What TV Fans Say: Analyzing 97,331 Danmu Comments with Python

1. Data Preview

The dataset contains 97,331 danmu entries from the three parts of the first episode. The following code shows how the Excel file is read and its fields inspected.

import pandas as pd
df = pd.read_excel('披荆斩棘的哥哥.xlsx')
df.info()

Fields: ids (string) – danmu ID, uid (Int64) – user ID, content (string) – comment text, time (Int64) – timestamp in milliseconds, v2_up_count (Int64) – like count, 时间 (Int64) – time in minutes, 上中下 (string) – segment (upper, middle, lower).

2. Overall Word Cloud

A word cloud generated with a custom tool shows that viewers mainly expressed laughter and applause.

3. Top‑Liked Danmu

The ten most liked comments are all from the middle part, especially during Zhao Wenzhuo’s performance of “流星雨”.

df.sort_values(by='v2_up_count', ascending=False).head(10).style.hide_index().hide_columns(['ids','uid','time'])

4. Most Active Danmu Users

One user posted 176 comments over 4.5 hours, averaging 0.65 comments per minute, making them the “danmu maniac”.

df.groupby('uid')['ids'].count().sort_values(ascending=False).to_frame('弹幕数').reset_index().head()

5. Most Popular Performers

Keyword analysis reveals that the “Da Wan Qu” group (Chen Xiaochun, Xie Tianhua, Lin Xiaofeng, Zhang Zhiling, Liang Hanwen) and individual stars Zhao Wenzhuo, Li Chengxuan, Ouyang Jing, and Zhang Yunlong received the most attention.

df[df['content'].astype('str').str.contains('大湾区|小春|春哥|谢天华|林晓峰|张智霖|梁汉文')]

6. Audience Evaluation of the Show

Many comments praise the Mango TV production.

df[df['content'].astype('str').str.contains('芒果')]

7. Danmu Data Collection Script

The following Python script fetches the JSON bullet data for each segment and builds a pandas DataFrame.

import requests
import pandas as pd

headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.66 Safari/537.36"}

datas = []
for i in range(100):
    url = f'https://bullet-ali.hitv.com/bullet/2021/08/17/192249/13137070/{i}.json'
    r = requests.get(url, headers=headers)
    if r.status_code == 200:
        data = r.json()['data']['items']
        datas.extend(data)
    else:
        break

df = pd.DataFrame(datas)
df = df[['ids','uid','content','time','v2_up_count']].fillna(0)
df['时间'] = df.time // 60000