Home Backend Development Python Tutorial Detailed Tutorial: Crawling GitHub Repository Folders Without API

Detailed Tutorial: Crawling GitHub Repository Folders Without API

Dec 16, 2024 am 06:28 AM

Detailed Tutorial: Crawling GitHub Repository Folders Without API

Ultra-Detailed Tutorial: Crawling GitHub Repository Folders Without API

This ultra-detailed tutorial, authored by Shpetim Haxhiu, walks you through crawling GitHub repository folders programmatically without relying on the GitHub API. It includes everything from understanding the structure to providing a robust, recursive implementation with enhancements.


1. Setup and Installation

Before you start, ensure you have:

  1. Python: Version 3.7 or higher installed.
  2. Libraries: Install requests and BeautifulSoup.

1

pip install requests beautifulsoup4

Copy after login
Copy after login
  1. Editor: Any Python-supported IDE, such as VS Code or PyCharm.

2. Analyzing GitHub HTML Structure

To scrape GitHub folders, you need to understand the HTML structure of a repository page. On a GitHub repository page:

  • Folders are linked with paths like /tree//.
  • Files are linked with paths like /blob//.

Each item (folder or file) is inside a

with the attribute role="rowheader" and contains an tag. For example:

1

2

3

<div role="rowheader">

  <a href="/owner/repo/tree/main/folder-name">folder-name</a>

</div>

Copy after login
Copy after login

3. Implementing the Scraper

3.1. Recursive Crawling Function

The script will recursively scrape folders and print their structure. To limit the recursion depth and avoid unnecessary load, we’ll use a depth parameter.

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

import requests

from bs4 import BeautifulSoup

import time

 

def crawl_github_folder(url, depth=0, max_depth=3):

    """

    Recursively crawls a GitHub repository folder structure.

 

    Parameters:

    - url (str): URL of the GitHub folder to scrape.

    - depth (int): Current recursion depth.

    - max_depth (int): Maximum depth to recurse.

    """

    if depth > max_depth:

        return

 

    headers = {"User-Agent": "Mozilla/5.0"}

    response = requests.get(url, headers=headers)

 

    if response.status_code != 200:

        print(f"Failed to access {url} (Status code: {response.status_code})")

        return

 

    soup = BeautifulSoup(response.text, 'html.parser')

 

    # Extract folder and file links

    items = soup.select('div[role="rowheader"] a')

 

    for item in items:

        item_name = item.text.strip()

        item_url = f"https://github.com{item['href']}"

 

        if '/tree/' in item_url:

            print(f"{'  ' * depth}Folder: {item_name}")

            crawl_github_folder(item_url, depth + 1, max_depth)

        elif '/blob/' in item_url:

            print(f"{'  ' * depth}File: {item_name}")

 

# Example usage

if __name__ == "__main__":

    repo_url = "https://github.com/<owner>/<repo>/tree/<branch>/<folder>"

    crawl_github_folder(repo_url)

Copy after login
Copy after login

4. Features Explained

  1. Headers for Request: Using a User-Agent string to mimic a browser and avoid blocking.
  2. Recursive Crawling:
    • Detects folders (/tree/) and recursively enters them.
    • Lists files (/blob/) without entering further.
  3. Indentation: Reflects folder hierarchy in the output.
  4. Depth Limitation: Prevents excessive recursion by setting a maximum depth (max_depth).

5. Enhancements

These enhancements are designed to improve the functionality and reliability of the crawler. They address common challenges like exporting results, handling errors, and avoiding rate limits, ensuring the tool is efficient and user-friendly.

5.1. Exporting Results

Save the output to a structured JSON file for easier usage.

1

pip install requests beautifulsoup4

Copy after login
Copy after login

5.2. Error Handling

Add robust error handling for network errors and unexpected HTML changes:

1

2

3

<div role="rowheader">

  <a href="/owner/repo/tree/main/folder-name">folder-name</a>

</div>

Copy after login
Copy after login

5.3. Rate Limiting

To avoid being rate-limited by GitHub, introduce delays:

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

import requests

from bs4 import BeautifulSoup

import time

 

def crawl_github_folder(url, depth=0, max_depth=3):

    """

    Recursively crawls a GitHub repository folder structure.

 

    Parameters:

    - url (str): URL of the GitHub folder to scrape.

    - depth (int): Current recursion depth.

    - max_depth (int): Maximum depth to recurse.

    """

    if depth > max_depth:

        return

 

    headers = {"User-Agent": "Mozilla/5.0"}

    response = requests.get(url, headers=headers)

 

    if response.status_code != 200:

        print(f"Failed to access {url} (Status code: {response.status_code})")

        return

 

    soup = BeautifulSoup(response.text, 'html.parser')

 

    # Extract folder and file links

    items = soup.select('div[role="rowheader"] a')

 

    for item in items:

        item_name = item.text.strip()

        item_url = f"https://github.com{item['href']}"

 

        if '/tree/' in item_url:

            print(f"{'  ' * depth}Folder: {item_name}")

            crawl_github_folder(item_url, depth + 1, max_depth)

        elif '/blob/' in item_url:

            print(f"{'  ' * depth}File: {item_name}")

 

# Example usage

if __name__ == "__main__":

    repo_url = "https://github.com/<owner>/<repo>/tree/<branch>/<folder>"

    crawl_github_folder(repo_url)

Copy after login
Copy after login

6. Ethical Considerations

Authored by Shpetim Haxhiu, an expert in software automation and ethical programming, this section ensures adherence to best practices while using the GitHub crawler.

  • Compliance: Adhere to GitHub’s Terms of Service.
  • Minimize Load: Respect GitHub’s servers by limiting requests and adding delays.
  • Permission: Obtain permission for extensive crawling of private repositories.

7. Complete Code

Here’s the consolidated script with all features included:

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

import json

 

def crawl_to_json(url, depth=0, max_depth=3):

    """Crawls and saves results as JSON."""

    result = {}

 

    if depth > max_depth:

        return result

 

    headers = {"User-Agent": "Mozilla/5.0"}

    response = requests.get(url, headers=headers)

 

    if response.status_code != 200:

        print(f"Failed to access {url}")

        return result

 

    soup = BeautifulSoup(response.text, 'html.parser')

    items = soup.select('div[role="rowheader"] a')

 

    for item in items:

        item_name = item.text.strip()

        item_url = f"https://github.com{item['href']}"

 

        if '/tree/' in item_url:

            result[item_name] = crawl_to_json(item_url, depth + 1, max_depth)

        elif '/blob/' in item_url:

            result[item_name] = "file"

 

    return result

 

if __name__ == "__main__":

    repo_url = "https://github.com/<owner>/<repo>/tree/<branch>/<folder>"

    structure = crawl_to_json(repo_url)

 

    with open("output.json", "w") as file:

        json.dump(structure, file, indent=2)

 

    print("Repository structure saved to output.json")

Copy after login

By following this detailed guide, you can build a robust GitHub folder crawler. This tool can be adapted for various needs while ensuring ethical compliance.


Feel free to leave questions in the comments section! Also, don’t forget to connect with me:

  • Email: shpetim.h@gmail.com
  • LinkedIn: linkedin.com/in/shpetimhaxhiu
  • GitHub: github.com/shpetimhaxhiu

The above is the detailed content of Detailed Tutorial: Crawling GitHub Repository Folders Without API. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

Java Tutorial
1664
14
PHP Tutorial
1267
29
C# Tutorial
1239
24
Python vs. C  : Applications and Use Cases Compared Python vs. C : Applications and Use Cases Compared Apr 12, 2025 am 12:01 AM

Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

Python: Games, GUIs, and More Python: Games, GUIs, and More Apr 13, 2025 am 12:14 AM

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

The 2-Hour Python Plan: A Realistic Approach The 2-Hour Python Plan: A Realistic Approach Apr 11, 2025 am 12:04 AM

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python vs. C  : Learning Curves and Ease of Use Python vs. C : Learning Curves and Ease of Use Apr 19, 2025 am 12:20 AM

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

How Much Python Can You Learn in 2 Hours? How Much Python Can You Learn in 2 Hours? Apr 09, 2025 pm 04:33 PM

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python and Time: Making the Most of Your Study Time Python and Time: Making the Most of Your Study Time Apr 14, 2025 am 12:02 AM

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python: Automation, Scripting, and Task Management Python: Automation, Scripting, and Task Management Apr 16, 2025 am 12:14 AM

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.

Python: Exploring Its Primary Applications Python: Exploring Its Primary Applications Apr 10, 2025 am 09:41 AM

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

See all articles