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added test to indexing on groupby, #32464 #44046

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239d42a
added test to indexing on groupby, #32464
gabrieldi95 Oct 16, 2021
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gabrieldi95 Oct 16, 2021
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Merge remote-tracking branch 'upstream/master' into tst_issue_32464
gabrieldi95 Oct 16, 2021
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changed file and asserting method
gabrieldi95 Oct 17, 2021
c89f4ad
removed test to see if df has multiindex
gabrieldi95 Oct 17, 2021
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Merge remote-tracking branch 'upstream/master' into tst_issue_32464
gabrieldi95 Oct 17, 2021
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changed assert method
gabrieldi95 Oct 17, 2021
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gabrieldi95 Oct 17, 2021
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gabrieldi95 Nov 17, 2021
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using multiindex constructor
gabrieldi95 Nov 17, 2021
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gabrieldi95 Nov 19, 2021
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gabrieldi95 Nov 28, 2021
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gabrieldi95 Feb 16, 2022
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gabrieldi95 Feb 22, 2022
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32 changes: 32 additions & 0 deletions pandas/tests/groupby/test_function.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1167,6 +1167,38 @@ def test_groupby_sum_below_mincount_nullable_integer():
tm.assert_frame_equal(result, expected)


def test_if_is_multiindex():
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Can you rename to something like test_empty_multiindex

# GH 32464
# Test if index after groupby with more then one column is always MultiIndex
a = DataFrame({"a": [1, 2], "b": [5, 6], "c": [8, 9]})

agg_1 = a.groupby(["a", "b"]).sum()
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Instead, assert the index of these two results independently.

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Sorry, I don't get what you mean...
You mean something like this?

tm.assert_index_equal(a.groupby(["a", "b", "c"]).sum().index, pd.MultiIndex.from_arrays([[], [], []], names=("a", "b", "c")))
tm.assert_index_equal(a.groupby(["a", "b"]).sum().index, pd.MultiIndex.from_arrays([[], []], names=("a", "b")))

I tried but it doesn't work, it says AssertionError: MultiIndex level [0] are different

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But I tried this and it passed:

def test_if_is_multiindex():
    # GH 32464
    # Test if index after groupby with more then one column is always MultiIndex
    a = DataFrame({"a": [1, 2], "b": [5, 6], "c": [8, 9]})

    result = a.groupby(["a", "b"]).sum().index
    expected = pd.MultiIndex.from_arrays([[1,2],[5,6]], names=("a", "b"))
    
    tm.assert_index_equal(result, expected)

    result = a.groupby(["a", "b", "c"]).sum().index
    expected = pd.MultiIndex.from_arrays([[1,2],[5,6],[8,9]], names=("a", "b", "c"))

    tm.assert_index_equal(result, expected)

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You can build the index using the pd.MultiIndex constructor directly.

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Can you just test in the case of an empty frame here - nonempty is covered elsewhere. Indeed it is tricky to construct an empty MultiIndex of the expected type; this works:

df = DataFrame({"a": [1], "b": [2], "c": [3]}).set_index(['a', 'b', 'c'])
result = df.groupby(["a", "b", "c"]).sum().index[:0]
expected = df.index[:0]
tm.assert_index_equal(result, expected)

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@gabrieldi95 can you replace the test with basically this example

agg_2 = a.groupby(["a", "b", "c"]).sum()

expected = MultiIndex(
levels=[[1, 2], [5, 6]], codes=[[0, 1], [0, 1]], names=["a", "b"]
)
result = agg_1.index

tm.assert_index_equal(expected, result)

expected = MultiIndex(
levels=[[1, 2], [5, 6], [8, 9]],
codes=[[0, 1], [0, 1], [0, 1]],
names=["a", "b", "c"],
)
result = agg_2.index

# Tests if group by with all columns has a MultiIndex
tm.assert_index_equal(expected, result)

index_1 = agg_1.iloc[:, :0].index
index_2 = agg_2.droplevel("c").index

# Tests if both agreggations have multiindex
tm.assert_index_equal(index_1, index_2)


def test_mean_on_timedelta():
# GH 17382
df = DataFrame({"time": pd.to_timedelta(range(10)), "cat": ["A", "B"] * 5})
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