175 lines
6.3 KiB
Python
175 lines
6.3 KiB
Python
|
import re
|
||
|
|
|
||
|
|
import pytest
|
||
|
|
|
||
|
|
from pandas.core.dtypes.common import (
|
||
|
|
is_bool_dtype,
|
||
|
|
is_numeric_dtype,
|
||
|
|
is_object_dtype,
|
||
|
|
is_string_dtype,
|
||
|
|
)
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
import pandas._testing as tm
|
||
|
|
|
||
|
|
|
||
|
|
@pytest.mark.filterwarnings(
|
||
|
|
"ignore:The default of observed=False is deprecated:FutureWarning"
|
||
|
|
)
|
||
|
|
class BaseGroupbyTests:
|
||
|
|
"""Groupby-specific tests."""
|
||
|
|
|
||
|
|
def test_grouping_grouper(self, data_for_grouping):
|
||
|
|
df = pd.DataFrame(
|
||
|
|
{
|
||
|
|
"A": pd.Series(
|
||
|
|
["B", "B", None, None, "A", "A", "B", "C"], dtype=object
|
||
|
|
),
|
||
|
|
"B": data_for_grouping,
|
||
|
|
}
|
||
|
|
)
|
||
|
|
gr1 = df.groupby("A")._grouper.groupings[0]
|
||
|
|
gr2 = df.groupby("B")._grouper.groupings[0]
|
||
|
|
|
||
|
|
tm.assert_numpy_array_equal(gr1.grouping_vector, df.A.values)
|
||
|
|
tm.assert_extension_array_equal(gr2.grouping_vector, 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})
|
||
|
|
|
||
|
|
is_bool = data_for_grouping.dtype._is_boolean
|
||
|
|
if is_bool:
|
||
|
|
# only 2 unique values, and the final entry has c==b
|
||
|
|
# (see data_for_grouping docstring)
|
||
|
|
df = df.iloc[:-1]
|
||
|
|
|
||
|
|
result = df.groupby("B", as_index=as_index).A.mean()
|
||
|
|
_, uniques = pd.factorize(data_for_grouping, sort=True)
|
||
|
|
|
||
|
|
exp_vals = [3.0, 1.0, 4.0]
|
||
|
|
if is_bool:
|
||
|
|
exp_vals = exp_vals[:-1]
|
||
|
|
if as_index:
|
||
|
|
index = pd.Index(uniques, name="B")
|
||
|
|
expected = pd.Series(exp_vals, index=index, name="A")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
else:
|
||
|
|
expected = pd.DataFrame({"B": uniques, "A": exp_vals})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
def test_groupby_agg_extension(self, data_for_grouping):
|
||
|
|
# GH#38980 groupby agg on extension type fails for non-numeric types
|
||
|
|
df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
|
||
|
|
|
||
|
|
expected = df.iloc[[0, 2, 4, 7]]
|
||
|
|
expected = expected.set_index("A")
|
||
|
|
|
||
|
|
result = df.groupby("A").agg({"B": "first"})
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.groupby("A").agg("first")
|
||
|
|
tm.assert_frame_equal(result, expected)
|
||
|
|
|
||
|
|
result = df.groupby("A").first()
|
||
|
|
tm.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})
|
||
|
|
|
||
|
|
is_bool = data_for_grouping.dtype._is_boolean
|
||
|
|
if is_bool:
|
||
|
|
# only 2 unique values, and the final entry has c==b
|
||
|
|
# (see data_for_grouping docstring)
|
||
|
|
df = df.iloc[:-1]
|
||
|
|
|
||
|
|
result = df.groupby("B", sort=False).A.mean()
|
||
|
|
_, index = pd.factorize(data_for_grouping, sort=False)
|
||
|
|
|
||
|
|
index = pd.Index(index, name="B")
|
||
|
|
exp_vals = [1.0, 3.0, 4.0]
|
||
|
|
if is_bool:
|
||
|
|
exp_vals = exp_vals[:-1]
|
||
|
|
expected = pd.Series(exp_vals, index=index, name="A")
|
||
|
|
tm.assert_series_equal(result, expected)
|
||
|
|
|
||
|
|
def test_groupby_extension_transform(self, data_for_grouping):
|
||
|
|
is_bool = data_for_grouping.dtype._is_boolean
|
||
|
|
|
||
|
|
valid = data_for_grouping[~data_for_grouping.isna()]
|
||
|
|
df = pd.DataFrame({"A": [1, 1, 3, 3, 1, 4], "B": valid})
|
||
|
|
is_bool = data_for_grouping.dtype._is_boolean
|
||
|
|
if is_bool:
|
||
|
|
# only 2 unique values, and the final entry has c==b
|
||
|
|
# (see data_for_grouping docstring)
|
||
|
|
df = df.iloc[:-1]
|
||
|
|
|
||
|
|
result = df.groupby("B").A.transform(len)
|
||
|
|
expected = pd.Series([3, 3, 2, 2, 3, 1], name="A")
|
||
|
|
if is_bool:
|
||
|
|
expected = expected[:-1]
|
||
|
|
|
||
|
|
tm.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})
|
||
|
|
msg = "DataFrameGroupBy.apply operated on the grouping columns"
|
||
|
|
with tm.assert_produces_warning(DeprecationWarning, match=msg):
|
||
|
|
df.groupby("B", group_keys=False, observed=False).apply(groupby_apply_op)
|
||
|
|
df.groupby("B", group_keys=False, observed=False).A.apply(groupby_apply_op)
|
||
|
|
msg = "DataFrameGroupBy.apply operated on the grouping columns"
|
||
|
|
with tm.assert_produces_warning(DeprecationWarning, match=msg):
|
||
|
|
df.groupby("A", group_keys=False, observed=False).apply(groupby_apply_op)
|
||
|
|
df.groupby("A", group_keys=False, observed=False).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",
|
||
|
|
)
|
||
|
|
tm.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],
|
||
|
|
}
|
||
|
|
)
|
||
|
|
|
||
|
|
dtype = data_for_grouping.dtype
|
||
|
|
if (
|
||
|
|
is_numeric_dtype(dtype)
|
||
|
|
or is_bool_dtype(dtype)
|
||
|
|
or dtype.name == "decimal"
|
||
|
|
or is_string_dtype(dtype)
|
||
|
|
or is_object_dtype(dtype)
|
||
|
|
or dtype.kind == "m" # in particular duration[*][pyarrow]
|
||
|
|
):
|
||
|
|
expected = pd.Index(["B", "C"])
|
||
|
|
result = df.groupby("A").sum().columns
|
||
|
|
else:
|
||
|
|
expected = pd.Index(["C"])
|
||
|
|
|
||
|
|
msg = "|".join(
|
||
|
|
[
|
||
|
|
# period/datetime
|
||
|
|
"does not support sum operations",
|
||
|
|
# all others
|
||
|
|
re.escape(f"agg function failed [how->sum,dtype->{dtype}"),
|
||
|
|
]
|
||
|
|
)
|
||
|
|
with pytest.raises(TypeError, match=msg):
|
||
|
|
df.groupby("A").sum()
|
||
|
|
result = df.groupby("A").sum(numeric_only=True).columns
|
||
|
|
tm.assert_index_equal(result, expected)
|