Question

我正在解析一长串文本并计算Python中每个单词出现的次数。我有一个功能，但我正在寻找建议是否有方法可以使它更有效（在速度方面）以及是否有甚至python库函数可以为我这样做所以我不是重新发明轮子？

您能否建议一种更有效的方法来计算长字符串中最常见的单词（通常在字符串中超过1000个单词）？

还有什么最好的方法将字典排序到第1个元素是最常用字的列表中，第2个元素是第2个最常用的字等等？

test = """abc def-ghi jkl abc
abc"""

def calculate_word_frequency(s):
    # Post: return a list of words ordered from the most
    # frequent to the least frequent

    words = s.split()
    freq  = {}
    for word in words:
        if freq.has_key(word):
            freq[word] += 1
        else:
            freq[word] = 1
    return sort(freq)

def sort(d):
    # Post: sort dictionary d into list of words ordered
    # from highest freq to lowest freq
    # eg: For {"the": 3, "a": 9, "abc": 2} should be
    # sorted into the following list ["a","the","abc"]

    #I have never used lambda's so I'm not sure this is correct
    return d.sort(cmp = lambda x,y: cmp(d[x],d[y]))

print calculate_word_frequency(test)

Answer 1

使用collections.Counter：

>>> from collections import Counter
>>> test = 'abc def abc def zzz zzz'
>>> Counter(test.split()).most_common()
[('abc', 2), ('zzz', 2), ('def', 2)]

Answer 2

>>>> test = """abc def-ghi jkl abc
abc"""
>>> from collections import Counter
>>> words = Counter()
>>> words.update(test.split()) # Update counter with words
>>> words.most_common()        # Print list with most common to least common
[('abc', 3), ('jkl', 1), ('def-ghi', 1)]

Answer 3

您还可以使用NLTK（自然语言工具包）。它提供了非常好的库来研究处理文本。对于此示例，您可以使用：

from nltk import FreqDist

text = "aa bb cc aa bb"
fdist1 = FreqDist(text)

# show most 10 frequent word in the text
print fdist1.most_common(10)

结果将是：

[('aa', 2), ('bb', 2), ('cc', 1)]

Answer 4

如果你想显示常用词和计数值而不是List，那么这是我的代码。

from collections import Counter

str = 'abc def ghi def abc abc'

arr = Counter(str.split()).most_common()

for word, count in arr:
    print(word, count)

输出：

abc 3
def 2
ghi 1

有效地计算字符串中的字频

4 个答案: