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ai-content-maker/.venv/Lib/site-packages/pandas/tests/groupby/test_apply.py

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2024-05-03 04:18:51 +03:00
from datetime import (
date,
datetime,
)
from io import StringIO
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
Index,
MultiIndex,
Series,
bdate_range,
)
import pandas._testing as tm
from pandas.core.api import Int64Index
from pandas.tests.groupby import get_groupby_method_args
def test_apply_issues():
# GH 5788
s = """2011.05.16,00:00,1.40893
2011.05.16,01:00,1.40760
2011.05.16,02:00,1.40750
2011.05.16,03:00,1.40649
2011.05.17,02:00,1.40893
2011.05.17,03:00,1.40760
2011.05.17,04:00,1.40750
2011.05.17,05:00,1.40649
2011.05.18,02:00,1.40893
2011.05.18,03:00,1.40760
2011.05.18,04:00,1.40750
2011.05.18,05:00,1.40649"""
df = pd.read_csv(
StringIO(s),
header=None,
names=["date", "time", "value"],
parse_dates=[["date", "time"]],
)
df = df.set_index("date_time")
expected = df.groupby(df.index.date).idxmax()
result = df.groupby(df.index.date).apply(lambda x: x.idxmax())
tm.assert_frame_equal(result, expected)
# GH 5789
# don't auto coerce dates
df = pd.read_csv(StringIO(s), header=None, names=["date", "time", "value"])
exp_idx = Index(
["2011.05.16", "2011.05.17", "2011.05.18"], dtype=object, name="date"
)
expected = Series(["00:00", "02:00", "02:00"], index=exp_idx)
result = df.groupby("date", group_keys=False).apply(
lambda x: x["time"][x["value"].idxmax()]
)
tm.assert_series_equal(result, expected)
def test_apply_trivial():
# GH 20066
# trivial apply: ignore input and return a constant dataframe.
df = DataFrame(
{"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
columns=["key", "data"],
)
expected = pd.concat([df.iloc[1:], df.iloc[1:]], axis=1, keys=["float64", "object"])
result = df.groupby([str(x) for x in df.dtypes], axis=1).apply(
lambda x: df.iloc[1:]
)
tm.assert_frame_equal(result, expected)
def test_apply_trivial_fail():
# GH 20066
df = DataFrame(
{"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
columns=["key", "data"],
)
expected = pd.concat([df, df], axis=1, keys=["float64", "object"])
result = df.groupby([str(x) for x in df.dtypes], axis=1, group_keys=True).apply(
lambda x: df
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"df, group_names",
[
(DataFrame({"a": [1, 1, 1, 2, 3], "b": ["a", "a", "a", "b", "c"]}), [1, 2, 3]),
(DataFrame({"a": [0, 0, 1, 1], "b": [0, 1, 0, 1]}), [0, 1]),
(DataFrame({"a": [1]}), [1]),
(DataFrame({"a": [1, 1, 1, 2, 2, 1, 1, 2], "b": range(8)}), [1, 2]),
(DataFrame({"a": [1, 2, 3, 1, 2, 3], "two": [4, 5, 6, 7, 8, 9]}), [1, 2, 3]),
(
DataFrame(
{
"a": list("aaabbbcccc"),
"B": [3, 4, 3, 6, 5, 2, 1, 9, 5, 4],
"C": [4, 0, 2, 2, 2, 7, 8, 6, 2, 8],
}
),
["a", "b", "c"],
),
(DataFrame([[1, 2, 3], [2, 2, 3]], columns=["a", "b", "c"]), [1, 2]),
],
ids=[
"GH2936",
"GH7739 & GH10519",
"GH10519",
"GH2656",
"GH12155",
"GH20084",
"GH21417",
],
)
def test_group_apply_once_per_group(df, group_names):
# GH2936, GH7739, GH10519, GH2656, GH12155, GH20084, GH21417
# This test should ensure that a function is only evaluated
# once per group. Previously the function has been evaluated twice
# on the first group to check if the Cython index slider is safe to use
# This test ensures that the side effect (append to list) is only triggered
# once per group
names = []
# cannot parameterize over the functions since they need external
# `names` to detect side effects
def f_copy(group):
# this takes the fast apply path
names.append(group.name)
return group.copy()
def f_nocopy(group):
# this takes the slow apply path
names.append(group.name)
return group
def f_scalar(group):
# GH7739, GH2656
names.append(group.name)
return 0
def f_none(group):
# GH10519, GH12155, GH21417
names.append(group.name)
return None
def f_constant_df(group):
# GH2936, GH20084
names.append(group.name)
return DataFrame({"a": [1], "b": [1]})
for func in [f_copy, f_nocopy, f_scalar, f_none, f_constant_df]:
del names[:]
df.groupby("a", group_keys=False).apply(func)
assert names == group_names
def test_group_apply_once_per_group2(capsys):
# GH: 31111
# groupby-apply need to execute len(set(group_by_columns)) times
expected = 2 # Number of times `apply` should call a function for the current test
df = DataFrame(
{
"group_by_column": [0, 0, 0, 0, 1, 1, 1, 1],
"test_column": ["0", "2", "4", "6", "8", "10", "12", "14"],
},
index=["0", "2", "4", "6", "8", "10", "12", "14"],
)
df.groupby("group_by_column", group_keys=False).apply(
lambda df: print("function_called")
)
result = capsys.readouterr().out.count("function_called")
# If `groupby` behaves unexpectedly, this test will break
assert result == expected
def test_apply_fast_slow_identical():
# GH 31613
df = DataFrame({"A": [0, 0, 1], "b": range(3)})
# For simple index structures we check for fast/slow apply using
# an identity check on in/output
def slow(group):
return group
def fast(group):
return group.copy()
fast_df = df.groupby("A", group_keys=False).apply(fast)
slow_df = df.groupby("A", group_keys=False).apply(slow)
tm.assert_frame_equal(fast_df, slow_df)
@pytest.mark.parametrize(
"func",
[
lambda x: x,
lambda x: x[:],
lambda x: x.copy(deep=False),
lambda x: x.copy(deep=True),
],
)
def test_groupby_apply_identity_maybecopy_index_identical(func):
# GH 14927
# Whether the function returns a copy of the input data or not should not
# have an impact on the index structure of the result since this is not
# transparent to the user
df = DataFrame({"g": [1, 2, 2, 2], "a": [1, 2, 3, 4], "b": [5, 6, 7, 8]})
result = df.groupby("g", group_keys=False).apply(func)
tm.assert_frame_equal(result, df)
def test_apply_with_mixed_dtype():
# GH3480, apply with mixed dtype on axis=1 breaks in 0.11
df = DataFrame(
{
"foo1": np.random.randn(6),
"foo2": ["one", "two", "two", "three", "one", "two"],
}
)
result = df.apply(lambda x: x, axis=1).dtypes
expected = df.dtypes
tm.assert_series_equal(result, expected)
# GH 3610 incorrect dtype conversion with as_index=False
df = DataFrame({"c1": [1, 2, 6, 6, 8]})
df["c2"] = df.c1 / 2.0
result1 = df.groupby("c2").mean().reset_index().c2
result2 = df.groupby("c2", as_index=False).mean().c2
tm.assert_series_equal(result1, result2)
def test_groupby_as_index_apply():
# GH #4648 and #3417
df = DataFrame(
{
"item_id": ["b", "b", "a", "c", "a", "b"],
"user_id": [1, 2, 1, 1, 3, 1],
"time": range(6),
}
)
g_as = df.groupby("user_id", as_index=True)
g_not_as = df.groupby("user_id", as_index=False)
res_as = g_as.head(2).index
res_not_as = g_not_as.head(2).index
exp = Index([0, 1, 2, 4])
tm.assert_index_equal(res_as, exp)
tm.assert_index_equal(res_not_as, exp)
res_as_apply = g_as.apply(lambda x: x.head(2)).index
res_not_as_apply = g_not_as.apply(lambda x: x.head(2)).index
# apply doesn't maintain the original ordering
# changed in GH5610 as the as_index=False returns a MI here
exp_not_as_apply = MultiIndex.from_tuples([(0, 0), (0, 2), (1, 1), (2, 4)])
tp = [(1, 0), (1, 2), (2, 1), (3, 4)]
exp_as_apply = MultiIndex.from_tuples(tp, names=["user_id", None])
tm.assert_index_equal(res_as_apply, exp_as_apply)
tm.assert_index_equal(res_not_as_apply, exp_not_as_apply)
ind = Index(list("abcde"))
df = DataFrame([[1, 2], [2, 3], [1, 4], [1, 5], [2, 6]], index=ind)
res = df.groupby(0, as_index=False, group_keys=False).apply(lambda x: x).index
tm.assert_index_equal(res, ind)
def test_apply_concat_preserve_names(three_group):
grouped = three_group.groupby(["A", "B"])
def desc(group):
result = group.describe()
result.index.name = "stat"
return result
def desc2(group):
result = group.describe()
result.index.name = "stat"
result = result[: len(group)]
# weirdo
return result
def desc3(group):
result = group.describe()
# names are different
result.index.name = f"stat_{len(group):d}"
result = result[: len(group)]
# weirdo
return result
result = grouped.apply(desc)
assert result.index.names == ("A", "B", "stat")
result2 = grouped.apply(desc2)
assert result2.index.names == ("A", "B", "stat")
result3 = grouped.apply(desc3)
assert result3.index.names == ("A", "B", None)
def test_apply_series_to_frame():
def f(piece):
with np.errstate(invalid="ignore"):
logged = np.log(piece)
return DataFrame(
{"value": piece, "demeaned": piece - piece.mean(), "logged": logged}
)
dr = bdate_range("1/1/2000", periods=100)
ts = Series(np.random.randn(100), index=dr)
grouped = ts.groupby(lambda x: x.month, group_keys=False)
result = grouped.apply(f)
assert isinstance(result, DataFrame)
assert not hasattr(result, "name") # GH49907
tm.assert_index_equal(result.index, ts.index)
def test_apply_series_yield_constant(df):
result = df.groupby(["A", "B"])["C"].apply(len)
assert result.index.names[:2] == ("A", "B")
def test_apply_frame_yield_constant(df):
# GH13568
result = df.groupby(["A", "B"]).apply(len)
assert isinstance(result, Series)
assert result.name is None
result = df.groupby(["A", "B"])[["C", "D"]].apply(len)
assert isinstance(result, Series)
assert result.name is None
def test_apply_frame_to_series(df):
grouped = df.groupby(["A", "B"])
result = grouped.apply(len)
expected = grouped.count()["C"]
tm.assert_index_equal(result.index, expected.index)
tm.assert_numpy_array_equal(result.values, expected.values)
def test_apply_frame_not_as_index_column_name(df):
# GH 35964 - path within _wrap_applied_output not hit by a test
grouped = df.groupby(["A", "B"], as_index=False)
result = grouped.apply(len)
expected = grouped.count().rename(columns={"C": np.nan}).drop(columns="D")
# TODO(GH#34306): Use assert_frame_equal when column name is not np.nan
tm.assert_index_equal(result.index, expected.index)
tm.assert_numpy_array_equal(result.values, expected.values)
def test_apply_frame_concat_series():
def trans(group):
return group.groupby("B")["C"].sum().sort_values().iloc[:2]
def trans2(group):
grouped = group.groupby(df.reindex(group.index)["B"])
return grouped.sum().sort_values().iloc[:2]
df = DataFrame(
{
"A": np.random.randint(0, 5, 1000),
"B": np.random.randint(0, 5, 1000),
"C": np.random.randn(1000),
}
)
result = df.groupby("A").apply(trans)
exp = df.groupby("A")["C"].apply(trans2)
tm.assert_series_equal(result, exp, check_names=False)
assert result.name == "C"
def test_apply_transform(ts):
grouped = ts.groupby(lambda x: x.month, group_keys=False)
result = grouped.apply(lambda x: x * 2)
expected = grouped.transform(lambda x: x * 2)
tm.assert_series_equal(result, expected)
def test_apply_multikey_corner(tsframe):
grouped = tsframe.groupby([lambda x: x.year, lambda x: x.month])
def f(group):
return group.sort_values("A")[-5:]
result = grouped.apply(f)
for key, group in grouped:
tm.assert_frame_equal(result.loc[key], f(group))
@pytest.mark.parametrize("group_keys", [True, False])
def test_apply_chunk_view(group_keys):
# Low level tinkering could be unsafe, make sure not
df = DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})
result = df.groupby("key", group_keys=group_keys).apply(lambda x: x.iloc[:2])
expected = df.take([0, 1, 3, 4, 6, 7])
if group_keys:
expected.index = MultiIndex.from_arrays(
[[1, 1, 2, 2, 3, 3], expected.index], names=["key", None]
)
tm.assert_frame_equal(result, expected)
def test_apply_no_name_column_conflict():
df = DataFrame(
{
"name": [1, 1, 1, 1, 1, 1, 2, 2, 2, 2],
"name2": [0, 0, 0, 1, 1, 1, 0, 0, 1, 1],
"value": range(9, -1, -1),
}
)
# it works! #2605
grouped = df.groupby(["name", "name2"])
grouped.apply(lambda x: x.sort_values("value", inplace=True))
def test_apply_typecast_fail():
df = DataFrame(
{
"d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
"c": np.tile(["a", "b", "c"], 2),
"v": np.arange(1.0, 7.0),
}
)
def f(group):
v = group["v"]
group["v2"] = (v - v.min()) / (v.max() - v.min())
return group
result = df.groupby("d", group_keys=False).apply(f)
expected = df.copy()
expected["v2"] = np.tile([0.0, 0.5, 1], 2)
tm.assert_frame_equal(result, expected)
def test_apply_multiindex_fail():
index = MultiIndex.from_arrays([[0, 0, 0, 1, 1, 1], [1, 2, 3, 1, 2, 3]])
df = DataFrame(
{
"d": [1.0, 1.0, 1.0, 2.0, 2.0, 2.0],
"c": np.tile(["a", "b", "c"], 2),
"v": np.arange(1.0, 7.0),
},
index=index,
)
def f(group):
v = group["v"]
group["v2"] = (v - v.min()) / (v.max() - v.min())
return group
result = df.groupby("d", group_keys=False).apply(f)
expected = df.copy()
expected["v2"] = np.tile([0.0, 0.5, 1], 2)
tm.assert_frame_equal(result, expected)
def test_apply_corner(tsframe):
result = tsframe.groupby(lambda x: x.year, group_keys=False).apply(lambda x: x * 2)
expected = tsframe * 2
tm.assert_frame_equal(result, expected)
def test_apply_without_copy():
# GH 5545
# returning a non-copy in an applied function fails
data = DataFrame(
{
"id_field": [100, 100, 200, 300],
"category": ["a", "b", "c", "c"],
"value": [1, 2, 3, 4],
}
)
def filt1(x):
if x.shape[0] == 1:
return x.copy()
else:
return x[x.category == "c"]
def filt2(x):
if x.shape[0] == 1:
return x
else:
return x[x.category == "c"]
expected = data.groupby("id_field").apply(filt1)
result = data.groupby("id_field").apply(filt2)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("test_series", [True, False])
def test_apply_with_duplicated_non_sorted_axis(test_series):
# GH 30667
df = DataFrame(
[["x", "p"], ["x", "p"], ["x", "o"]], columns=["X", "Y"], index=[1, 2, 2]
)
if test_series:
ser = df.set_index("Y")["X"]
result = ser.groupby(level=0, group_keys=False).apply(lambda x: x)
# not expecting the order to remain the same for duplicated axis
result = result.sort_index()
expected = ser.sort_index()
tm.assert_series_equal(result, expected)
else:
result = df.groupby("Y", group_keys=False).apply(lambda x: x)
# not expecting the order to remain the same for duplicated axis
result = result.sort_values("Y")
expected = df.sort_values("Y")
tm.assert_frame_equal(result, expected)
def test_apply_reindex_values():
# GH: 26209
# reindexing from a single column of a groupby object with duplicate indices caused
# a ValueError (cannot reindex from duplicate axis) in 0.24.2, the problem was
# solved in #30679
values = [1, 2, 3, 4]
indices = [1, 1, 2, 2]
df = DataFrame({"group": ["Group1", "Group2"] * 2, "value": values}, index=indices)
expected = Series(values, index=indices, name="value")
def reindex_helper(x):
return x.reindex(np.arange(x.index.min(), x.index.max() + 1))
# the following group by raised a ValueError
result = df.groupby("group", group_keys=False).value.apply(reindex_helper)
tm.assert_series_equal(expected, result)
def test_apply_corner_cases():
# #535, can't use sliding iterator
N = 1000
labels = np.random.randint(0, 100, size=N)
df = DataFrame(
{
"key": labels,
"value1": np.random.randn(N),
"value2": ["foo", "bar", "baz", "qux"] * (N // 4),
}
)
grouped = df.groupby("key", group_keys=False)
def f(g):
g["value3"] = g["value1"] * 2
return g
result = grouped.apply(f)
assert "value3" in result
def test_apply_numeric_coercion_when_datetime():
# In the past, group-by/apply operations have been over-eager
# in converting dtypes to numeric, in the presence of datetime
# columns. Various GH issues were filed, the reproductions
# for which are here.
# GH 15670
df = DataFrame(
{"Number": [1, 2], "Date": ["2017-03-02"] * 2, "Str": ["foo", "inf"]}
)
expected = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
df.Date = pd.to_datetime(df.Date)
result = df.groupby(["Number"]).apply(lambda x: x.iloc[0])
tm.assert_series_equal(result["Str"], expected["Str"])
# GH 15421
df = DataFrame(
{"A": [10, 20, 30], "B": ["foo", "3", "4"], "T": [pd.Timestamp("12:31:22")] * 3}
)
def get_B(g):
return g.iloc[0][["B"]]
result = df.groupby("A").apply(get_B)["B"]
expected = df.B
expected.index = df.A
tm.assert_series_equal(result, expected)
# GH 14423
def predictions(tool):
out = Series(index=["p1", "p2", "useTime"], dtype=object)
if "step1" in list(tool.State):
out["p1"] = str(tool[tool.State == "step1"].Machine.values[0])
if "step2" in list(tool.State):
out["p2"] = str(tool[tool.State == "step2"].Machine.values[0])
out["useTime"] = str(tool[tool.State == "step2"].oTime.values[0])
return out
df1 = DataFrame(
{
"Key": ["B", "B", "A", "A"],
"State": ["step1", "step2", "step1", "step2"],
"oTime": ["", "2016-09-19 05:24:33", "", "2016-09-19 23:59:04"],
"Machine": ["23", "36L", "36R", "36R"],
}
)
df2 = df1.copy()
df2.oTime = pd.to_datetime(df2.oTime)
expected = df1.groupby("Key").apply(predictions).p1
result = df2.groupby("Key").apply(predictions).p1
tm.assert_series_equal(expected, result)
def test_apply_aggregating_timedelta_and_datetime():
# Regression test for GH 15562
# The following groupby caused ValueErrors and IndexErrors pre 0.20.0
df = DataFrame(
{
"clientid": ["A", "B", "C"],
"datetime": [np.datetime64("2017-02-01 00:00:00")] * 3,
}
)
df["time_delta_zero"] = df.datetime - df.datetime
result = df.groupby("clientid").apply(
lambda ddf: Series(
{"clientid_age": ddf.time_delta_zero.min(), "date": ddf.datetime.min()}
)
)
expected = DataFrame(
{
"clientid": ["A", "B", "C"],
"clientid_age": [np.timedelta64(0, "D")] * 3,
"date": [np.datetime64("2017-02-01 00:00:00")] * 3,
}
).set_index("clientid")
tm.assert_frame_equal(result, expected)
def test_apply_groupby_datetimeindex():
# GH 26182
# groupby apply failed on dataframe with DatetimeIndex
data = [["A", 10], ["B", 20], ["B", 30], ["C", 40], ["C", 50]]
df = DataFrame(
data, columns=["Name", "Value"], index=pd.date_range("2020-09-01", "2020-09-05")
)
result = df.groupby("Name").sum()
expected = DataFrame({"Name": ["A", "B", "C"], "Value": [10, 50, 90]})
expected.set_index("Name", inplace=True)
tm.assert_frame_equal(result, expected)
def test_time_field_bug():
# Test a fix for the following error related to GH issue 11324 When
# non-key fields in a group-by dataframe contained time-based fields
# that were not returned by the apply function, an exception would be
# raised.
df = DataFrame({"a": 1, "b": [datetime.now() for nn in range(10)]})
def func_with_no_date(batch):
return Series({"c": 2})
def func_with_date(batch):
return Series({"b": datetime(2015, 1, 1), "c": 2})
dfg_no_conversion = df.groupby(by=["a"]).apply(func_with_no_date)
dfg_no_conversion_expected = DataFrame({"c": 2}, index=[1])
dfg_no_conversion_expected.index.name = "a"
dfg_conversion = df.groupby(by=["a"]).apply(func_with_date)
dfg_conversion_expected = DataFrame({"b": datetime(2015, 1, 1), "c": 2}, index=[1])
dfg_conversion_expected.index.name = "a"
tm.assert_frame_equal(dfg_no_conversion, dfg_no_conversion_expected)
tm.assert_frame_equal(dfg_conversion, dfg_conversion_expected)
def test_gb_apply_list_of_unequal_len_arrays():
# GH1738
df = DataFrame(
{
"group1": ["a", "a", "a", "b", "b", "b", "a", "a", "a", "b", "b", "b"],
"group2": ["c", "c", "d", "d", "d", "e", "c", "c", "d", "d", "d", "e"],
"weight": [1.1, 2, 3, 4, 5, 6, 2, 4, 6, 8, 1, 2],
"value": [7.1, 8, 9, 10, 11, 12, 8, 7, 6, 5, 4, 3],
}
)
df = df.set_index(["group1", "group2"])
df_grouped = df.groupby(level=["group1", "group2"], sort=True)
def noddy(value, weight):
out = np.array(value * weight).repeat(3)
return out
# the kernel function returns arrays of unequal length
# pandas sniffs the first one, sees it's an array and not
# a list, and assumed the rest are of equal length
# and so tries a vstack
# don't die
df_grouped.apply(lambda x: noddy(x.value, x.weight))
def test_groupby_apply_all_none():
# Tests to make sure no errors if apply function returns all None
# values. Issue 9684.
test_df = DataFrame({"groups": [0, 0, 1, 1], "random_vars": [8, 7, 4, 5]})
def test_func(x):
pass
result = test_df.groupby("groups").apply(test_func)
expected = DataFrame()
tm.assert_frame_equal(result, expected)
def test_groupby_apply_none_first():
# GH 12824. Tests if apply returns None first.
test_df1 = DataFrame({"groups": [1, 1, 1, 2], "vars": [0, 1, 2, 3]})
test_df2 = DataFrame({"groups": [1, 2, 2, 2], "vars": [0, 1, 2, 3]})
def test_func(x):
if x.shape[0] < 2:
return None
return x.iloc[[0, -1]]
result1 = test_df1.groupby("groups").apply(test_func)
result2 = test_df2.groupby("groups").apply(test_func)
index1 = MultiIndex.from_arrays([[1, 1], [0, 2]], names=["groups", None])
index2 = MultiIndex.from_arrays([[2, 2], [1, 3]], names=["groups", None])
expected1 = DataFrame({"groups": [1, 1], "vars": [0, 2]}, index=index1)
expected2 = DataFrame({"groups": [2, 2], "vars": [1, 3]}, index=index2)
tm.assert_frame_equal(result1, expected1)
tm.assert_frame_equal(result2, expected2)
def test_groupby_apply_return_empty_chunk():
# GH 22221: apply filter which returns some empty groups
df = DataFrame({"value": [0, 1], "group": ["filled", "empty"]})
groups = df.groupby("group")
result = groups.apply(lambda group: group[group.value != 1]["value"])
expected = Series(
[0],
name="value",
index=MultiIndex.from_product(
[["empty", "filled"], [0]], names=["group", None]
).drop("empty"),
)
tm.assert_series_equal(result, expected)
def test_apply_with_mixed_types():
# gh-20949
df = DataFrame({"A": "a a b".split(), "B": [1, 2, 3], "C": [4, 6, 5]})
g = df.groupby("A", group_keys=False)
result = g.transform(lambda x: x / x.sum())
expected = DataFrame({"B": [1 / 3.0, 2 / 3.0, 1], "C": [0.4, 0.6, 1.0]})
tm.assert_frame_equal(result, expected)
result = g.apply(lambda x: x / x.sum())
tm.assert_frame_equal(result, expected)
def test_func_returns_object():
# GH 28652
df = DataFrame({"a": [1, 2]}, index=Int64Index([1, 2]))
result = df.groupby("a").apply(lambda g: g.index)
expected = Series(
[Int64Index([1]), Int64Index([2])], index=Int64Index([1, 2], name="a")
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"group_column_dtlike",
[datetime.today(), datetime.today().date(), datetime.today().time()],
)
def test_apply_datetime_issue(group_column_dtlike):
# GH-28247
# groupby-apply throws an error if one of the columns in the DataFrame
# is a datetime object and the column labels are different from
# standard int values in range(len(num_columns))
df = DataFrame({"a": ["foo"], "b": [group_column_dtlike]})
result = df.groupby("a").apply(lambda x: Series(["spam"], index=[42]))
expected = DataFrame(
["spam"], Index(["foo"], dtype="object", name="a"), columns=[42]
)
tm.assert_frame_equal(result, expected)
def test_apply_series_return_dataframe_groups():
# GH 10078
tdf = DataFrame(
{
"day": {
0: pd.Timestamp("2015-02-24 00:00:00"),
1: pd.Timestamp("2015-02-24 00:00:00"),
2: pd.Timestamp("2015-02-24 00:00:00"),
3: pd.Timestamp("2015-02-24 00:00:00"),
4: pd.Timestamp("2015-02-24 00:00:00"),
},
"userAgent": {
0: "some UA string",
1: "some UA string",
2: "some UA string",
3: "another UA string",
4: "some UA string",
},
"userId": {
0: "17661101",
1: "17661101",
2: "17661101",
3: "17661101",
4: "17661101",
},
}
)
def most_common_values(df):
return Series({c: s.value_counts().index[0] for c, s in df.items()})
result = tdf.groupby("day").apply(most_common_values)["userId"]
expected = Series(
["17661101"], index=pd.DatetimeIndex(["2015-02-24"], name="day"), name="userId"
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("category", [False, True])
def test_apply_multi_level_name(category):
# https://github.com/pandas-dev/pandas/issues/31068
b = [1, 2] * 5
if category:
b = pd.Categorical(b, categories=[1, 2, 3])
expected_index = pd.CategoricalIndex([1, 2], categories=[1, 2, 3], name="B")
else:
expected_index = Index([1, 2], name="B")
df = DataFrame(
{"A": np.arange(10), "B": b, "C": list(range(10)), "D": list(range(10))}
).set_index(["A", "B"])
result = df.groupby("B").apply(lambda x: x.sum())
expected = DataFrame({"C": [20, 25], "D": [20, 25]}, index=expected_index)
tm.assert_frame_equal(result, expected)
assert df.index.names == ["A", "B"]
def test_groupby_apply_datetime_result_dtypes():
# GH 14849
data = DataFrame.from_records(
[
(pd.Timestamp(2016, 1, 1), "red", "dark", 1, "8"),
(pd.Timestamp(2015, 1, 1), "green", "stormy", 2, "9"),
(pd.Timestamp(2014, 1, 1), "blue", "bright", 3, "10"),
(pd.Timestamp(2013, 1, 1), "blue", "calm", 4, "potato"),
],
columns=["observation", "color", "mood", "intensity", "score"],
)
result = data.groupby("color").apply(lambda g: g.iloc[0]).dtypes
expected = Series(
[np.dtype("datetime64[ns]"), object, object, np.int64, object],
index=["observation", "color", "mood", "intensity", "score"],
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"index",
[
pd.CategoricalIndex(list("abc")),
pd.interval_range(0, 3),
pd.period_range("2020", periods=3, freq="D"),
MultiIndex.from_tuples([("a", 0), ("a", 1), ("b", 0)]),
],
)
def test_apply_index_has_complex_internals(index):
# GH 31248
df = DataFrame({"group": [1, 1, 2], "value": [0, 1, 0]}, index=index)
result = df.groupby("group", group_keys=False).apply(lambda x: x)
tm.assert_frame_equal(result, df)
@pytest.mark.parametrize(
"function, expected_values",
[
(lambda x: x.index.to_list(), [[0, 1], [2, 3]]),
(lambda x: set(x.index.to_list()), [{0, 1}, {2, 3}]),
(lambda x: tuple(x.index.to_list()), [(0, 1), (2, 3)]),
(
lambda x: {n: i for (n, i) in enumerate(x.index.to_list())},
[{0: 0, 1: 1}, {0: 2, 1: 3}],
),
(
lambda x: [{n: i} for (n, i) in enumerate(x.index.to_list())],
[[{0: 0}, {1: 1}], [{0: 2}, {1: 3}]],
),
],
)
def test_apply_function_returns_non_pandas_non_scalar(function, expected_values):
# GH 31441
df = DataFrame(["A", "A", "B", "B"], columns=["groups"])
result = df.groupby("groups").apply(function)
expected = Series(expected_values, index=Index(["A", "B"], name="groups"))
tm.assert_series_equal(result, expected)
def test_apply_function_returns_numpy_array():
# GH 31605
def fct(group):
return group["B"].values.flatten()
df = DataFrame({"A": ["a", "a", "b", "none"], "B": [1, 2, 3, np.nan]})
result = df.groupby("A").apply(fct)
expected = Series(
[[1.0, 2.0], [3.0], [np.nan]], index=Index(["a", "b", "none"], name="A")
)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize("function", [lambda gr: gr.index, lambda gr: gr.index + 1 - 1])
def test_apply_function_index_return(function):
# GH: 22541
df = DataFrame([1, 2, 2, 2, 1, 2, 3, 1, 3, 1], columns=["id"])
result = df.groupby("id").apply(function)
expected = Series(
[Index([0, 4, 7, 9]), Index([1, 2, 3, 5]), Index([6, 8])],
index=Index([1, 2, 3], name="id"),
)
tm.assert_series_equal(result, expected)
def test_apply_function_with_indexing_return_column():
# GH: 7002
df = DataFrame(
{
"foo1": ["one", "two", "two", "three", "one", "two"],
"foo2": [1, 2, 4, 4, 5, 6],
}
)
with tm.assert_produces_warning(FutureWarning, match="Select only valid"):
result = df.groupby("foo1", as_index=False).apply(lambda x: x.mean())
expected = DataFrame({"foo1": ["one", "three", "two"], "foo2": [3.0, 4.0, 4.0]})
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"udf",
[(lambda x: x.copy()), (lambda x: x.copy().rename(lambda y: y + 1))],
)
@pytest.mark.parametrize("group_keys", [True, False])
def test_apply_result_type(group_keys, udf):
# https://github.com/pandas-dev/pandas/issues/34809
# We'd like to control whether the group keys end up in the index
# regardless of whether the UDF happens to be a transform.
df = DataFrame({"A": ["a", "b"], "B": [1, 2]})
df_result = df.groupby("A", group_keys=group_keys).apply(udf)
series_result = df.B.groupby(df.A, group_keys=group_keys).apply(udf)
if group_keys:
assert df_result.index.nlevels == 2
assert series_result.index.nlevels == 2
else:
assert df_result.index.nlevels == 1
assert series_result.index.nlevels == 1
def test_result_order_group_keys_false():
# GH 34998
# apply result order should not depend on whether index is the same or just equal
df = DataFrame({"A": [2, 1, 2], "B": [1, 2, 3]})
result = df.groupby("A", group_keys=False).apply(lambda x: x)
expected = df.groupby("A", group_keys=False).apply(lambda x: x.copy())
tm.assert_frame_equal(result, expected)
def test_groupby_apply_group_keys_warns():
df = DataFrame({"A": [0, 1, 1], "B": [1, 2, 3]})
msg = "Not prepending group keys to the result index"
with tm.assert_produces_warning(FutureWarning, match=msg):
result = df.groupby("A").apply(lambda x: x)
tm.assert_frame_equal(result, df)
with tm.assert_produces_warning(FutureWarning, match=msg):
result = df.groupby("A")["B"].apply(lambda x: x)
tm.assert_series_equal(result, df["B"])
with tm.assert_produces_warning(FutureWarning, match=msg):
result = df["B"].groupby(df["A"]).apply(lambda x: x)
tm.assert_series_equal(result, df["B"])
def test_apply_with_timezones_aware():
# GH: 27212
dates = ["2001-01-01"] * 2 + ["2001-01-02"] * 2 + ["2001-01-03"] * 2
index_no_tz = pd.DatetimeIndex(dates)
index_tz = pd.DatetimeIndex(dates, tz="UTC")
df1 = DataFrame({"x": list(range(2)) * 3, "y": range(6), "t": index_no_tz})
df2 = DataFrame({"x": list(range(2)) * 3, "y": range(6), "t": index_tz})
result1 = df1.groupby("x", group_keys=False).apply(lambda df: df[["x", "y"]].copy())
result2 = df2.groupby("x", group_keys=False).apply(lambda df: df[["x", "y"]].copy())
tm.assert_frame_equal(result1, result2)
def test_apply_is_unchanged_when_other_methods_are_called_first(reduction_func):
# GH #34656
# GH #34271
warn = FutureWarning if reduction_func == "mad" else None
df = DataFrame(
{
"a": [99, 99, 99, 88, 88, 88],
"b": [1, 2, 3, 4, 5, 6],
"c": [10, 20, 30, 40, 50, 60],
}
)
expected = DataFrame(
{"a": [264, 297], "b": [15, 6], "c": [150, 60]},
index=Index([88, 99], name="a"),
)
# Check output when no other methods are called before .apply()
grp = df.groupby(by="a")
result = grp.apply(sum)
tm.assert_frame_equal(result, expected)
# Check output when another method is called before .apply()
grp = df.groupby(by="a")
args = get_groupby_method_args(reduction_func, df)
with tm.assert_produces_warning(warn, match="The 'mad' method is deprecated"):
_ = getattr(grp, reduction_func)(*args)
result = grp.apply(sum)
tm.assert_frame_equal(result, expected)
def test_apply_with_date_in_multiindex_does_not_convert_to_timestamp():
# GH 29617
df = DataFrame(
{
"A": ["a", "a", "a", "b"],
"B": [
date(2020, 1, 10),
date(2020, 1, 10),
date(2020, 2, 10),
date(2020, 2, 10),
],
"C": [1, 2, 3, 4],
},
index=Index([100, 101, 102, 103], name="idx"),
)
grp = df.groupby(["A", "B"])
result = grp.apply(lambda x: x.head(1))
expected = df.iloc[[0, 2, 3]]
expected = expected.reset_index()
expected.index = MultiIndex.from_frame(expected[["A", "B", "idx"]])
expected = expected.drop(columns="idx")
tm.assert_frame_equal(result, expected)
for val in result.index.levels[1]:
assert type(val) is date
def test_apply_by_cols_equals_apply_by_rows_transposed():
# GH 16646
# Operating on the columns, or transposing and operating on the rows
# should give the same result. There was previously a bug where the
# by_rows operation would work fine, but by_cols would throw a ValueError
df = DataFrame(
np.random.random([6, 4]),
columns=MultiIndex.from_product([["A", "B"], [1, 2]]),
)
by_rows = df.T.groupby(axis=0, level=0).apply(
lambda x: x.droplevel(axis=0, level=0)
)
by_cols = df.groupby(axis=1, level=0).apply(lambda x: x.droplevel(axis=1, level=0))
tm.assert_frame_equal(by_cols, by_rows.T)
tm.assert_frame_equal(by_cols, df)
@pytest.mark.parametrize("dropna", [True, False])
def test_apply_dropna_with_indexed_same(dropna):
# GH 38227
# GH#43205
df = DataFrame(
{
"col": [1, 2, 3, 4, 5],
"group": ["a", np.nan, np.nan, "b", "b"],
},
index=list("xxyxz"),
)
result = df.groupby("group", dropna=dropna, group_keys=False).apply(lambda x: x)
expected = df.dropna() if dropna else df.iloc[[0, 3, 1, 2, 4]]
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"as_index, expected",
[
[
False,
DataFrame(
[[1, 1, 1], [2, 2, 1]], columns=Index(["a", "b", None], dtype=object)
),
],
[
True,
Series(
[1, 1], index=MultiIndex.from_tuples([(1, 1), (2, 2)], names=["a", "b"])
),
],
],
)
def test_apply_as_index_constant_lambda(as_index, expected):
# GH 13217
df = DataFrame({"a": [1, 1, 2, 2], "b": [1, 1, 2, 2], "c": [1, 1, 1, 1]})
result = df.groupby(["a", "b"], as_index=as_index).apply(lambda x: 1)
tm.assert_equal(result, expected)
def test_sort_index_groups():
# GH 20420
df = DataFrame(
{"A": [1, 2, 3, 4, 5], "B": [6, 7, 8, 9, 0], "C": [1, 1, 1, 2, 2]},
index=range(5),
)
result = df.groupby("C").apply(lambda x: x.A.sort_index())
expected = Series(
range(1, 6),
index=MultiIndex.from_tuples(
[(1, 0), (1, 1), (1, 2), (2, 3), (2, 4)], names=["C", None]
),
name="A",
)
tm.assert_series_equal(result, expected)
def test_positional_slice_groups_datetimelike():
# GH 21651
expected = DataFrame(
{
"date": pd.date_range("2010-01-01", freq="12H", periods=5),
"vals": range(5),
"let": list("abcde"),
}
)
result = expected.groupby(
[expected.let, expected.date.dt.date], group_keys=False
).apply(lambda x: x.iloc[0:])
tm.assert_frame_equal(result, expected)
def test_groupby_apply_shape_cache_safety():
# GH#42702 this fails if we cache_readonly Block.shape
df = DataFrame({"A": ["a", "a", "b"], "B": [1, 2, 3], "C": [4, 6, 5]})
gb = df.groupby("A")
result = gb[["B", "C"]].apply(lambda x: x.astype(float).max() - x.min())
expected = DataFrame(
{"B": [1.0, 0.0], "C": [2.0, 0.0]}, index=Index(["a", "b"], name="A")
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("dropna", [True, False])
def test_apply_na(dropna):
# GH#28984
df = DataFrame(
{"grp": [1, 1, 2, 2], "y": [1, 0, 2, 5], "z": [1, 2, np.nan, np.nan]}
)
dfgrp = df.groupby("grp", dropna=dropna)
result = dfgrp.apply(lambda grp_df: grp_df.nlargest(1, "z"))
expected = dfgrp.apply(lambda x: x.sort_values("z", ascending=False).head(1))
tm.assert_frame_equal(result, expected)
def test_apply_empty_string_nan_coerce_bug():
# GH#24903
result = (
DataFrame(
{
"a": [1, 1, 2, 2],
"b": ["", "", "", ""],
"c": pd.to_datetime([1, 2, 3, 4], unit="s"),
}
)
.groupby(["a", "b"])
.apply(lambda df: df.iloc[-1])
)
expected = DataFrame(
[[1, "", pd.to_datetime(2, unit="s")], [2, "", pd.to_datetime(4, unit="s")]],
columns=["a", "b", "c"],
index=MultiIndex.from_tuples([(1, ""), (2, "")], names=["a", "b"]),
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("index_values", [[1, 2, 3], [1.0, 2.0, 3.0]])
def test_apply_index_key_error_bug(index_values):
# GH 44310
result = DataFrame(
{
"a": ["aa", "a2", "a3"],
"b": [1, 2, 3],
},
index=Index(index_values),
)
expected = DataFrame(
{
"b_mean": [2.0, 3.0, 1.0],
},
index=Index(["a2", "a3", "aa"], name="a"),
)
result = result.groupby("a").apply(
lambda df: Series([df["b"].mean()], index=["b_mean"])
)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"arg,idx",
[
[
[
1,
2,
3,
],
[
0.1,
0.3,
0.2,
],
],
[
[
1,
2,
3,
],
[
0.1,
0.2,
0.3,
],
],
[
[
1,
4,
3,
],
[
0.1,
0.4,
0.2,
],
],
],
)
def test_apply_nonmonotonic_float_index(arg, idx):
# GH 34455
expected = DataFrame({"col": arg}, index=idx)
result = expected.groupby("col", group_keys=False).apply(lambda x: x)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("args, kwargs", [([True], {}), ([], {"numeric_only": True})])
def test_apply_str_with_args(df, args, kwargs):
# GH#46479
gb = df.groupby("A")
result = gb.apply("sum", *args, **kwargs)
expected = gb.sum(numeric_only=True)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize("name", ["some_name", None])
def test_result_name_when_one_group(name):
# GH 46369
ser = Series([1, 2], name=name)
result = ser.groupby(["a", "a"], group_keys=False).apply(lambda x: x)
expected = Series([1, 2], name=name)
tm.assert_series_equal(result, expected)
@pytest.mark.parametrize(
"method, op",
[
("apply", lambda gb: gb.values[-1]),
("apply", lambda gb: gb["b"].iloc[0]),
("agg", "mad"),
("agg", "skew"),
("agg", "prod"),
("agg", "sum"),
],
)
def test_empty_df(method, op):
# GH 47985
empty_df = DataFrame({"a": [], "b": []})
gb = empty_df.groupby("a", group_keys=True)
group = getattr(gb, "b")
result = getattr(group, method)(op)
expected = Series(
[], name="b", dtype="float64", index=Index([], dtype="float64", name="a")
)
tm.assert_series_equal(result, expected)
def test_numeric_only_warning_numpy():
# GH#50538
df = DataFrame({"a": [1, 1, 2], "b": list("xyz"), "c": [3, 4, 5]})
gb = df.groupby("a")
msg = "The operation <function mean.*failed"
# Warning is raised from within NumPy
with tm.assert_produces_warning(FutureWarning, match=msg, check_stacklevel=False):
gb.apply(np.mean)
# Ensure users can't pass numeric_only
with pytest.raises(TypeError, match="got an unexpected keyword argument"):
gb.apply(np.mean, numeric_only=True)