
|
index = pd.Index(index, name="B")
|
||
|
|
expected = pd.Series([1, 3, 4], index=index, name="A")
|
||
|
|
self.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_groupby_extension_transform(self, data_for_grouping):
|
||
|
|
valid = data_for_grouping[~data_for_grouping.isna()]
|
||
|
|
df = pd.DataFrame({"A": [1, 1, 3, 3, 1, 4], "B": valid})
|
||
|
|
|
||
|
|
result = df.groupby("B").A.transform(len)
|
||
|
|
expected = pd.Series([3, 3, 2, 2, 3, 1], name="A")
|
||
|
|
|
||
|
|
self.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_groupby_extension_apply(self, data_for_grouping, groupby_apply_op):
|
||
|
|
df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
|
||
|
|
df.groupby("B").apply(groupby_apply_op)
|
||
|
|
df.groupby("B").A.apply(groupby_apply_op)
|
||
|
|
df.groupby("A").apply(groupby_apply_op)
|
||
|
|
df.groupby("A").B.apply(groupby_apply_op)
|
||
|
|
|
||
|
|
def test_groupby_apply_identity(self, data_for_grouping):
|
||
|
|
df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
|
||
|
|
result = df.groupby("A").B.apply(lambda x: x.array)
|
||
|
|
expected = pd.Series(
|
||
|
|
[
|
||
|
|
df.B.iloc[[0, 1, 6]].array,
|
||
|
|
df.B.iloc[[2, 3]].array,
|
||
|
|
df.B.iloc[[4, 5]].array,
|
||
|
|
df.B.iloc[[7]].array,
|
||
|
|
],
|
||
|
|
index=pd.Index([1, 2, 3, 4], name="A"),
|
||
|
|
name="B",
|
||
|
|
)
|
||
|
|
self.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_in_numeric_groupby(self, data_for_grouping):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A": [1, 1, 2, 2, 3, 3, 1, 4],
|
||
|
|
"B": data_for_grouping,
|
||
|
|
"C": [1, 1, 1, 1, 1, 1, 1, 1],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
result = df.groupby("A").sum().columns
|
||
|
|
|
||
|
|
if data_for_grouping.dtype._is_numeric:
|
||
|
|
expected = pd.Index(["B", "C"])
|
||
|
|
else:
|
||
|
|
expected = pd.Index(["C"])
|
||
|
|
|
||
|
|
tm.assert_index_equal(result, expected)
|