Python大型多列表高效查询

Question

我正在尝试创建如何使用Python操作由CSV表组成的海量数据库的示例.

我想找到一种方法来模拟在一些表中传播的高效索引查询 list()

以下示例在3.2Ghz Core i5中需要24秒

#!/usr/bin/env python
import csv
MAINDIR = "../"
pf = open (MAINDIR+"atp_players.csv")
players = [p for p in csv.reader(pf)]
rf = open (MAINDIR+"atp_rankings_current.csv")
rankings = [r for r in csv.reader(rf)]
for i in rankings[:10]:
    player = filter(lambda x: x[0]==i[2],players)[0]
    print "%s(%s),(%s) Points: %s"%(player[2],player[5],player[3],i[3])

对于此数据集.

将非常感谢更有效或更pythonic的方式.

Answer 1

您可以itertools.islice代替读取所有行并使用itertools.ifilter:

import csv
from itertools import islice,ifilter

MAINDIR = "../"
with  open(MAINDIR + "atp_players.csv") as pf,  open(MAINDIR + "atp_rankings_current.csv") as rf:
    players = list(csv.reader(pf))
    rankings = csv.reader(rf)
    # only get first ten rows using islice
    for i in islice(rankings, None, 10):
        # ifilter won't create a list, gives values in the fly
        player = next(ifilter(lambda x: x[0] == i[2], players),"")

不太清楚filter(lambda x: x[0]==i[2],players)[0]正在做什么,你似乎每次都在搜索整个玩家列表,只保留第一个元素.使用第一个元素作为键对列表进行排序可能需要付费,并使用二分搜索或构建一个dict,第一个元素作为键,行作为值然后只进行查找.

import csv
from itertools import islice,ifilter
from collections import OrderedDict

MAINDIR = "../"
with  open(MAINDIR + "atp_players.csv") as pf,  open(MAINDIR + "atp_rankings_current.csv") as rf:
    players = OrderedDict((row[0],row) for row in csv.reader(pf))
    rankings = csv.reader(rf)
    for i in islice(rankings, None, 10):
        # now constant work getting row as opposed to 0(n)    
        player = players.get(i[2])

你使用什么默认值,或者如果需要,你必须决定.

如果在每行的开头有重复元素但只想返回第一个匹配项:

with  open(MAINDIR + "atp_players.csv") as pf, open(MAINDIR + "atp_rankings_current.csv") as rf:
    players = {}
    for row in csv.reader(pf):
        key = row[0]
        if key in players:
            continue
        players[key] = row
    rankings = csv.reader(rf)
    for i in islice(rankings, None, 10):
        player = players.get(i[2])

输出:

Djokovic(SRB),(R) Points: 11360
Federer(SUI),(R) Points: 9625
Nadal(ESP),(L) Points: 6585
Wawrinka(SUI),(R) Points: 5120
Nishikori(JPN),(R) Points: 5025
Murray(GBR),(R) Points: 4675
Berdych(CZE),(R) Points: 4600
Raonic(CAN),(R) Points: 4440
Cilic(CRO),(R) Points: 4150
Ferrer(ESP),(R) Points: 4045

十个玩家的代码时间显示ifilter是最快的,但是当我们提高排名时,我们会看到dict获胜,以及你的代码有多么糟糕:

In [33]: %%timeit
MAINDIR = "tennis_atp-master/"
pf = open ("/tennis_atp-master/atp_players.csv")                                                          players = [p for p in csv.reader(pf)]
rf =open( "/tennis_atp-master/atp_rankings_current.csv")
rankings = [r for r in csv.reader(rf)]
for i in rankings[:10]:
    player = filter(lambda x: x[0]==i[2],players)[0]
   ....: 
10 loops, best of 3: 123 ms per loop

In [34]: %%timeit
with  open("/tennis_atp-master/atp_players.csv") as pf, open( "/tennis_atp-master/atp_rankings_current.csv") as rf:                     players = list(csv.reader(pf))
    rankings = csv.reader(rf)    # only get first ten rows using islice
    for i in islice(rankings, None, 10):
        # ifilter won't create a list, gives values in the fly
        player = next(ifilter(lambda x: x[0] == i[2], players),"")
   ....: 
10 loops, best of 3: 43.6 ms per loop

In [35]: %%timeit                           
with  open("/tennis_atp-master/atp_players.csv") as pf, open( "/tennis_atp-master/atp_rankings_current.csv") as rf:
    players = {}
    for row in csv.reader(pf):
        key = row[0]
        if key in players:
            continue
        players[row[0]] = row
    rankings = csv.reader(rf)
    for i in islice(rankings, None, 10):
        player = players.get(i[2])
        pass
   ....: 
10 loops, best of 3: 50.7 ms per loop

现在有100名玩家,你会看到dict和10的速度一样快.构建dict的成本已经被恒定的时间查找所抵消:

In [38]: %%timeit
with  open("/tennis_atp-master/atp_players.csv") as pf, open("/tennis_atp-master/atp_rankings_current.csv") as rf:
    players = list(csv.reader(pf))
    rankings = csv.reader(rf)
    # only get first ten rows using islice
    for i in islice(rankings, None, 100):
        # ifilter won't create a list, gives values in the fly
        player = next(ifilter(lambda x: x[0] == i[2], players),"")
   ....: 
10 loops, best of 3: 120 ms per loop

In [39]: %%timeit
with  open("/tennis_atp-master/atp_players.csv") as pf, open( "/tennis_atp-master/atp_rankings_current.csv") as rf:
    players = {}                  
    for row in csv.reader(pf):
        key = row[0]
        if key in players:
            continue                                          
        players[row[0]] = row                                     
    rankings = csv.reader(rf)
    for i in islice(rankings, None, 100):
        player = players.get(i[2])
        pass
   ....: 
10 loops, best of 3: 50.7 ms per loop

In [40]: %%timeit
MAINDIR = "tennis_atp-master/"
pf = open ("/tennis_atp-master/atp_players.csv")
players = [p for p in csv.reader(pf)]
rf =open( "/tennis_atp-master/atp_rankings_current.csv")
rankings = [r for r in csv.reader(rf)]
for i in rankings[:100]:
    player = filter(lambda x: x[0]==i[2],players)[0]
   ....: 
1 loops, best of 3: 806 ms per loop

250名球员:

# your code
1 loops, best of 3: 1.86 s per loop

# dict
10 loops, best of 3: 50.7 ms per loop

# ifilter
10 loops, best of 3: 483  ms per loop

最终测试循环整个排名:

# your code

1 loops, best of 3: 2min 40s per loop

# dict 
10 loops, best of 3: 67 ms per loop

# ifilter
1 loops, best of 3: 1min 3s per loop

所以你可以看到,当我们循环更多的排名时,dict选项是迄今为止最有效的运行时,并且将非常好地扩展.