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DOC: Fix Series nsmallest and nlargest docstring/doctests #22731

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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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2 changes: 1 addition & 1 deletion ci/doctests.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ if [ "$DOCTEST" ]; then
fi

pytest --doctest-modules -v pandas/core/series.py \
-k"-nlargest -nonzero -nsmallest -reindex -searchsorted -to_dict"
-k"-nonzero -reindex -searchsorted -to_dict"

if [ $? -ne "0" ]; then
RET=1
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187 changes: 143 additions & 44 deletions pandas/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2743,17 +2743,20 @@ def nlargest(self, n=5, keep='first'):

Parameters
----------
n : int
Return this many descending sorted values
keep : {'first', 'last'}, default 'first'
Where there are duplicate values:
- ``first`` : take the first occurrence.
- ``last`` : take the last occurrence.
n : int, default 5
Return this many descending sorted values.
keep : {'first', 'last', 'all'}, default 'first'
When there are duplicate values that cannot all fit in a
Series of `n` elements:
- ``first`` : take the first occurrences based on the index order
- ``last`` : take the last occurrences based on the index order
- ``all`` : keep all occurrences. This can result in a Series of
size larger than `n`.
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Is the period here on the last bullet required to pass the docstring validation as-is? Shouldn't be necessary but if that's the intent here just something we should address separately @datapythonista

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I confirm that the validation fails if the last period is not present.


Returns
-------
top_n : Series
The n largest values in the Series, in sorted order
Series
The `n` largest values in the Series, sorted in decreasing order.

Notes
-----
Expand All @@ -2762,23 +2765,70 @@ def nlargest(self, n=5, keep='first'):

See Also
--------
Series.nsmallest
Series.nsmallest: Get the `n` smallest elements.
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I'd add sort_values and head here, as they're mentioned in the notes. In both docstrings.

Series.sort_values: Sort Series by values.
Series.head: Return the first `n` rows.

Examples
--------
>>> s = pd.Series(np.random.randn(10**6))
>>> s.nlargest(10) # only sorts up to the N requested
219921 4.644710
82124 4.608745
421689 4.564644
425277 4.447014
718691 4.414137
43154 4.403520
283187 4.313922
595519 4.273635
503969 4.250236
121637 4.240952
dtype: float64
>>> countries_population = {"Italy": 59000000, "France": 65000000,
... "Malta": 434000, "Maldives": 434000,
... "Brunei": 434000, "Iceland": 337000,
... "Nauru": 11300, "Tuvalu": 11300,
... "Anguilla": 11300, "Monserat": 5200}
>>> s = pd.Series(countries_population)
>>> s
Italy 59000000
France 65000000
Malta 434000
Maldives 434000
Brunei 434000
Iceland 337000
Nauru 11300
Tuvalu 11300
Anguilla 11300
Monserat 5200
dtype: int64

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

>>> s.nlargest()
France 65000000
Italy 59000000
Malta 434000
Maldives 434000
Brunei 434000
dtype: int64

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

>>> s.nlargest(3)
France 65000000
Italy 59000000
Malta 434000
dtype: int64

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.

>>> s.nlargest(3, keep='last')
France 65000000
Italy 59000000
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.

>>> s.nlargest(3, keep='all')
France 65000000
Italy 59000000
Malta 434000
Maldives 434000
Brunei 434000
dtype: int64
"""
return algorithms.SelectNSeries(self, n=n, keep=keep).nlargest()

Expand All @@ -2788,17 +2838,20 @@ def nsmallest(self, n=5, keep='first'):

Parameters
----------
n : int
Return this many ascending sorted values
keep : {'first', 'last'}, default 'first'
Where there are duplicate values:
- ``first`` : take the first occurrence.
- ``last`` : take the last occurrence.
n : int, default 5
Return this many ascending sorted values.
keep : {'first', 'last', 'all'}, default 'first'
When there are duplicate values that cannot all fit in a
Series of `n` elements:
- ``first`` : take the first occurrences based on the index order
- ``last`` : take the last occurrences based on the index order
- ``all`` : keep all occurrences. This can result in a Series of
size larger than `n`.

Returns
-------
bottom_n : Series
The n smallest values in the Series, in sorted order
Series
The `n` smallest values in the Series, sorted in increasing order.

Notes
-----
Expand All @@ -2807,23 +2860,69 @@ def nsmallest(self, n=5, keep='first'):

See Also
--------
Series.nlargest
Series.nlargest: Get the `n` largest elements.
Series.sort_values: Sort Series by values.
Series.head: Return the first `n` rows.

Examples
--------
>>> s = pd.Series(np.random.randn(10**6))
>>> s.nsmallest(10) # only sorts up to the N requested
288532 -4.954580
732345 -4.835960
64803 -4.812550
446457 -4.609998
501225 -4.483945
669476 -4.472935
973615 -4.401699
621279 -4.355126
773916 -4.347355
359919 -4.331927
dtype: float64
>>> countries_population = {"Italy": 59000000, "France": 65000000,
... "Brunei": 434000, "Malta": 434000,
... "Maldives": 434000, "Iceland": 337000,
... "Nauru": 11300, "Tuvalu": 11300,
... "Anguilla": 11300, "Monserat": 5200}
>>> s = pd.Series(countries_population)
>>> s
Italy 59000000
France 65000000
Brunei 434000
Malta 434000
Maldives 434000
Iceland 337000
Nauru 11300
Tuvalu 11300
Anguilla 11300
Monserat 5200
dtype: int64

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

>>> s.nsmallest()
Monserat 5200
Nauru 11300
Tuvalu 11300
Anguilla 11300
Iceland 337000
dtype: int64

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.

>>> 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
that the returned Series has four elements due to the three duplicates.

>>> s.nsmallest(3, keep='all')
Monserat 5200
Nauru 11300
Tuvalu 11300
Anguilla 11300
dtype: int64
"""
return algorithms.SelectNSeries(self, n=n, keep=keep).nsmallest()

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