我正在尝试应用double for循环来解决问题。理想情况下，我不希望使用for循环，因为我拥有的数据集非常庞大，并且需要花费很多时间才能遍历整个循环。下面是代码：
words_data_set = pandas.DataFrame({'keywords':['wlmart womens book set','microsoft fish sauce','books from walmat store','mens login for facebook fools','mens login for facbook fools','login for twetter boy','apples from cook']})
company_name_list = ['walmart','microsoft','facebook','twitter','amazon','apple']
import pandas
from fuzzywuzzy import fuzz
from fuzzywuzzy import process
import time
print(len(words_data_set),'....rows')
start_time = time.time()
fuzzed_data_final = pandas.DataFrame()
for s in words_data_set.keywords.tolist():
step1 = words_data_set[words_data_set.keywords == s]
step1['keywords2'] = step1.keywords.str.split()
step2 = step1.keywords2.values.tolist()
step3 = [item for sublist in step2 for item in sublist]
step3 = pandas.DataFrame(step3)
step3.columns = ['search_words']
step3['keywords'] = s
fuzzed_data = pandas.DataFrame()
for w in step3.search_words.tolist():
step4 = step3[step3.search_words == w]
step5 = pandas.DataFrame(process.extract(w,company_name_list))
step5.columns = ['w','score']
if step5.score.max() >= 90:
w = ''
else:
w
step4['search_words'] = w
fuzzed_data = fuzzed_data.append(step4)
fuzzed_data_final = fuzzed_data_final.append(fuzzed_data)
print("--- %s seconds ---" % (time.time() - start_time))
我该如何针对速度和效率进行优化。 实际上，words_data_set大约为一百万行。 实际上，company_name_list大约有2,000个元素。
答案 0 :(得分：1)
当您仅可以使用Python内置函数时，请尝试不要使用pandas创建新的临时对象。我不知道您要解决的问题，但是如果我只是清理一下我认为冗余的代码，则代码运行速度将提高9倍（0.045对0.410秒）：
import pandas
from fuzzywuzzy import process
from operator import itemgetter
import time
words_data_set = pandas.DataFrame({
'keywords': ['wlmart womens book set',
'microsoft fish sauce',
'books from walmat store',
'mens login for facebook fools',
'mens login for facbook fools',
'login for twetter boy',
'apples from cook']})
company_name_list = [
'walmart', 'microsoft', 'facebook', 'twitter', 'amazon', 'apple']
print(len(words_data_set), '....rows')
start_time = time.time()
fuzzed_data_final = pandas.DataFrame()
for s in words_data_set.keywords.tolist():
step3 = pandas.DataFrame(s.split())
step3.columns = ['search_words']
step3['keywords'] = s
fuzzed_data = pandas.DataFrame()
for w in step3.search_words.tolist():
step4 = step3[step3.search_words == w]
if max(process.extract(w, company_name_list), key=itemgetter(1))[1] >= 90:
w = ''
default = pandas.options.mode.chained_assignment
pandas.options.mode.chained_assignment = None
step4['search_words'] = w
pandas.options.mode.chained_assignment = default
fuzzed_data = fuzzed_data.append(step4)
fuzzed_data_final = fuzzed_data_final.append(fuzzed_data)
print("--- %s seconds ---" % (time.time() - start_time))
print(fuzzed_data_final)
现在输出：
7 ....rows
--- 0.04493832588195801 seconds ---
search_words keywords
0 wlmart womens book set
1 womens wlmart womens book set
2 wlmart womens book set
3 set wlmart womens book set
0 microsoft fish sauce
1 fish microsoft fish sauce
2 sauce microsoft fish sauce
0 books books from walmat store
1 from books from walmat store
2 books from walmat store
3 store books from walmat store
0 mens mens login for facebook fools
1 login mens login for facebook fools
2 for mens login for facebook fools
3 mens login for facebook fools
4 fools mens login for facebook fools
0 mens mens login for facbook fools
1 login mens login for facbook fools
2 for mens login for facbook fools
3 mens login for facbook fools
4 fools mens login for facbook fools
0 login login for twetter boy
1 for login for twetter boy
2 twetter login for twetter boy
3 boy login for twetter boy
0 apples from cook
1 from apples from cook
2 cook apples from cook
Process finished with exit code 0
之前的输出：
7 ....rows
/Users/alex/PycharmProjects/game/pandas_double_for_loop_original.py:18: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
step1['keywords2'] = step1.keywords.str.split()
/Users/alex/PycharmProjects/game/pandas_double_for_loop_original.py:36: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
step4['search_words'] = w
--- 0.4108889102935791 seconds ---
search_words keywords
0 wlmart womens book set
1 womens wlmart womens book set
2 wlmart womens book set
3 set wlmart womens book set
0 microsoft fish sauce
1 fish microsoft fish sauce
2 sauce microsoft fish sauce
0 books books from walmat store
1 from books from walmat store
2 books from walmat store
3 store books from walmat store
0 mens mens login for facebook fools
1 login mens login for facebook fools
2 for mens login for facebook fools
3 mens login for facebook fools
4 fools mens login for facebook fools
0 mens mens login for facbook fools
1 login mens login for facbook fools
2 for mens login for facbook fools
3 mens login for facbook fools
4 fools mens login for facbook fools
0 login login for twetter boy
1 for login for twetter boy
2 twetter login for twetter boy
3 boy login for twetter boy
0 apples from cook
1 from apples from cook
2 cook apples from cook
Process finished with exit code 0
更新：关于双循环效率的答案。这是版本2程序：
import pandas
from fuzzywuzzy import process
import time
lines = [
'wlmart womens book set', 'microsoft fish sauce',
'books from walmat store', 'mens login for facebook fools',
'mens login for facbook fools', 'login for twetter boy',
'apples from cook'
]
companies = ['walmart', 'microsoft', 'facebook', 'twitter', 'amazon', 'apple']
fuzzed_data_final = pandas.DataFrame()
lines_results = []
def part0():
counter = 0
for line in lines:
for word in line.split():
counter += 1
print('Part 0. Count all words.\n', counter, 'words')
def part1():
for line in lines:
line_results = []
for word in line.split():
match_score_list = process.extractBests(
word, companies, score_cutoff=90, limit=1)
line_results.append(True if match_score_list else False)
lines_results.append(line_results)
print('Part 1. Match all words.\n', lines_results)
def part2():
global fuzzed_data_final
for i, line in enumerate(lines):
step3 = pandas.DataFrame(line.split())
step3.columns = ['search_words']
step3['keywords'] = line
fuzzed_data = pandas.DataFrame()
for j, word in enumerate(line.split()):
step4 = step3[step3.search_words == word]
w = word
if lines_results[i][j]:
w = ''
default = pandas.options.mode.chained_assignment
pandas.options.mode.chained_assignment = None
step4['search_words'] = w
pandas.options.mode.chained_assignment = default
fuzzed_data = fuzzed_data.append(step4)
fuzzed_data_final = fuzzed_data_final.append(fuzzed_data)
print('Part 2. Create pandas.DataFrame fuzzed_data_final.\n',
fuzzed_data_final)
def execute(f):
start_time = time.perf_counter()
f()
total_time = time.perf_counter() - start_time
print("--- %f seconds ---" % total_time)
rows = 1
names = 2000
e = total_time / len(lines) / len(companies) * rows * 1000000. * names
h = e / 3600
d = h / 24
print('Time estimation for %d million rows and %d company names: %d seconds or'
' %d hours or %d days'
% (rows, names, e, h, d))
execute(part0)
execute(part1)
execute(part2)
输出：
Part 0. Count all words.
28 words
--- 0.000032 seconds ---
Time estimation for 1 million rows and 2000 company names: 1534 seconds or 0 hours or 0 days
Part 1. Match all words.
[[True, False, True, False], [True, False, False], [False, False, True, False], [False, False, False, True, False], [False, False, False, True, False], [False, False, False, False], [True, False, False]]
--- 0.006723 seconds ---
Time estimation for 1 million rows and 2000 company names: 320165 seconds or 88 hours or 3 days
Part 2. Create pandas.DataFrame fuzzed_data_final.
search_words keywords
0 wlmart womens book set
1 womens wlmart womens book set
2 wlmart womens book set
3 set wlmart womens book set
0 microsoft fish sauce
1 fish microsoft fish sauce
2 sauce microsoft fish sauce
0 books books from walmat store
1 from books from walmat store
2 books from walmat store
3 store books from walmat store
0 mens mens login for facebook fools
1 login mens login for facebook fools
2 for mens login for facebook fools
3 mens login for facebook fools
4 fools mens login for facebook fools
0 mens mens login for facbook fools
1 login mens login for facbook fools
2 for mens login for facbook fools
3 mens login for facbook fools
4 fools mens login for facbook fools
0 login login for twetter boy
1 for login for twetter boy
2 twetter login for twetter boy
3 boy login for twetter boy
0 apples from cook
1 from apples from cook
2 cook apples from cook
--- 0.042164 seconds ---
Time estimation for 1 million rows and 2000 company names: 2007804 seconds or 557 hours or 23 days
Process finished with exit code 0
因此，仅读取100万行并计数所有单词将花费大约半小时。 88个小时对所有单词进行模糊匹配，而23天的创建时间fuzzed_data_final大约有4,000,0000行。我会看看这是否可以优化。
更新2：优化了创建过程fuzzed_data_final
import pandas
from fuzzywuzzy import process
import time
lines = [
'wlmart womens book set', 'microsoft fish sauce',
'books from walmat store', 'mens login for facebook fools',
'mens login for facbook fools', 'login for twetter boy',
'apples from cook'
]
companies = ['walmart', 'microsoft', 'facebook', 'twitter', 'amazon', 'apple']
start_time = time.perf_counter()
keywords = []
search_words = []
for line in lines:
line_results = []
for word in line.split():
match_score_list = process.extractBests(
word, companies, score_cutoff=90, limit=1)
keywords.append(line)
search_words.append('' if match_score_list else word)
fuzzed_data_final = pandas.DataFrame(
{ 'search_words': pandas.Series(search_words),
'keywords': pandas.Series(keywords) })
total_time = time.perf_counter() - start_time
print("--- %f seconds ---" % total_time)
rows = 1
names = 2000
e = total_time / len(lines) / len(companies) * rows * 1000000. * names
h = e / 3600
d = h / 24
print('Time estimation for %d million rows and %d company names: %d seconds or'
' %d hours or %d days'
% (rows, names, e, h, d))
print(fuzzed_data_final)
输出：
/usr/local/bin/python3.7 /Users/alex/PycharmProjects/game/pandas_doble_for_loop_v3.py
--- 0.008402 seconds ---
Time estimation for 1 million rows and 2000 company names: 400107 seconds or 111 hours or 4 days
search_words keywords
0 wlmart womens book set
1 womens wlmart womens book set
2 wlmart womens book set
3 set wlmart womens book set
4 microsoft fish sauce
5 fish microsoft fish sauce
6 sauce microsoft fish sauce
7 books books from walmat store
8 from books from walmat store
9 books from walmat store
10 store books from walmat store
11 mens mens login for facebook fools
12 login mens login for facebook fools
13 for mens login for facebook fools
14 mens login for facebook fools
15 fools mens login for facebook fools
16 mens mens login for facbook fools
17 login mens login for facbook fools
18 for mens login for facbook fools
19 mens login for facbook fools
20 fools mens login for facbook fools
21 login login for twetter boy
22 for login for twetter boy
23 twetter login for twetter boy
24 boy login for twetter boy
25 apples from cook
26 from apples from cook
27 cook apples from cook
Process finished with exit code 0
比原始版本快47倍。我看到了另一种提高1,000,000行文本性能的技巧：为匹配的单词使用字典。好的词汇量约为20,000个单词。每行大约有10个字。因此，每个单词平均10,000,000 / 20,000 = 500次重复。
更新＃3：为匹配的单词添加了字典
import pandas
from fuzzywuzzy import process
import time
lines = [
'wlmart womens book set', 'microsoft fish sauce',
'books from walmat store', 'mens login for facebook fools',
'mens login for facbook fools', 'login for twetter boy',
'apples from cook'
]
companies = ['walmart', 'microsoft', 'facebook', 'twitter', 'amazon', 'apple']
start_time = time.perf_counter()
keywords = []
search_words = []
dictionary = {}
for line in lines:
for word in line.split():
if word in dictionary:
score = dictionary[word]
else:
match_score_list = process.extractBests(
word, companies, score_cutoff=90, limit=1)
score = True if match_score_list else False
dictionary[word] = True if match_score_list else False
keywords.append(line)
search_words.append('' if score else word)
fuzzed_data_final = pandas.DataFrame(
{'search_words': pandas.Series(search_words),
'keywords': pandas.Series(keywords)})
total_time = time.perf_counter() - start_time
print("--- %f seconds ---" % total_time)
rows = 1
names = 2000
e = total_time / len(lines) / len(companies) * rows * 1000000. * names
h = e / 3600
d = h / 24
print('Time estimation for %d million rows and %d company names: %d seconds or'
' %d hours or %d days' % (rows, names, e, h, d))
print(fuzzed_data_final)
输出：
/usr/local/bin/python3.7 /Users/alex/PycharmProjects/game/pandas_doble_for_loop_v4.py
--- 0.005707 seconds ---
Time estimation for 1 million rows and 2000 company names: 271761 seconds or 75 hours or 3 days
search_words keywords
0 wlmart womens book set
1 womens wlmart womens book set
2 wlmart womens book set
3 set wlmart womens book set
4 microsoft fish sauce
5 fish microsoft fish sauce
6 sauce microsoft fish sauce
7 books books from walmat store
8 from books from walmat store
9 books from walmat store
10 store books from walmat store
11 mens mens login for facebook fools
12 login mens login for facebook fools
13 for mens login for facebook fools
14 mens login for facebook fools
15 fools mens login for facebook fools
16 mens mens login for facbook fools
17 login mens login for facbook fools
18 for mens login for facbook fools
19 mens login for facbook fools
20 fools mens login for facbook fools
21 login login for twetter boy
22 for login for twetter boy
23 twetter login for twetter boy
24 boy login for twetter boy
25 apples from cook
26 from apples from cook
27 cook apples from cook
Process finished with exit code 0
它比原始脚本快69倍。我们能做到100吗？