Add key= argument to heapq.nsmallest() and heapq.nlargest().

This commit is contained in:
Raymond Hettinger 2004-12-02 08:59:14 +00:00
parent de7b99045d
commit 4901a1f267
4 changed files with 90 additions and 8 deletions

View File

@ -90,16 +90,24 @@ True
The module also offers two general purpose functions based on heaps.
\begin{funcdesc}{nlargest}{n, iterable}
\begin{funcdesc}{nlargest}{n, iterable\optional{, key}}
Return a list with the \var{n} largest elements from the dataset defined
by \var{iterable}. Equivalent to: \code{sorted(iterable, reverse=True)[:n]}
\versionadded{2.4}
by \var{iterable}. \var{key}, if provided, specifies a function of one
argument that is used to extract a comparison key from each element
in the iterable: \samp{\var{key}=\function{str.lower}}
Equivalent to: \samp{sorted(iterable, key=key, reverse=True)[:n]}
\versionadded{2.4}
\versionchanged[Added the optional \var{key} argument]{2.5}
\end{funcdesc}
\begin{funcdesc}{nsmallest}{n, iterable}
\begin{funcdesc}{nsmallest}{n, iterable\optional{, key}}
Return a list with the \var{n} smallest elements from the dataset defined
by \var{iterable}. Equivalent to: \code{sorted(iterable)[:n]}
\versionadded{2.4}
by \var{iterable}. \var{key}, if provided, specifies a function of one
argument that is used to extract a comparison key from each element
in the iterable: \samp{\var{key}=\function{str.lower}}
Equivalent to: \samp{sorted(iterable, key=key)[:n]}
\versionadded{2.4}
\versionchanged[Added the optional \var{key} argument]{2.5}
\end{funcdesc}
Both functions perform best for smaller values of \var{n}. For larger

View File

@ -129,7 +129,8 @@ From all times, sorting has always been a Great Art! :-)
__all__ = ['heappush', 'heappop', 'heapify', 'heapreplace', 'nlargest',
'nsmallest']
from itertools import islice, repeat
from itertools import islice, repeat, count, imap, izip, tee
from operator import itemgetter
import bisect
def heappush(heap, item):
@ -307,6 +308,33 @@ try:
except ImportError:
pass
# Extend the implementations of nsmallest and nlargest to use a key= argument
_nsmallest = nsmallest
def nsmallest(n, iterable, key=None):
"""Find the n smallest elements in a dataset.
Equivalent to: sorted(iterable, key=key)[:n]
"""
if key is None:
return _nsmallest(n, iterable)
in1, in2 = tee(iterable)
it = izip(imap(key, in1), count(), in2) # decorate
result = _nsmallest(n, it)
return map(itemgetter(2), result) # undecorate
_nlargest = nlargest
def nlargest(n, iterable, key=None):
"""Find the n largest elements in a dataset.
Equivalent to: sorted(iterable, key=key, reverse=True)[:n]
"""
if key is None:
return _nlargest(n, iterable)
in1, in2 = tee(iterable)
it = izip(imap(key, in1), count(), in2) # decorate
result = _nlargest(n, it)
return map(itemgetter(2), result) # undecorate
if __name__ == "__main__":
# Simple sanity test
heap = []

View File

@ -105,13 +105,19 @@ class TestHeap(unittest.TestCase):
def test_nsmallest(self):
data = [random.randrange(2000) for i in range(1000)]
f = lambda x: x * 547 % 2000
for n in (0, 1, 2, 10, 100, 400, 999, 1000, 1100):
self.assertEqual(nsmallest(n, data), sorted(data)[:n])
self.assertEqual(nsmallest(n, data, key=f),
sorted(data, key=f)[:n])
def test_largest(self):
def test_nlargest(self):
data = [random.randrange(2000) for i in range(1000)]
f = lambda x: x * 547 % 2000
for n in (0, 1, 2, 10, 100, 400, 999, 1000, 1100):
self.assertEqual(nlargest(n, data), sorted(data, reverse=True)[:n])
self.assertEqual(nlargest(n, data, key=f),
sorted(data, key=f, reverse=True)[:n])
#==============================================================================

View File

@ -4,6 +4,46 @@ Python News
(editors: check NEWS.help for information about editing NEWS using ReST.)
What's New in Python 2.5 alpha 1?
=================================
Core and builtins
-----------------
Extension Modules
-----------------
Library
-------
- heapq.nsmallest() and heapq.nlargest() now support key= arguments with
the same meaning as for list.sort().
Build
-----
C API
-----
Tests
-----
Mac
---
Tools/Demos
-----------
What's New in Python 2.4 final?
===============================