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6 months ago
import numpy as np
import pandas as pd
import pandas._testing as tm
def test_group_by_copy():
# GH#44803
df = pd.DataFrame(
{
"name": ["Alice", "Bob", "Carl"],
"age": [20, 21, 20],
}
).set_index("name")
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
grp_by_same_value = df.groupby(["age"], group_keys=False).apply(
lambda group: group
)
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
grp_by_copy = df.groupby(["age"], group_keys=False).apply(
lambda group: group.copy()
)
tm.assert_frame_equal(grp_by_same_value, grp_by_copy)
def test_mutate_groups():
# GH3380
df = pd.DataFrame(
{
"cat1": ["a"] * 8 + ["b"] * 6,
"cat2": ["c"] * 2
+ ["d"] * 2
+ ["e"] * 2
+ ["f"] * 2
+ ["c"] * 2
+ ["d"] * 2
+ ["e"] * 2,
"cat3": [f"g{x}" for x in range(1, 15)],
"val": np.random.default_rng(2).integers(100, size=14),
}
)
def f_copy(x):
x = x.copy()
x["rank"] = x.val.rank(method="min")
return x.groupby("cat2")["rank"].min()
def f_no_copy(x):
x["rank"] = x.val.rank(method="min")
return x.groupby("cat2")["rank"].min()
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
grpby_copy = df.groupby("cat1").apply(f_copy)
with tm.assert_produces_warning(DeprecationWarning, match=msg):
grpby_no_copy = df.groupby("cat1").apply(f_no_copy)
tm.assert_series_equal(grpby_copy, grpby_no_copy)
def test_no_mutate_but_looks_like():
# GH 8467
# first show's mutation indicator
# second does not, but should yield the same results
df = pd.DataFrame({"key": [1, 1, 1, 2, 2, 2, 3, 3, 3], "value": range(9)})
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(DeprecationWarning, match=msg):
result1 = df.groupby("key", group_keys=True).apply(lambda x: x[:].key)
with tm.assert_produces_warning(DeprecationWarning, match=msg):
result2 = df.groupby("key", group_keys=True).apply(lambda x: x.key)
tm.assert_series_equal(result1, result2)
def test_apply_function_with_indexing(warn_copy_on_write):
# GH: 33058
df = pd.DataFrame(
{"col1": ["A", "A", "A", "B", "B", "B"], "col2": [1, 2, 3, 4, 5, 6]}
)
def fn(x):
x.loc[x.index[-1], "col2"] = 0
return x.col2
msg = "DataFrameGroupBy.apply operated on the grouping columns"
with tm.assert_produces_warning(
DeprecationWarning, match=msg, raise_on_extra_warnings=not warn_copy_on_write
):
result = df.groupby(["col1"], as_index=False).apply(fn)
expected = pd.Series(
[1, 2, 0, 4, 5, 0],
index=pd.MultiIndex.from_tuples(
[(0, 0), (0, 1), (0, 2), (1, 3), (1, 4), (1, 5)]
),
name="col2",
)
tm.assert_series_equal(result, expected)
def test_apply_mutate_columns_multiindex():
# GH 12652
df = pd.DataFrame(
{
("C", "julian"): [1, 2, 3],
("B", "geoffrey"): [1, 2, 3],
("A", "julian"): [1, 2, 3],
("B", "julian"): [1, 2, 3],
("A", "geoffrey"): [1, 2, 3],
("C", "geoffrey"): [1, 2, 3],
},
columns=pd.MultiIndex.from_tuples(
[
("A", "julian"),
("A", "geoffrey"),
("B", "julian"),
("B", "geoffrey"),
("C", "julian"),
("C", "geoffrey"),
]
),
)
def add_column(grouped):
name = grouped.columns[0][1]
grouped["sum", name] = grouped.sum(axis=1)
return grouped
msg = "DataFrame.groupby with axis=1 is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
gb = df.groupby(level=1, axis=1)
result = gb.apply(add_column)
expected = pd.DataFrame(
[
[1, 1, 1, 3, 1, 1, 1, 3],
[2, 2, 2, 6, 2, 2, 2, 6],
[
3,
3,
3,
9,
3,
3,
3,
9,
],
],
columns=pd.MultiIndex.from_tuples(
[
("geoffrey", "A", "geoffrey"),
("geoffrey", "B", "geoffrey"),
("geoffrey", "C", "geoffrey"),
("geoffrey", "sum", "geoffrey"),
("julian", "A", "julian"),
("julian", "B", "julian"),
("julian", "C", "julian"),
("julian", "sum", "julian"),
]
),
)
tm.assert_frame_equal(result, expected)