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TST: setitem preserving period[D] dtype #52704

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Merged
merged 4 commits into from
Apr 19, 2023
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TST: setitem preserving period[D] dtype #52704

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TST: setitem preserving period[D] dtype
srkds Apr 17, 2023
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Merge remote-tracking branch 'upstream/main' into test/39763/setitem_…
srkds Apr 19, 2023
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srkds Apr 19, 2023
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change function name
srkds Apr 19, 2023
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9 changes: 9 additions & 0 deletions pandas/tests/frame/indexing/test_setitem.py
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Original file line number Diff line number Diff line change
Expand Up @@ -379,6 +379,15 @@ def test_setitem_complete_column_with_array(self):
assert expected["d"].dtype == arr.dtype
tm.assert_frame_equal(df, expected)

def test_setitem_periodd_dtype(self):
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typo extra "d"

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typo extra "d"

Originally its period[D] but to name a function I thought this would be fine.

But I think there are ways we can rename

  1. ..._period_d_dtpye(self)
  2. ..._period_dtype(self)

What do you think?

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_period_d_dtype is fine

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Okay! sounds good 👍
Thanks

# GH 39763
rng = period_range("2016-01-01", periods=9, freq="D", name="A")
df = DataFrame(rng)
expected = df.dtypes
df.iloc[:] = rng._na_value
result = df.dtypes
tm.assert_equal(result, expected)
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Please construct the expected DataFrame and use tm.assert_frame_equal

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Please construct the expected DataFrame and use tm.assert_frame_equal

I'm a bit confused here, I thought that way but after executing this line df.iloc[:] = rng._na_value we get following o/p

# o/p of  df.iloc[:] = rng._na_value
	A
0	NaT
1	NaT
2	NaT
3	NaT
4	NaT
5	NaT
6	NaT
7	NaT
8	NaT

# And it's data type
# -> df.dtypes
A    period[D]
dtype: object

and that was a result.

but for expected when I tried creating the same data frame and changing its type to period[D] it did not work.

expected = pd.DataFrame({"A": ['NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT', 'NaT']})
expected.astype(obj.dtypes) # 1 did not work
expected.astype("period[D]") # 2 did not work
expected.dtypes

# o/p
A    object
dtype: object

as per your suggestion, we can create a new data frame using period_range and compare using tm.assert_frame_equal here dtypes would be the same but the data is different so the test will fail.

I hope I'm able to describe the confusion clearly here.
Please let me know your thoughts on this.

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In [3]: pd.DataFrame({"A": ["NaT"]}, dtype="period[D]")
Out[3]: 
     A
0  NaT

In [4]: pd.DataFrame({"A": ["NaT"]}, dtype="period[D]").dtypes
Out[4]: 
A    period[D]
dtype: object


@pytest.mark.parametrize("dtype", ["f8", "i8", "u8"])
def test_setitem_bool_with_numeric_index(self, dtype):
# GH#36319
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