674 lines
20 KiB
Python
674 lines
20 KiB
Python
"""
|
|
test all other .agg behavior
|
|
"""
|
|
|
|
import datetime as dt
|
|
from functools import partial
|
|
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import pandas as pd
|
|
from pandas import (
|
|
DataFrame,
|
|
Index,
|
|
MultiIndex,
|
|
PeriodIndex,
|
|
Series,
|
|
date_range,
|
|
period_range,
|
|
)
|
|
import pandas._testing as tm
|
|
from pandas.core.base import SpecificationError
|
|
|
|
from pandas.io.formats.printing import pprint_thing
|
|
|
|
|
|
def test_agg_api():
|
|
# GH 6337
|
|
# https://stackoverflow.com/questions/21706030/pandas-groupby-agg-function-column-dtype-error
|
|
# different api for agg when passed custom function with mixed frame
|
|
|
|
df = DataFrame(
|
|
{
|
|
"data1": np.random.randn(5),
|
|
"data2": np.random.randn(5),
|
|
"key1": ["a", "a", "b", "b", "a"],
|
|
"key2": ["one", "two", "one", "two", "one"],
|
|
}
|
|
)
|
|
grouped = df.groupby("key1")
|
|
|
|
def peak_to_peak(arr):
|
|
return arr.max() - arr.min()
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning,
|
|
match=r"\['key2'\] did not aggregate successfully",
|
|
):
|
|
expected = grouped.agg([peak_to_peak])
|
|
expected.columns = ["data1", "data2"]
|
|
|
|
with tm.assert_produces_warning(
|
|
FutureWarning,
|
|
match=r"\['key2'\] did not aggregate successfully",
|
|
):
|
|
result = grouped.agg(peak_to_peak)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_agg_datetimes_mixed():
|
|
data = [[1, "2012-01-01", 1.0], [2, "2012-01-02", 2.0], [3, None, 3.0]]
|
|
|
|
df1 = DataFrame(
|
|
{
|
|
"key": [x[0] for x in data],
|
|
"date": [x[1] for x in data],
|
|
"value": [x[2] for x in data],
|
|
}
|
|
)
|
|
|
|
data = [
|
|
[
|
|
row[0],
|
|
(dt.datetime.strptime(row[1], "%Y-%m-%d").date() if row[1] else None),
|
|
row[2],
|
|
]
|
|
for row in data
|
|
]
|
|
|
|
df2 = DataFrame(
|
|
{
|
|
"key": [x[0] for x in data],
|
|
"date": [x[1] for x in data],
|
|
"value": [x[2] for x in data],
|
|
}
|
|
)
|
|
|
|
df1["weights"] = df1["value"] / df1["value"].sum()
|
|
gb1 = df1.groupby("date").aggregate(np.sum)
|
|
|
|
df2["weights"] = df1["value"] / df1["value"].sum()
|
|
gb2 = df2.groupby("date").aggregate(np.sum)
|
|
|
|
assert len(gb1) == len(gb2)
|
|
|
|
|
|
def test_agg_period_index():
|
|
prng = period_range("2012-1-1", freq="M", periods=3)
|
|
df = DataFrame(np.random.randn(3, 2), index=prng)
|
|
rs = df.groupby(level=0).sum()
|
|
assert isinstance(rs.index, PeriodIndex)
|
|
|
|
# GH 3579
|
|
index = period_range(start="1999-01", periods=5, freq="M")
|
|
s1 = Series(np.random.rand(len(index)), index=index)
|
|
s2 = Series(np.random.rand(len(index)), index=index)
|
|
df = DataFrame.from_dict({"s1": s1, "s2": s2})
|
|
grouped = df.groupby(df.index.month)
|
|
list(grouped)
|
|
|
|
|
|
def test_agg_dict_parameter_cast_result_dtypes():
|
|
# GH 12821
|
|
|
|
df = DataFrame(
|
|
{
|
|
"class": ["A", "A", "B", "B", "C", "C", "D", "D"],
|
|
"time": date_range("1/1/2011", periods=8, freq="H"),
|
|
}
|
|
)
|
|
df.loc[[0, 1, 2, 5], "time"] = None
|
|
|
|
# test for `first` function
|
|
exp = df.loc[[0, 3, 4, 6]].set_index("class")
|
|
grouped = df.groupby("class")
|
|
tm.assert_frame_equal(grouped.first(), exp)
|
|
tm.assert_frame_equal(grouped.agg("first"), exp)
|
|
tm.assert_frame_equal(grouped.agg({"time": "first"}), exp)
|
|
tm.assert_series_equal(grouped.time.first(), exp["time"])
|
|
tm.assert_series_equal(grouped.time.agg("first"), exp["time"])
|
|
|
|
# test for `last` function
|
|
exp = df.loc[[0, 3, 4, 7]].set_index("class")
|
|
grouped = df.groupby("class")
|
|
tm.assert_frame_equal(grouped.last(), exp)
|
|
tm.assert_frame_equal(grouped.agg("last"), exp)
|
|
tm.assert_frame_equal(grouped.agg({"time": "last"}), exp)
|
|
tm.assert_series_equal(grouped.time.last(), exp["time"])
|
|
tm.assert_series_equal(grouped.time.agg("last"), exp["time"])
|
|
|
|
# count
|
|
exp = Series([2, 2, 2, 2], index=Index(list("ABCD"), name="class"), name="time")
|
|
tm.assert_series_equal(grouped.time.agg(len), exp)
|
|
tm.assert_series_equal(grouped.time.size(), exp)
|
|
|
|
exp = Series([0, 1, 1, 2], index=Index(list("ABCD"), name="class"), name="time")
|
|
tm.assert_series_equal(grouped.time.count(), exp)
|
|
|
|
|
|
def test_agg_cast_results_dtypes():
|
|
# similar to GH12821
|
|
# xref #11444
|
|
u = [dt.datetime(2015, x + 1, 1) for x in range(12)]
|
|
v = list("aaabbbbbbccd")
|
|
df = DataFrame({"X": v, "Y": u})
|
|
|
|
result = df.groupby("X")["Y"].agg(len)
|
|
expected = df.groupby("X")["Y"].count()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_aggregate_float64_no_int64():
|
|
# see gh-11199
|
|
df = DataFrame({"a": [1, 2, 3, 4, 5], "b": [1, 2, 2, 4, 5], "c": [1, 2, 3, 4, 5]})
|
|
|
|
expected = DataFrame({"a": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
|
|
expected.index.name = "b"
|
|
|
|
result = df.groupby("b")[["a"]].mean()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
expected = DataFrame({"a": [1, 2.5, 4, 5], "c": [1, 2.5, 4, 5]}, index=[1, 2, 4, 5])
|
|
expected.index.name = "b"
|
|
|
|
result = df.groupby("b")[["a", "c"]].mean()
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_aggregate_api_consistency():
|
|
# GH 9052
|
|
# make sure that the aggregates via dict
|
|
# are consistent
|
|
df = DataFrame(
|
|
{
|
|
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
|
|
"B": ["one", "one", "two", "two", "two", "two", "one", "two"],
|
|
"C": np.random.randn(8) + 1.0,
|
|
"D": np.arange(8),
|
|
}
|
|
)
|
|
|
|
grouped = df.groupby(["A", "B"])
|
|
c_mean = grouped["C"].mean()
|
|
c_sum = grouped["C"].sum()
|
|
d_mean = grouped["D"].mean()
|
|
d_sum = grouped["D"].sum()
|
|
|
|
result = grouped["D"].agg(["sum", "mean"])
|
|
expected = pd.concat([d_sum, d_mean], axis=1)
|
|
expected.columns = ["sum", "mean"]
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
|
|
|
result = grouped.agg([np.sum, np.mean])
|
|
expected = pd.concat([c_sum, c_mean, d_sum, d_mean], axis=1)
|
|
expected.columns = MultiIndex.from_product([["C", "D"], ["sum", "mean"]])
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
|
|
|
result = grouped[["D", "C"]].agg([np.sum, np.mean])
|
|
expected = pd.concat([d_sum, d_mean, c_sum, c_mean], axis=1)
|
|
expected.columns = MultiIndex.from_product([["D", "C"], ["sum", "mean"]])
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
|
|
|
result = grouped.agg({"C": "mean", "D": "sum"})
|
|
expected = pd.concat([d_sum, c_mean], axis=1)
|
|
tm.assert_frame_equal(result, expected, check_like=True)
|
|
|
|
result = grouped.agg({"C": ["mean", "sum"], "D": ["mean", "sum"]})
|
|
expected = pd.concat([c_mean, c_sum, d_mean, d_sum], axis=1)
|
|
expected.columns = MultiIndex.from_product([["C", "D"], ["mean", "sum"]])
|
|
|
|
msg = r"Column\(s\) \['r', 'r2'\] do not exist"
|
|
with pytest.raises(KeyError, match=msg):
|
|
grouped[["D", "C"]].agg({"r": np.sum, "r2": np.mean})
|
|
|
|
|
|
def test_agg_dict_renaming_deprecation():
|
|
# 15931
|
|
df = DataFrame({"A": [1, 1, 1, 2, 2], "B": range(5), "C": range(5)})
|
|
|
|
msg = r"nested renamer is not supported"
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
df.groupby("A").agg(
|
|
{"B": {"foo": ["sum", "max"]}, "C": {"bar": ["count", "min"]}}
|
|
)
|
|
|
|
msg = r"Column\(s\) \['ma'\] do not exist"
|
|
with pytest.raises(KeyError, match=msg):
|
|
df.groupby("A")[["B", "C"]].agg({"ma": "max"})
|
|
|
|
msg = r"nested renamer is not supported"
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
df.groupby("A").B.agg({"foo": "count"})
|
|
|
|
|
|
def test_agg_compat():
|
|
# GH 12334
|
|
df = DataFrame(
|
|
{
|
|
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
|
|
"B": ["one", "one", "two", "two", "two", "two", "one", "two"],
|
|
"C": np.random.randn(8) + 1.0,
|
|
"D": np.arange(8),
|
|
}
|
|
)
|
|
|
|
g = df.groupby(["A", "B"])
|
|
|
|
msg = r"nested renamer is not supported"
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
g["D"].agg({"C": ["sum", "std"]})
|
|
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
g["D"].agg({"C": "sum", "D": "std"})
|
|
|
|
|
|
def test_agg_nested_dicts():
|
|
# API change for disallowing these types of nested dicts
|
|
df = DataFrame(
|
|
{
|
|
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
|
|
"B": ["one", "one", "two", "two", "two", "two", "one", "two"],
|
|
"C": np.random.randn(8) + 1.0,
|
|
"D": np.arange(8),
|
|
}
|
|
)
|
|
|
|
g = df.groupby(["A", "B"])
|
|
|
|
msg = r"nested renamer is not supported"
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
g.aggregate({"r1": {"C": ["mean", "sum"]}, "r2": {"D": ["mean", "sum"]}})
|
|
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
g.agg({"C": {"ra": ["mean", "std"]}, "D": {"rb": ["mean", "std"]}})
|
|
|
|
# same name as the original column
|
|
# GH9052
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
g["D"].agg({"result1": np.sum, "result2": np.mean})
|
|
|
|
with pytest.raises(SpecificationError, match=msg):
|
|
g["D"].agg({"D": np.sum, "result2": np.mean})
|
|
|
|
|
|
def test_agg_item_by_item_raise_typeerror():
|
|
df = DataFrame(np.random.randint(10, size=(20, 10)))
|
|
|
|
def raiseException(df):
|
|
pprint_thing("----------------------------------------")
|
|
pprint_thing(df.to_string())
|
|
raise TypeError("test")
|
|
|
|
with pytest.raises(TypeError, match="test"):
|
|
with tm.assert_produces_warning(FutureWarning, match="Dropping invalid"):
|
|
df.groupby(0).agg(raiseException)
|
|
|
|
|
|
def test_series_agg_multikey():
|
|
ts = tm.makeTimeSeries()
|
|
grouped = ts.groupby([lambda x: x.year, lambda x: x.month])
|
|
|
|
result = grouped.agg(np.sum)
|
|
expected = grouped.sum()
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_series_agg_multi_pure_python():
|
|
data = DataFrame(
|
|
{
|
|
"A": [
|
|
"foo",
|
|
"foo",
|
|
"foo",
|
|
"foo",
|
|
"bar",
|
|
"bar",
|
|
"bar",
|
|
"bar",
|
|
"foo",
|
|
"foo",
|
|
"foo",
|
|
],
|
|
"B": [
|
|
"one",
|
|
"one",
|
|
"one",
|
|
"two",
|
|
"one",
|
|
"one",
|
|
"one",
|
|
"two",
|
|
"two",
|
|
"two",
|
|
"one",
|
|
],
|
|
"C": [
|
|
"dull",
|
|
"dull",
|
|
"shiny",
|
|
"dull",
|
|
"dull",
|
|
"shiny",
|
|
"shiny",
|
|
"dull",
|
|
"shiny",
|
|
"shiny",
|
|
"shiny",
|
|
],
|
|
"D": np.random.randn(11),
|
|
"E": np.random.randn(11),
|
|
"F": np.random.randn(11),
|
|
}
|
|
)
|
|
|
|
def bad(x):
|
|
assert len(x.values.base) > 0
|
|
return "foo"
|
|
|
|
result = data.groupby(["A", "B"]).agg(bad)
|
|
expected = data.groupby(["A", "B"]).agg(lambda x: "foo")
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_agg_consistency():
|
|
# agg with ([]) and () not consistent
|
|
# GH 6715
|
|
def P1(a):
|
|
return np.percentile(a.dropna(), q=1)
|
|
|
|
df = DataFrame(
|
|
{
|
|
"col1": [1, 2, 3, 4],
|
|
"col2": [10, 25, 26, 31],
|
|
"date": [
|
|
dt.date(2013, 2, 10),
|
|
dt.date(2013, 2, 10),
|
|
dt.date(2013, 2, 11),
|
|
dt.date(2013, 2, 11),
|
|
],
|
|
}
|
|
)
|
|
|
|
g = df.groupby("date")
|
|
|
|
expected = g.agg([P1])
|
|
expected.columns = expected.columns.levels[0]
|
|
|
|
result = g.agg(P1)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_agg_callables():
|
|
# GH 7929
|
|
df = DataFrame({"foo": [1, 2], "bar": [3, 4]}).astype(np.int64)
|
|
|
|
class fn_class:
|
|
def __call__(self, x):
|
|
return sum(x)
|
|
|
|
equiv_callables = [
|
|
sum,
|
|
np.sum,
|
|
lambda x: sum(x),
|
|
lambda x: x.sum(),
|
|
partial(sum),
|
|
fn_class(),
|
|
]
|
|
|
|
expected = df.groupby("foo").agg(sum)
|
|
for ecall in equiv_callables:
|
|
result = df.groupby("foo").agg(ecall)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_agg_over_numpy_arrays():
|
|
# GH 3788
|
|
df = DataFrame(
|
|
[
|
|
[1, np.array([10, 20, 30])],
|
|
[1, np.array([40, 50, 60])],
|
|
[2, np.array([20, 30, 40])],
|
|
],
|
|
columns=["category", "arraydata"],
|
|
)
|
|
gb = df.groupby("category")
|
|
|
|
expected_data = [[np.array([50, 70, 90])], [np.array([20, 30, 40])]]
|
|
expected_index = Index([1, 2], name="category")
|
|
expected_column = ["arraydata"]
|
|
expected = DataFrame(expected_data, index=expected_index, columns=expected_column)
|
|
|
|
alt = gb.sum(numeric_only=False)
|
|
tm.assert_frame_equal(alt, expected)
|
|
|
|
result = gb.agg("sum", numeric_only=False)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# FIXME: the original version of this test called `gb.agg(sum)`
|
|
# and that raises TypeError if `numeric_only=False` is passed
|
|
|
|
|
|
@pytest.mark.parametrize("as_period", [True, False])
|
|
def test_agg_tzaware_non_datetime_result(as_period):
|
|
# discussed in GH#29589, fixed in GH#29641, operating on tzaware values
|
|
# with function that is not dtype-preserving
|
|
dti = date_range("2012-01-01", periods=4, tz="UTC")
|
|
if as_period:
|
|
dti = dti.tz_localize(None).to_period("D")
|
|
|
|
df = DataFrame({"a": [0, 0, 1, 1], "b": dti})
|
|
gb = df.groupby("a")
|
|
|
|
# Case that _does_ preserve the dtype
|
|
result = gb["b"].agg(lambda x: x.iloc[0])
|
|
expected = Series(dti[::2], name="b")
|
|
expected.index.name = "a"
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
# Cases that do _not_ preserve the dtype
|
|
result = gb["b"].agg(lambda x: x.iloc[0].year)
|
|
expected = Series([2012, 2012], name="b")
|
|
expected.index.name = "a"
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
result = gb["b"].agg(lambda x: x.iloc[-1] - x.iloc[0])
|
|
expected = Series([pd.Timedelta(days=1), pd.Timedelta(days=1)], name="b")
|
|
expected.index.name = "a"
|
|
if as_period:
|
|
expected = Series([pd.offsets.Day(1), pd.offsets.Day(1)], name="b")
|
|
expected.index.name = "a"
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_agg_timezone_round_trip():
|
|
# GH 15426
|
|
ts = pd.Timestamp("2016-01-01 12:00:00", tz="US/Pacific")
|
|
df = DataFrame({"a": 1, "b": [ts + dt.timedelta(minutes=nn) for nn in range(10)]})
|
|
|
|
result1 = df.groupby("a")["b"].agg(np.min).iloc[0]
|
|
result2 = df.groupby("a")["b"].agg(lambda x: np.min(x)).iloc[0]
|
|
result3 = df.groupby("a")["b"].min().iloc[0]
|
|
|
|
assert result1 == ts
|
|
assert result2 == ts
|
|
assert result3 == ts
|
|
|
|
dates = [
|
|
pd.Timestamp(f"2016-01-0{i:d} 12:00:00", tz="US/Pacific") for i in range(1, 5)
|
|
]
|
|
df = DataFrame({"A": ["a", "b"] * 2, "B": dates})
|
|
grouped = df.groupby("A")
|
|
|
|
ts = df["B"].iloc[0]
|
|
assert ts == grouped.nth(0)["B"].iloc[0]
|
|
assert ts == grouped.head(1)["B"].iloc[0]
|
|
assert ts == grouped.first()["B"].iloc[0]
|
|
|
|
# GH#27110 applying iloc should return a DataFrame
|
|
assert ts == grouped.apply(lambda x: x.iloc[0]).iloc[0, 1]
|
|
|
|
ts = df["B"].iloc[2]
|
|
assert ts == grouped.last()["B"].iloc[0]
|
|
|
|
# GH#27110 applying iloc should return a DataFrame
|
|
assert ts == grouped.apply(lambda x: x.iloc[-1]).iloc[0, 1]
|
|
|
|
|
|
def test_sum_uint64_overflow():
|
|
# see gh-14758
|
|
# Convert to uint64 and don't overflow
|
|
df = DataFrame([[1, 2], [3, 4], [5, 6]], dtype=object)
|
|
df = df + 9223372036854775807
|
|
|
|
index = Index(
|
|
[9223372036854775808, 9223372036854775810, 9223372036854775812], dtype=np.uint64
|
|
)
|
|
expected = DataFrame(
|
|
{1: [9223372036854775809, 9223372036854775811, 9223372036854775813]},
|
|
index=index,
|
|
)
|
|
|
|
expected.index.name = 0
|
|
result = df.groupby(0).sum(numeric_only=False)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
# out column is non-numeric, so with numeric_only=True it is dropped
|
|
result2 = df.groupby(0).sum(numeric_only=True)
|
|
expected2 = expected[[]]
|
|
tm.assert_frame_equal(result2, expected2)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"structure, expected",
|
|
[
|
|
(tuple, DataFrame({"C": {(1, 1): (1, 1, 1), (3, 4): (3, 4, 4)}})),
|
|
(list, DataFrame({"C": {(1, 1): [1, 1, 1], (3, 4): [3, 4, 4]}})),
|
|
(
|
|
lambda x: tuple(x),
|
|
DataFrame({"C": {(1, 1): (1, 1, 1), (3, 4): (3, 4, 4)}}),
|
|
),
|
|
(
|
|
lambda x: list(x),
|
|
DataFrame({"C": {(1, 1): [1, 1, 1], (3, 4): [3, 4, 4]}}),
|
|
),
|
|
],
|
|
)
|
|
def test_agg_structs_dataframe(structure, expected):
|
|
df = DataFrame(
|
|
{"A": [1, 1, 1, 3, 3, 3], "B": [1, 1, 1, 4, 4, 4], "C": [1, 1, 1, 3, 4, 4]}
|
|
)
|
|
|
|
result = df.groupby(["A", "B"]).aggregate(structure)
|
|
expected.index.names = ["A", "B"]
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"structure, expected",
|
|
[
|
|
(tuple, Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name="C")),
|
|
(list, Series([[1, 1, 1], [3, 4, 4]], index=[1, 3], name="C")),
|
|
(lambda x: tuple(x), Series([(1, 1, 1), (3, 4, 4)], index=[1, 3], name="C")),
|
|
(lambda x: list(x), Series([[1, 1, 1], [3, 4, 4]], index=[1, 3], name="C")),
|
|
],
|
|
)
|
|
def test_agg_structs_series(structure, expected):
|
|
# Issue #18079
|
|
df = DataFrame(
|
|
{"A": [1, 1, 1, 3, 3, 3], "B": [1, 1, 1, 4, 4, 4], "C": [1, 1, 1, 3, 4, 4]}
|
|
)
|
|
|
|
result = df.groupby("A")["C"].aggregate(structure)
|
|
expected.index.name = "A"
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_agg_category_nansum(observed):
|
|
categories = ["a", "b", "c"]
|
|
df = DataFrame(
|
|
{"A": pd.Categorical(["a", "a", "b"], categories=categories), "B": [1, 2, 3]}
|
|
)
|
|
result = df.groupby("A", observed=observed).B.agg(np.nansum)
|
|
expected = Series(
|
|
[3, 3, 0],
|
|
index=pd.CategoricalIndex(["a", "b", "c"], categories=categories, name="A"),
|
|
name="B",
|
|
)
|
|
if observed:
|
|
expected = expected[expected != 0]
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
|
|
def test_agg_list_like_func():
|
|
# GH 18473
|
|
df = DataFrame({"A": [str(x) for x in range(3)], "B": [str(x) for x in range(3)]})
|
|
grouped = df.groupby("A", as_index=False, sort=False)
|
|
result = grouped.agg({"B": lambda x: list(x)})
|
|
expected = DataFrame(
|
|
{"A": [str(x) for x in range(3)], "B": [[str(x)] for x in range(3)]}
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
def test_agg_lambda_with_timezone():
|
|
# GH 23683
|
|
df = DataFrame(
|
|
{
|
|
"tag": [1, 1],
|
|
"date": [
|
|
pd.Timestamp("2018-01-01", tz="UTC"),
|
|
pd.Timestamp("2018-01-02", tz="UTC"),
|
|
],
|
|
}
|
|
)
|
|
result = df.groupby("tag").agg({"date": lambda e: e.head(1)})
|
|
expected = DataFrame(
|
|
[pd.Timestamp("2018-01-01", tz="UTC")],
|
|
index=Index([1], name="tag"),
|
|
columns=["date"],
|
|
)
|
|
tm.assert_frame_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"err_cls",
|
|
[
|
|
NotImplementedError,
|
|
RuntimeError,
|
|
KeyError,
|
|
IndexError,
|
|
OSError,
|
|
ValueError,
|
|
ArithmeticError,
|
|
AttributeError,
|
|
],
|
|
)
|
|
def test_groupby_agg_err_catching(err_cls):
|
|
# make sure we suppress anything other than TypeError or AssertionError
|
|
# in _python_agg_general
|
|
|
|
# Use a non-standard EA to make sure we don't go down ndarray paths
|
|
from pandas.tests.extension.decimal.array import (
|
|
DecimalArray,
|
|
make_data,
|
|
to_decimal,
|
|
)
|
|
|
|
data = make_data()[:5]
|
|
df = DataFrame(
|
|
{"id1": [0, 0, 0, 1, 1], "id2": [0, 1, 0, 1, 1], "decimals": DecimalArray(data)}
|
|
)
|
|
|
|
expected = Series(to_decimal([data[0], data[3]]))
|
|
|
|
def weird_func(x):
|
|
# weird function that raise something other than TypeError or IndexError
|
|
# in _python_agg_general
|
|
if len(x) == 0:
|
|
raise err_cls
|
|
return x.iloc[0]
|
|
|
|
result = df["decimals"].groupby(df["id1"]).agg(weird_func)
|
|
tm.assert_series_equal(result, expected, check_names=False)
|