开发环境

Python版本： python3.6
调试工具：pycharm 2017.1.3
电脑系统：Windows 10 64位系统

统计列表中的数字频度

生成数据

from random import randint
data = [randint(-10, 10) for _ in range(30)]
print(data)

使用字典统计数据

使用迭代方法

from random import randint
data = [randint(-10, 10) for _ in range(30)]
print(data)
c = dict.fromkeys(data, 0)
print(c)
for x in data:c[x] += 1print(c)

[-7, -2, 8, 6, 3, -4, 10, -1, 2, -9, 5, 4, 8, -2, 3, 3, -10, -4, 2, 6, -9, -1, -3, 9, 6, -9, 3, -2, -7, -3]
{-7: 0, -2: 0, 8: 0, 6: 0, 3: 0, -4: 0, 10: 0, -1: 0, 2: 0, -9: 0, 5: 0, 4: 0, -10: 0, -3: 0, 9: 0}
{-7: 2, -2: 3, 8: 2, 6: 3, 3: 4, -4: 2, 10: 1, -1: 2, 2: 2, -9: 3, 5: 1, 4: 1, -10: 1, -3: 2, 9: 1}

使用标准库collections下的Counter函数

from random import randint
from collections import Counter
data = [randint(-10, 10) for _ in range(30)]
print(data)
c1 = Counter(data)
print(c1)

[4, 2, 4, -2, 7, 8, 2, -9, 3, 2, -8, 3, -9, -4, 10, 2, -1, 6, 9, 9, -10, 8, 8, -1, 10, -1, 0, -7, -10, 3]
Counter({2: 4, 8: 3, 3: 3, -1: 3, 4: 2, -9: 2, 10: 2, 9: 2, -10: 2, -2: 1, 7: 1, -8: 1, -4: 1, 6: 1, 0: 1, -7: 1})

找到出现频度最高的三个元素

from random import randint
from collections import Counter
data = [randint(-10, 10) for _ in range(30)]
print(data)
c1 = Counter(data)
print(c1)
res = c1.most_common(3)
print(res)

[9, -9, 4, -4, 0, -4, 7, 1, -7, 8, 10, -1, 1, 3, 6, 1, -9, -6, 7, -7, -3, 7, 7, 10, -8, -4, 6, 0, 6, -1]
Counter({7: 4, -4: 3, 1: 3, 6: 3, -9: 2, 0: 2, -7: 2, 10: 2, -1: 2, 9: 1, 4: 1, 8: 1, 3: 1, -6: 1, -3: 1, -8: 1})
[(7, 4), (-4, 3), (1, 3)]

统计英文文章词频统计

数据来源

import this

就会得到Python之禅

使用re.split()进行单词分割

import re
data = """The Zen of Python, by Tim PetersBeautiful is better than ugly.Explicit is better than implicit.Simple is better than complex.Complex is better than complicated.Flat is better than nested.Sparse is better than dense.Readability counts.Special cases aren't special enough to break the rules.Although practicality beats purity.Errors should never pass silently.Unless explicitly silenced.In the face of ambiguity, refuse the temptation to guess.There should be one-- and preferably only one --obvious way to do it.Although that way may not be obvious at first unless you're Dutch.Now is better than never.Although never is often better than *right* now.If the implementation is hard to explain, it's a bad idea.If the implementation is easy to explain, it may be a good idea.Namespaces are one honking great idea -- let's do more of those!"""
words = re.split('\W+', data)
print(words)

['', 'The', 'Zen', 'of', 'Python', 'by', 'Tim', 'Peters', 'Beautiful', 'is', 'better', 'than', 'ugly', 'Explicit', 'is', 'better', 'than', 'implicit', 'Simple', 'is', 'better', 'than', 'complex', 'Complex', 'is', 'better', 'than', 'complicated', 'Flat', 'is', 'better', 'than', 'nested', 'Sparse', 'is', 'better', 'than', 'dense', 'Readability', 'counts', 'Special', 'cases', 'aren', 't', 'special', 'enough', 'to', 'break', 'the', 'rules', 'Although', 'practicality', 'beats', 'purity', 'Errors', 'should', 'never', 'pass', 'silently', 'Unless', 'explicitly', 'silenced', 'In', 'the', 'face', 'of', 'ambiguity', 'refuse', 'the', 'temptation', 'to', 'guess', 'There', 'should', 'be', 'one', 'and', 'preferably', 'only', 'one', 'obvious', 'way', 'to', 'do', 'it', 'Although', 'that', 'way', 'may', 'not', 'be', 'obvious', 'at', 'first', 'unless', 'you', 're', 'Dutch', 'Now', 'is', 'better', 'than', 'never', 'Although', 'never', 'is', 'often', 'better', 'than', 'right', 'now', 'If', 'the', 'implementation', 'is', 'hard', 'to', 'explain', 'it', 's', 'a', 'bad', 'idea', 'If', 'the', 'implementation', 'is', 'easy', 'to', 'explain', 'it', 'may', 'be', 'a', 'good', 'idea', 'Namespaces', 'are', 'one', 'honking', 'great', 'idea', 'let', 's', 'do', 'more', 'of', 'those', '']

词频统计

import re
from collections import Counter
data = """The Zen of Python, by Tim PetersBeautiful is better than ugly.Explicit is better than implicit.Simple is better than complex.Complex is better than complicated.Flat is better than nested.Sparse is better than dense.Readability counts.Special cases aren't special enough to break the rules.Although practicality beats purity.Errors should never pass silently.Unless explicitly silenced.In the face of ambiguity, refuse the temptation to guess.There should be one-- and preferably only one --obvious way to do it.Although that way may not be obvious at first unless you're Dutch.Now is better than never.Although never is often better than *right* now.If the implementation is hard to explain, it's a bad idea.If the implementation is easy to explain, it may be a good idea.Namespaces are one honking great idea -- let's do more of those!"""
# 分割单词
words = re.split('\W+', data)
# 统计词频
c2 = Counter(words)
print(c2)
# 找出词频最高的10个单词
res = c2.most_common(10)
print(res)

Counter({'is': 10, 'better': 8, 'than': 8, 'to': 5, 'the': 5, 'of': 3, 'Although': 3, 'never': 3, 'be': 3, 'one': 3, 'it': 3, 'idea': 3, '': 2, 'should': 2, 'obvious': 2, 'way': 2, 'do': 2, 'may': 2, 'If': 2, 'implementation': 2, 'explain': 2, 's': 2, 'a': 2, 'The': 1, 'Zen': 1, 'Python': 1, 'by': 1, 'Tim': 1, 'Peters': 1, 'Beautiful': 1, 'ugly': 1, 'Explicit': 1, 'implicit': 1, 'Simple': 1, 'complex': 1, 'Complex': 1, 'complicated': 1, 'Flat': 1, 'nested': 1, 'Sparse': 1, 'dense': 1, 'Readability': 1, 'counts': 1, 'Special': 1, 'cases': 1, 'aren': 1, 't': 1, 'special': 1, 'enough': 1, 'break': 1, 'rules': 1, 'practicality': 1, 'beats': 1, 'purity': 1, 'Errors': 1, 'pass': 1, 'silently': 1, 'Unless': 1, 'explicitly': 1, 'silenced': 1, 'In': 1, 'face': 1, 'ambiguity': 1, 'refuse': 1, 'temptation': 1, 'guess': 1, 'There': 1, 'and': 1, 'preferably': 1, 'only': 1, 'that': 1, 'not': 1, 'at': 1, 'first': 1, 'unless': 1, 'you': 1, 're': 1, 'Dutch': 1, 'Now': 1, 'often': 1, 'right': 1, 'now': 1, 'hard': 1, 'bad': 1, 'easy': 1, 'good': 1, 'Namespaces': 1, 'are': 1, 'honking': 1, 'great': 1, 'let': 1, 'more': 1, 'those': 1})
[('is', 10), ('better', 8), ('than', 8), ('to', 5), ('the', 5), ('of', 3), ('Although', 3), ('never', 3), ('be', 3), ('one', 3)]

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[Python高效编程] - 统计元素出现频度

开发环境

统计列表中的数字频度

生成数据

使用字典统计数据

使用迭代方法

使用标准库collections下的Counter函数

统计英文文章词频统计

数据来源

使用re.split()进行单词分割

词频统计

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