Web-Data
所属分类：硬件设计
开发工具：Jupyter Notebook
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上传日期：2023-11-20 22:31:26
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sh-1993
说明： 以在线数据集成和分析为中心的项目的Python代码可以在该存储库中找到。web抓取模块，XML...
(Python code for a project centered on online data integration and analysis may be found in this repository. Modules for web scraping, XML parsing, API data collecting, and exploratory data analysis (EDA) are all included in the project.)
文件列表:
LICENSE (1087, 2023-11-21)
VGiannaccini (1).ipynb (965505, 2023-11-21)
VGiannaccini.ipynb (827726, 2023-11-21)
pyproject.toml (773, 2023-11-21)
src/ (0, 2023-11-21)
src/web-data/ (0, 2023-11-21)
src/web-data/__init__.py (129, 2023-11-21)
src/web-data/api_handler.py (1726, 2023-11-21)
src/web-data/web_scraper.py (1020, 2023-11-21)
src/web-data/xml_parser.py (1496, 2023-11-21)
## Web Data Extraction and Modification with Python
### Overview:
In data science and analytics, operations like web data extraction and manipulation are crucial. This project uses Python to investigate three main areas: online scraping, API interaction, and XML parsing. Every task adds to a modular package in Python, which makes it possible to combine explanations and code in an easy-to-use manner.
### Tools and Technologies:
- Python 3.11.6
- Requests 2.31
- BeautifulSoup 4.12.2
- xml.etree.ElementTree (for XML parsing)
- Pandas 2.1.1
- Matplotlib 3.8.0
- Seaborn 0.13.0
- WordCloud 1.9.2
### XML Parsing:
In the XML parsing section, we delve into Apple's robots.txt file to explore the structure of their website. By extracting information from XML sitemaps, we use Python to traverse HTML structures and transform unprocessed data into clean Pandas DataFrames.
```python
# Example XML Parsing Code
from src.web_data.xml_parser import XmlParser
website_robots_url = "https://www.apple.com/robots.txt"
xml_parser = XmlParser(website_robots_url)
# Extract and print sitemap URLs
sitemap_urls = xml_parser.get_sitemap_urls()
print("Sitemap URLs:", sitemap_urls)
# Transform sitemap data into a DataFrame
sitemap_df = xml_parser.get_sitemap_data()
print(sitemap_df)
```
### Applying an API:
We enable the Open Trivia Database API under the API section to collect difficult questions and answers. Using this plethora of information, our Python class ApiHandler transforms raw data into a clean DataFrame suitable for insightful analysis.
### Getting Started:
To get started with the project, follow these steps:
```python
1. Clone the repository to your local machine.
2. Install the required dependencies using pip install -r requirements.txt.
3. Explore the provided scripts in the src/web_data directory for XML parsing, API handling, and more.
```
### References:
- [Requests Library Documentation](https://docs.python-requests.org/en/latest/)
- [Beautiful Soup Documentation](https://www.crummy.com/software/BeautifulSoup/bs4/doc/)
- [xml.etree.ElementTree Documentation](https://docs.python.org/3/library/xml.etree.elementtree.html)
- [Open Trivia Database API Documentation](https://opentdb.com/api_config.php)
- [Matplotlib Documentation](https://matplotlib.org/stable/contents.html)
- [Seaborn Documentation](https://seaborn.pydata.org/)
- [WordCloud Documentation](https://www.datacamp.com/tutorial/wordcloud-python)
## Additional Resources:
- [W3Schools - XML Tutorial](https://www.w3schools.com/xml/)
- [Open Trivia Database](https://opentdb.com/)
- [Apple Robots.txt](https://www.apple.com/robots.txt)
- [Real Python](https://realpython.com/)
## Contact Information:
- Email: vgiannac@mail.yu.edu.
- New York, NY.
- GitHub: [VGiannac]
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