How to Compute Word Frequencies in Python with Jieba and Export to Excel

Introduction

Hello, I am Pipi. A question about word‑frequency processing in Python was raised in a Python community group, and I share the solution here.

Below is the original code that simply counts word frequencies using collections.Counter:

# 统计词频
from collections import import Counter

wordcount = Counter(all_words)
word_count = wordcount.most_common(30)

frequence_list = []
for i in range(len(word_count)):
    frequence_list.append(word_count[i][0])
frequence_list

Implementation

A contributor provided a more complete solution that uses jieba for Chinese word segmentation, writes the frequencies to a text file, and then stores the results in an Excel workbook using xlwt.

#!/usr/bin/env python3
# -*- coding:utf-8 -*-

import sys
import jieba
import jieba.analyse
import xlwt  # write to Excel

if __name__ == "__main__":
    wbk = xlwt.Workbook(encoding='ascii')
    sheet = wbk.add_sheet("wordCount")
    word_lst = []
    key_list = []
    for line in open('./《都挺好》阿耐_分词后_outputs.txt', 'r', encoding='utf-8'):
        item = line.strip('
\r').split('\t')
        tags = jieba.analyse.extract_tags(item[0])
        for t in tags:
            word_lst.append(t)
    word_dict = {}
    with open("分词结果.txt", 'w') as wf2:
        for item in word_lst:
            if item not in word_dict:
                word_dict[item] = 1
            else:
                word_dict[item] += 1
        orderList = list(word_dict.values())
        orderList.sort(reverse=True)
        for i in range(len(orderList)):
            for key in word_dict:
                if word_dict[key] == orderList[i]:
                    wf2.write(key + ' ' + str(word_dict[key]) + '
')
                    key_list.append(key)
                    word_dict[key] = 0
    for i in range(len(key_list)):
        sheet.write(i, 1, label=orderList[i])
        sheet.write(i, 0, label=key_list[i])
    wbk.save('wordCount_all_lyrics.xls')

Other participants also shared alternative methods, such as converting the segmentation results into a pandas DataFrame and using groupby to aggregate frequencies.

Conclusion

The article outlines a practical approach to processing Chinese text for word‑frequency analysis in Python, offering complete code, explanations, and multiple community‑contributed solutions to help readers solve similar problems.