在Python中，如何计算列表中的出现次数？

我是python新手，我想计算每个单词在所有文件中出现的次数。显示每个单词、出现的次数和出现的时间百分比。对列表进行排序，使最频繁的单词最先出现，最不频繁的单词最后出现。 我正在做一个小样本，我只知道一个文件，但我无法正常工作

 from collections import defaultdict

words = "apple banana apple strawberry banana lemon"

d = defaultdict(int)
for word in words.split():
    d[word] += 1

---

使用您的代码，这里有一种更简洁的方法：

# Initializing Dictionary
d = {}
with open(sys.argv[1], 'r') as f:

    # counting number of times each word comes up in list of words (in dictionary)
    for line in f: 
        words = line.lower().split() 
        # Iterate over each word in line 
        for word in words: 
            if word not in d.keys():
                d[word] = 1
            else:
                d[word]+=1

n_all_words = sum([k.values])

# Print percentage occurance
for k, v in d.items():
    print(f'{k} occurs {v} times and is {(100*v/n_all_words):,.2f}% total of words.')


# Sort a dictionary using this useful solution
# https://stackoverflow.com/a/613218/10521959
import operator
sorted_d = sorted(d.items(), key=operator.itemgetter(1))

---

正如评论中提到的，这正是集合。计数器

从文档：

---

最简单的方法就是使用计数器功能：

from collections import Counter
c = Counter(words.split())

输出：

Counter({'apple': 2, 'banana': 2, 'strawberry': 1, 'lemon': 1})

要使单词排列整齐，或计数：

list(c.keys())
list(c.values())

或者把它放到一个普通的字典里：

dict(c.items())

或元组列表：

c.most_common()

如上所述，collections模块中的Counter类绝对是计数应用程序的首选

此解决方案还解决了使用fileinput.input方法对多个文件中的字进行计数的请求，以迭代命令行上指定的所有文件名的内容，或者如果命令行上未指定任何文件名，则将从STDIN（通常是键盘）读取

最后，它使用了一种更为复杂的方法，用正则表达式作为分隔符，将行拆分为“单词”。正如代码中所指出的，它将更优雅地处理缩略语，但是它会被撇号和单引号混淆

"""countwords.py
   count all words across all files
"""

import fileinput
import re
import collections

# create a regex delimiter that is any character that is  not 1 or
# more word character or an apostrophe, this allows contractions
# to be treated as a word (eg can't  won't  didn't )
# Caution: this WILL get confused by a line that uses apostrophe
# as a single quote: eg 'hello' would be treated as a 7 letter word

word_delimiter = re.compile(r"[^\w']+")

# create an empty Counter

counter = collections.Counter()

# use fileinput.input() to open and read ALL lines from ALL files
# specified on the command line, or if no files specified on the
# command line then read from STDIN (ie the keyboard or redirect)

for line in fileinput.input():
    for word in word_delimiter.split(line):
        counter[word.lower()] += 1   # count case insensitively

del counter['']   # handle corner case of the occasional 'empty' word

# compute the total number of words using .values() to get an
# generator of all the Counter values (ie the individual word counts)        
# then pass that generator to the sum function which is able to 
# work with a list or a generator

total = sum(counter.values())

# iterate through the key/value pairs (ie word/word_count) in sorted
# order - the lambda function says sort based on position 1 of each
# word/word_count tuple (ie the word_count) and reverse=True does
# exactly what it says = reverse the normal order so it now goes
# from highest word_count to lowest word_count

print("{:>10s}  {:>8s} {:s}".format("occurs", "percent", "word"))

for word, count in sorted(counter.items(),
                          key=lambda t: t[1],
                          reverse=True):
    print ("{:10d} {:8.2f}% {:s}".format(count, count/total*100, word))

示例输出：

$ python3 countwords.py
I have a dog, he is a good dog, but he can't fly
^D

occurs   percent word
     2    15.38% a
     2    15.38% dog
     2    15.38% he
     1     7.69% i
     1     7.69% have
     1     7.69% is
     1     7.69% good
     1     7.69% but
     1     7.69% can't
     1     7.69% fly

以及：

---

看看什么不起作用？从诊断的角度来说，我不能正常工作是没有帮助的。如果引发异常，能否提供示例输入、预期输出和实际输出，包括回溯？这些是一个项目的基础。正如@RaySteam所说，collections.Counter是在实际代码中如何实现的，但是对于学习练习/家庭作业，您可能希望自己实现它。您使用的是什么版本的python@nubbyFor reference.strip在调用.split（不带参数）之前是不必要的；而sumd.values在没有列表理解的情况下工作得很好。python 3但是我修复了print语句，但仍然不工作，我不理解这一行，n_all_words=sum[v代表k.values中的v]这里的缩进正确吗？打开后没有任何缩进。。。行，我来修一下@kaya3

"""countwords.py
   count all words across all files
"""

import fileinput
import re
import collections

# create a regex delimiter that is any character that is  not 1 or
# more word character or an apostrophe, this allows contractions
# to be treated as a word (eg can't  won't  didn't )
# Caution: this WILL get confused by a line that uses apostrophe
# as a single quote: eg 'hello' would be treated as a 7 letter word

word_delimiter = re.compile(r"[^\w']+")

# create an empty Counter

counter = collections.Counter()

# use fileinput.input() to open and read ALL lines from ALL files
# specified on the command line, or if no files specified on the
# command line then read from STDIN (ie the keyboard or redirect)

for line in fileinput.input():
    for word in word_delimiter.split(line):
        counter[word.lower()] += 1   # count case insensitively

del counter['']   # handle corner case of the occasional 'empty' word

# compute the total number of words using .values() to get an
# generator of all the Counter values (ie the individual word counts)        
# then pass that generator to the sum function which is able to 
# work with a list or a generator

total = sum(counter.values())

# iterate through the key/value pairs (ie word/word_count) in sorted
# order - the lambda function says sort based on position 1 of each
# word/word_count tuple (ie the word_count) and reverse=True does
# exactly what it says = reverse the normal order so it now goes
# from highest word_count to lowest word_count

print("{:>10s}  {:>8s} {:s}".format("occurs", "percent", "word"))

for word, count in sorted(counter.items(),
                          key=lambda t: t[1],
                          reverse=True):
    print ("{:10d} {:8.2f}% {:s}".format(count, count/total*100, word))

$ python3 countwords.py
I have a dog, he is a good dog, but he can't fly
^D

occurs   percent word
     2    15.38% a
     2    15.38% dog
     2    15.38% he
     1     7.69% i
     1     7.69% have
     1     7.69% is
     1     7.69% good
     1     7.69% but
     1     7.69% can't
     1     7.69% fly

$ python3 countwords.py text1 text2
    occurs   percent word
         2    11.11% hello
         2    11.11% i
         1     5.56% there
         1     5.56% how
         1     5.56% are
         1     5.56% you
         1     5.56% am
         1     5.56% fine
         1     5.56% mark
         1     5.56% where
         1     5.56% is
         1     5.56% the
         1     5.56% dog
         1     5.56% haven't
         1     5.56% seen
         1     5.56% him