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

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Sep 18, 2018
Merged

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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157 changes: 114 additions & 43 deletions pandas/core/series.py
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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 : str, default 'first'
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I think it's better to keep {'first', 'last', 'all'}, as I don't think there is any other value allowed. That applies to both docstrings.

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 +2765,56 @@ 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.


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

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

>>> 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

>>> s.nlargest(3, keep='last')
France 65000000
Italy 59000000
Brunei 434000
dtype: int64

>>> 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 @@ -2789,16 +2825,19 @@ def nsmallest(self, n=5, keep='first'):
Parameters
----------
n : int
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can you add the default here?

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.
Return this many ascending sorted values.
keep : str, 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 +2846,55 @@ def nsmallest(self, n=5, keep='first'):

See Also
--------
Series.nlargest
Series.nlargest: Get the `n` largest elements.

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

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

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

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

>>> 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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