TST: setitem preserving period[D] dtype #52704
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| 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: objectand 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: objectas 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
mroeschke
added
the
Testing
| assert expected["d"].dtype == arr.dtype | ||
| tm.assert_frame_equal(df, expected) | ||
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| def test_setitem_periodd_dtype(self): |
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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
..._period_d_dtpye(self)..._period_dtype(self)
What do you think?
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Okay! sounds good 👍
Thanks
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Thanks @srkds |
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Thanks @mroeschke and @jbrockmendel for review ❤😀 |
3 participants
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.