Python词频统计代码

发布时间:2023-05-10

引言

在大量文本数据中,关键词的频次统计往往是必不可少的。无论是从市场营销角度还是从学术角度,词频统计都占有重要的地位。而Python作为一种擅长文本处理的编程语言,提供了一种快速而准确的词频统计方法。

正文

hadoop词频统计代码

Hadoop作为一种分布式框架,自然也提供了词频统计的功能。其词频统计的实现是基于MapReduce的,需要编写Mapper和Reducer程序。以下是一个简单的hadoop词频统计代码实例:

import re
import sys
from collections import Counter
from hadoop import HadoopJobRunner
WORD_RE = re.compile(r'\w+')
def mapper(line):
    for word in WORD_RE.findall(line):
        yield word.lower(), 1
def reducer(word, counts):
    yield word, sum(counts)
if __name__ == '__main__':
    input_file = sys.argv[1]
    with open(input_file, 'r') as f:
        lines = f.readlines()
    runner = HadoopJobRunner()
    runner.set_mapper(mapper)
    runner.set_reducer(reducer)
    runner.set_input(lines)
    runner.set_output_format('text')
    counts = Counter(runner.run())
    for word, count in counts.items():
        print(word, count)

英语单词词频统计代码

对于英语文本,可以使用Python的collections库中的Counter类进行词频统计。以下是一个简单的英语单词词频统计的Python代码实例:

import re
from collections import Counter
text = "This is a sample text with several words. It contains some duplicate words as well."
words = re.findall('\w+', text.lower())
word_counts = Counter(words)
for word, count in word_counts.items():
    print(word, count)

wordcount词频统计代码

另外,Python还提供了一个最简单的词频统计方法——wordcount。这个方法可以直接对文本进行分词和计数,不需要进行任何额外的处理。以下是一个wordcount的Python代码实例:

from collections import Counter
text = "This is a sample text with several words. It contains some duplicate words as well."
word_counts = Counter(text.split())
for word, count in word_counts.items():
    print(word, count)

红楼梦词频统计Python代码

对于中文文本而言,词语之间没有像英文文本那样直接的边界,因此需要使用中文分词工具进行分词。这里以《红楼梦》的文本为例,使用jieba分词库对红楼梦进行词频统计。以下是一个红楼梦词频统计的Python代码实例:

import jieba
from collections import Counter
with open('hongloumeng.txt', 'r') as f:
    text = f.read()
words = jieba.lcut(text)
word_counts = Counter(words)
for word, count in word_counts.items():
    print(word, count)

中文词频统计Python代码

同样地,对于中文文本,我们也可以使用Python内置的collections库中的Counter类进行中文词频统计。以下是一个简单的中文词频统计代码示例:

from collections import Counter
text = "这是一个样本文本,其中包含了多个词语和一些重复的词语。"
words = [word for word in jieba.lcut(text)]
word_counts = Counter(words)
for word, count in word_counts.items():
    print(word, count)

文本词频统计Python代码

对于文本的词频统计而言,我们可以将文本中所有的文件合并为一个整体进行统计。以下是一个简单的文本词频统计Python代码示例:

import os
import re
from collections import Counter
WORD_RE = re.compile(r'\w+')
texts = []
for filename in os.listdir('texts'):
    with open(os.path.join('texts', filename), 'r') as f:
        texts.append(f.read())
words = []
for text in texts:
    words += WORD_RE.findall(text.lower())
word_counts = Counter(words)
for word, count in word_counts.most_common(10):
    print(word, count)

Python英文词频统计代码

除了使用collections库中的Counter类之外,我们还可以使用pandas库进行词频统计。pandas库提供了一个简单且易用的方法——value_counts,一行代码就可以搞定英文词频统计。以下是一个Python英文词频统计代码示例:

import pandas as pd
text = "This is a sample text with several words. It contains some duplicate words as well."
words = text.split()
word_counts = pd.Series(words).value_counts()
print(word_counts)

利用Python进行词频统计代码

综上所述,Python提供了多种快速而准确的词频统计方法,这些方法的灵活性和易用性也使得词频统计变得更加容易。 最后,我们来看一个完整的程序,该程序可以实现对指定文本的词频分析、特定词语的查询和高频词统计。以下是Python中文文本词频统计代码实例:

import os
import re
from collections import Counter
import jieba
WORD_RE = re.compile(r'\w+')
def word_count(filename):
    with open(filename, 'r') as f:
        text = f.read()
    words = [word for word in jieba.lcut(text)]
    word_counts = Counter(words)
    for word, count in word_counts.items():
        print(word, count)
def search_word(filename, word):
    with open(filename, 'r') as f:
        text = f.read()
    words = [word for word in jieba.lcut(text)]
    word_counts = Counter(words)
    print('{}出现的次数:{}'.format(word, word_counts[word]))
def top_words(filename):
    with open(filename, 'r') as f:
        text = f.read()
    words = [word for word in jieba.lcut(text)]
    word_counts = Counter(words)
    for word, count in word_counts.most_common(10):
        print(word, count)
if __name__ == '__main__':
    filename = 'hongloumeng.txt'
    word = '贾宝玉'
    word_count(filename)
    search_word(filename, word)
    top_words(filename)

结论

通过上述代码示例,我们可以看到Python在词频统计方面的强大实力。在当前大数据时代,同样的方法也可以应用于更大规模的数据中,从而提取更有价值的信息。