8000 DOC: Fix Series nsmallest and nlargest docstring/doctests by Moisan · Pull Request #22731 · pandas-dev/pandas · GitHub
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DOC: Fix Series nsmallest and nlargest docstring/doctests #22731

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Merged
merged 5 commits into from
Sep 18, 2018
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DOC: Fix Series nsmallest and nlargest docstring/doctests #22731

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724610f
DOC: Fix Series nsmallest and nlargest docstring/doctests
Sep 17, 2018
1af1280
Fix a typo in nsmallest doctest
Sep 17, 2018
5c881f9
Add quick descriptions in the doctests of Series.nlargest and Series.…
Sep 17, 2018
5d6d5ed
Update nlargest and nsmallest docstring with backticks
Sep 18, 2018
7f311f9
Various changes to nlargest and nsmallest based on datapythonista review
Sep 18, 2018
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Update nlargest and nsmallest docstring with backticks
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Thierry Moisan committed Sep 18, 2018
commit 5d6d5ed08a6ec75169ecd5df8dba360a6fe41264
31 changes: 15 additions & 16 deletions pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2756,7 +2756,7 @@ def nlargest(self, n=5, keep='first'):
Returns
-------
Series
The n largest values in the Series, sorted in decreasing order.
The `n` largest values in the Series, sorted in decreasing order.

Notes
-----
Expand Down Expand Up @@ -2788,7 +2788,7 @@ def nlargest(self, n=5, keep='first'):
Monserat 5200
dtype: int64

The n largest elements where n=5 by default.
The `n` largest elements where ``n=5`` by default.

>>> s.nlargest()
France 65000000
Expand All @@ -2798,17 +2798,16 @@ def nlargest(self, n=5, keep='first'):
Brunei 434000
dtype: int64

The n largest elements where n=3. Default keep value is 'first' so
Malta will
be kept.
The `n` largest elements where ``n=3``. Default `keep` value is 'first'
so Malta will be kept.

>>> s.nlargest(3)
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I think it would be nice to have just a quick one-liner to highlight the difference between this and the subsequent example

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Do you mean a comment at the end of the line?

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No just some text in between the examples to call out what the user should be looking at

France 65000000
Italy 59000000
Malta 434000
dtype: int64

The n largest elements where n=3 and keeping the last duplicates.
The `n` largest elements where ``n=3`` and keeping the last duplicates.
Brunei will be kept since it is the last with value 434000 based on
the index order.

Expand All @@ -2818,8 +2817,8 @@ def nlargest(self, n=5, keep='first'):
Brunei 434000
dtype: int64

The n largest elements where n=3 with all duplicates kept. Note that the
returned Series has five elements due to the three duplicates.
The `n` largest elements where ``n=3`` with all duplicates kept. Note
that the returned Series has five elements due to the three duplicates.

>>> s.nlargest(3, keep='all')
France 65000000
Expand Down Expand Up @@ -2850,7 +2849,7 @@ def nsmallest(self, n=5, keep='first'):
Returns
-------
Series
The n smallest values in the Series, sorted in increasing order.
The `n` smallest values in the Series, sorted in increasing order.

Notes
-----
Expand Down Expand Up @@ -2882,7 +2881,7 @@ def nsmallest(self, n=5, keep='first'):
Monserat 5200
dtype: int64

The n largest elements where n=5 by default.
The `n` largest elements where ``n=5`` by default.

>>> s.nsmallest()
Monserat 5200
Expand All @@ -2892,26 +2891,26 @@ def nsmallest(self, n=5, keep='first'):
Iceland 337000
dtype: int64

The n smallest elements where n=3. Default keep value is 'first' so
Nauru and Tuvalu will be kept.
The `n` smallest elements where ``n=3``. Default `keep` value is
'first' so Nauru and Tuvalu will be kept.

>>> s.nsmallest(3)
Monserat 5200
Nauru 11300
Tuvalu 11300
dtype: int64

The n smallest elements where n=3 and keeping the last duplicates.
Anguilla and Tuvalu will be kept since they are the last with value
11300 based on the index order.
The `n` smallest elements where ``n=3`` and keeping the last
duplicates. Anguilla and Tuvalu will be kept since they are the last
with value 11300 based on the index order.

>>> s.nsmallest(3, keep='last')
Monserat 5200
Anguilla 11300
Tuvalu 11300
dtype: int64

The n smallest elements where n=3 with all duplicates kept. Note
The `n` smallest elements where ``n=3`` with all duplicates kept. Note
that the returned Series has four elements due to the three duplicates.

>>> s.nsmallest(3, keep='all')
Expand Down
0