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DOC: Improve documentation for DataFrame.__setitem__ and .loc assignment from Series #61662

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@cxder-77

Pandas version checks

  • I have checked that the issue still exists on the latest versions of the docs on main here

Location of the documentation

pandas.DataFrame.setitem
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.__setitem__.html

pandas.core.indexing.IndexingMixin.loc
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html

User Guide: Indexing and Selecting Data
https://pandas.pydata.org/docs/user_guide/indexing.html

Documentation problem

Documentation Enhancement*

The following behavior is not clearly explained in the documentation:

```python
import pandas as pd

df = pd.DataFrame({'a': [1, 2, 3]})
df['b'] = pd.Series({1: 'b'})
print(df)

# Output:
#    a    b
# 0  1  NaN
# 1  2    b
# 2  3  NaN
```

- The Series is **reindexed** to match the DataFrame index.
- Values are inserted **by index label**, not by position.
- Missing labels yield **NaN**, and the order is adjusted accordingly.

This behavior is:
- Not explained in the `__setitem__` documentation (which is missing entirely).
- Only mentioned vaguely in `.loc` docs, with no example.
- Absent from the "Indexing and Selecting Data" user guide when assigning Series with unordered or partial index.

Suggested fix for documentation

  1. Add docstring for DataFrame.__setitem__ with clear explanation that:
    > When assigning a Series, pandas aligns on index. Values in the Series that don't match an index label will result in NaN.

    1. Update .loc documentation:
      Include a note that when assigning a Series to .loc[row_labels, col], pandas aligns the Series by index and not by order.

    2. Add example in the User Guide under:
      Indexing and Selecting Data

      Assigning a Series with unordered/missing index keys to a DataFrame column.

    Suggested example:

    df = pd.DataFrame({'a': [1, 2, 3]})
    s = pd.Series({2: 'zero', 1: 'one', 0: 'two'})
    df['d'] = s
    
    # Output:
    #    a     d
    # 0  1   two
    # 1  2   one
    # 2  3  zero

    📈 Why this is better:

    The current documentation is incomplete and vague about how Series alignment works in assignments. This fix:

    • Makes __setitem__ behavior explicit and discoverable.
    • Improves .loc docs with better clarity and practical context.
    • Adds real-world examples to the user guide to reduce silent bugs and confusion.

    These improvements help all users—especially beginners—understand how pandas handles Series assignment internally.

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