DOC: Improve documentation for DataFrame.__setitem__ and .loc assignment from Series #61804
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@WillAyd Any thought on this resolution? |
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Thanks @niruta25 for the PR
I see that "align" is found 16 times when searching "Intro to data structures" section of the docs. This chapter is only preceded by "10 minutes to pandas" so i'm not sure that the linked issue which states "The current documentation is incomplete and vague about how Series alignment works in assignments." is correct that this fundamental paradigm of pandas is not covered in the documentation. I'm not a member of the documentation team so others may be more positive to these changes, but if I was to review this PR, I would prefer to see more discussion on the issue itself before proceeding to the PR stage. |
doc/source/user_guide/indexing.rst
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| #If you want positional assignment instead of index alignment: | ||
| # Convert Series to array/list for positional assignment | ||
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| df['positional'] = s1.values # or s1.tolist() |
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Using .values is typically discouraged, so I think should remove this
doc/source/user_guide/indexing.rst
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| df['positional'] = s1.values # or s1.tolist() | ||
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| # Or reset the Series index to match DataFrame index | ||
| df['reset_index'] = s1.reindex(df.index) |
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Nit but I think naming the column reset_index here is distracting; maybe just call s1_values?
| by index labels, not by position. This means: | ||
| * Values from the Series are matched to DataFrame rows by index label | ||
| * If a Series index label doesn't exist in the DataFrame index, it's ignored |
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I don't follow the difference between this and the line directly following it with the distinction of ignored versus NaN - can you help me understand?
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- Series index labels NOT in DataFrame → IGNORED
df = pd.DataFrame({'A': [1, 2, 3]}, index=['x', 'y', 'z'])
s = pd.Series([10, 20, 30, 40, 50], index=['x', 'y', 'a', 'b', 'z'])
df['B'] = s
df
A B
x 1 10
y 2 20
z 3 50
# Values for 'a' and 'b' are completely ignored!
- DataFrame index labels NOT in Series → NaN
df = pd.DataFrame({'A': [1, 2, 3, 4]}, index=['x', 'y', 'z', 'w'])
s = pd.Series([100, 300], index=['x', 'z']) # Missing 'y' and 'w'
df['B'] = s
df
A B
x 1 100.0
y 2 NaN
z 3 300.0
w 4 NaN
# Rows 'y' and 'w' get NaN because they're missing from Series
Added the ignored example to documentation.
pandas/core/indexing.py
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| **Assignment with Series** | ||
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| When assigning a Series to .loc[row_indexer, col_indexer], pandas aligns | ||
| the Series by index labels, not by order or position. This is consistent |
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I think you can remove the line This is consistent with pandas' general alignment behavior.
pandas/tests/frame/test_api.py
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| @@ -378,3 +378,22 @@ def test_inspect_getmembers(self): | |||
| # GH38740 | |||
| df = DataFrame() | |||
| inspect.getmembers(df) | |||
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| def test_setitem_series_alignment_documentation(self): | |||
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I definitely appreciate you adding tests, but since this is a documentation change you shouldn't need to add anything here. Feel free to remove
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ok! Will generate another PR to add these tests in. Would be good to have.
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#61822 for follow up for adding tests.
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Was there an original discussion on tests being required? From a glance at them I would think our existing test base already covers those use cases, but maybe I am overlooking something
doc/source/whatsnew/vX.X.X.rstfile if fixing a bug or adding a new feature.The core problem is that when assigning a Series, pandas aligns on index and values in the Series that don't match an index label will result in NaN DOC: Improve documentation for DataFrame.setitem and .loc assignment from Series · Issue #61662 · pandas-dev/pandas, but this behavior is poorly documented.
My proposed solution addresses the issue comprehensively by:
The fix emphasizes that pandas performs index-based alignment rather than positional assignment, which is the source of confusion for many users. The documentation will now clearly explain that when you assign a Series to a DataFrame column, pandas matches values by index labels, not by position, and missing labels result in NaN values.
This solution follows pandas' documentation conventions and provides both reference documentation and practical examples that will help users understand and correctly use this important feature.