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Doc: Adds example of joining a series to a dataframe. #19235

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25 changes: 25 additions & 0 deletions doc/source/merging.rst
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Expand Up @@ -714,6 +714,31 @@ either the left or right tables, the values in the joined table will be
labels=['left', 'right'], vertical=False);
plt.close('all');

To join a Series and a DataFrame, the Series has to be transformed into a DataFrame first:
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Explain that the Series is a MultiIndexed series, and that's what you're joining on. Something like

To join a Series with a MultiIndex and a DataFrame, using the levels of the
MultiIndex and columns from the DataFrame, transform the Series to a DataFrame
using :meth:`Series.reset_index`.


.. ipython:: python

df = pd.DataFrame({"Let": ["A", "B", "C"], "Num": [1, 2, 3]})
df

# The series has a multi-index with levels corresponding to columns in the DataFrame we want to merge with
ser = pd.Series(
['a', 'b', 'c', 'd', 'e', 'f'],
index=pd.MultiIndex.from_arrays([["A", "B", "C"]*2, [1, 2, 3, 4, 5, 6]])
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Use the names parameter here, instead of setting it below.

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Spaces around the *

)
ser

# Name the row index levels
ser.index.names=['Let','Num']
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Space after the comma.

ser

# reset_index turns the multi-level row index into columns, which requires a DataFrame
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I think this can be removed, to make the example shorter. I'd just write it as

# Convert the Series to a DataFrame and merge
pd.merge(df, ser.reset_index(), on=['Let', 'Num'])

df2 = ser.reset_index()
type(df2)

# Now we merge the DataFrames
pd.merge(df, df2, on=['Let','Num'])
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Space after the comma.

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will do


Here is another example with duplicate join keys in ``DataFrame``s:

.. ipython:: python
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