Big Data 5 min read

How to Scrape and Visualize the 2021 China Hurun Rich List with Python

This tutorial demonstrates how to collect the top 2000 entries of the 2021 China Hurun Rich List using Python's requests library, process the data with pandas, and create insightful visualizations in Tableau, covering data extraction, cleaning, and analysis steps.

Python Crawling & Data Mining
Python Crawling & Data Mining
Python Crawling & Data Mining
How to Scrape and Visualize the 2021 China Hurun Rich List with Python

2021 China Hurun Rich List

The Hurun Institute released its 23rd annual China Rich List in 2021, maintaining a 20‑billion‑yuan entry threshold for the ninth consecutive year.

Data Collection

Data source: https://www.hurun.net/zh-CN/Rank/HsRankDetails?pagetype=rich. Open the page, press F12 and Ctrl+R to view network requests. The request URL contains offset (page number) and limit (max items per request). Setting limit=2000 retrieves all entries in a single call.

import requests
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.54 Safari/537.36'}
params = {'num': 'YUBAO34E', 'offset': 0, 'limit': 2000}
url = 'https://www.hurun.net/zh-CN/Rank/HsRankDetailsList'
page_text = requests.get(url=url, headers=headers, params=params).json()
page_text

The JSON response contains many fields; we extract the ones needed for analysis.

df['birth_place_split'] = df['birth_place'].str.split('-')
df['birth_place_split'] = df['birth_place_split'].apply(lambda x: '' if len(x) == 1 else x[1])
df['photo_split'] = df['photo'].apply(lambda x: x.split('/')[-1])
df.head()

After cleaning, the dataset is saved locally for further processing.

Visualization

Visualization tool: Tableau 2021.3.

Top 10 Richest

Top 10 Richest
Top 10 Richest

The top‑10 list includes several familiar names, with industries such as beverages and healthcare appearing prominently.

Age Distribution

Age Distribution
Age Distribution

About 74% of the listed individuals are aged 45‑70, with the most common age being 58 (125 people).

Birthplace Distribution

Birthplace Distribution
Birthplace Distribution

The top three birthplaces are Zhejiang, Guangzhou, and Jiangsu.

Hot Industries

Hot Industries
Hot Industries

Dominant sectors include real estate, investment, pharmaceuticals, food, and chemicals.

Combined Dashboard

Interactive Dashboard
Interactive Dashboard

An integrated dashboard with linked visualizations provides a comprehensive overview of the dataset.

Original Source

Signed-in readers can open the original source through BestHub's protected redirect.

Sign in to view source
Republication Notice

This article has been distilled and summarized from source material, then republished for learning and reference. If you believe it infringes your rights, please contactadmin@besthub.devand we will review it promptly.

Pythonvisualizationdata-analysisweb-scrapinghurunrich-list
Python Crawling & Data Mining
Written by

Python Crawling & Data Mining

Life's short, I code in Python. This channel shares Python web crawling, data mining, analysis, processing, visualization, automated testing, DevOps, big data, AI, cloud computing, machine learning tools, resources, news, technical articles, tutorial videos and learning materials. Join us!

0 followers
Reader feedback

How this landed with the community

Sign in to like

Rate this article

Was this worth your time?

Sign in to rate
Discussion

0 Comments

Thoughtful readers leave field notes, pushback, and hard-won operational detail here.