ENH: column-wise DataFrame.fillna with Series/Dict value #38352
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5 tasks
arw2019
added
Enhancement
Missing-data
jreback
requested changes
Dec 14, 2020
doc/source/whatsnew/v1.2.0.rst
Outdated
| @@ -260,6 +260,7 @@ Other enhancements | |||
| - Added :meth:`~DataFrame.set_flags` for setting table-wide flags on a Series or DataFrame (:issue:`28394`) | |||
| - :meth:`DataFrame.applymap` now supports ``na_action`` (:issue:`23803`) | |||
| - :class:`Index` with object dtype supports division and multiplication (:issue:`34160`) | |||
| - :meth:`DataFrame.fillna` can fill NA values column-wise with a dictionary or :class:`Series` (:issue:`4514`) | |||
| df = DataFrame([[np.nan, 2], [3, np.nan], [np.nan, np.nan]], columns=list("AB")) | ||
| s = Series([100, 200, 300]) | ||
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| expected = DataFrame( |
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what happens if we have a datetime column mixed in here?
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We end up with object dtype:
In [5]: df = pd.DataFrame([[np.nan, 2], [3, np.nan], [np.nan, np.nan]], columns=list("AB"))
...: s = pd.Series(pd.to_datetime([100, 200, 300], unit="ns"))
...:
...: result = df.fillna(s, axis=1, downcast="infer")
...: result
Out[5]:
A B
0 1970-01-01 00:00:00.000000100 2.0
1 3.0 1970-01-01 00:00:00.000000200
2 1970-01-01 00:00:00.000000300 1970-01-01 00:00:00.000000300
In [6]: result.dtypes
Out[6]:
A object
B object
dtype: objectIn [8]: df = pd.DataFrame({"A": pd.to_datetime([np.nan, 2, np.nan]), "B": pd.to_datetime([3, np.nan, np.nan])})
...: s = pd.Series([100, 200, 300])
...:
...: result = df.fillna(s, axis=1, downcast="infer")
...: result
Out[8]:
A B
0 100 1970-01-01 00:00:00.000000003
1 1970-01-01 00:00:00.000000002 200
2 300 300
In [9]: result.dtypes
Out[9]:
A object
B object
|
Green + addressed comments |
|
Thanks for the PR but it appears this has gone stale. Looks fairly close but let us know if you'd like to continue by merging master and targeting 1.4 |
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black pandasgit diff upstream/master -u -- "*.py" | flake8 --diffPicking up #30922