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。