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。