| Name | snakyscraper JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | https://github.com/riodevnet/snakyscraper |
| Summary | SnakyScraper is a lightweight and Pythonic web scraping toolkit built on top of BeautifulSoup and Requests. It provides an elegant interface for extracting structured HTML and metadata from websites with clean, direct outputs. |
| upload_time | 2025-08-15 08:01:07 |
| maintainer | None |
| docs_url | None |
| author | Rio Dev |
| requires_python | >=3.6 |
| license | MIT |
| keywords |
snakyscraper
scraping
scraper
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# 🐍 SnakyScraper
**SnakyScraper** is a lightweight and Pythonic web scraping toolkit built on top of BeautifulSoup and Requests. It provides an elegant interface for extracting structured HTML and metadata from websites with clean, direct outputs.
> Fast. Accurate. Snake-style scraping. 🐍🎯
---
## 🚀 Features
- ✅ Extract metadata: title, description, keywords, author, and more
- ✅ Built-in support for Open Graph, Twitter Card, canonical, and CSRF tags
- ✅ Extract HTML structures: `h1`–`h6`, `p`, `ul`, `ol`, `img`, links
- ✅ Powerful `filter()` method with class, ID, and tag-based selectors
- ✅ `return_html` toggle to return clean text or raw HTML
- ✅ Simple return values: string, list, or dictionary
- ✅ Powered by BeautifulSoup4 and Requests
---
## 📦 Installation
```bash
pip install snakyscraper
```
> Requires Python 3.7 or later
---
## 🛠️ Basic Usage
```python
from snakyscraper import SnakyScraper
scraper = SnakyScraper("https://example.com")
# Get the page title
print(scraper.title()) # "Welcome to Example.com"
# Get meta description
print(scraper.description()) # "This is the example meta description."
# Get all <h1> elements
print(scraper.h1()) # ["Welcome", "Latest News"]
# Extract Open Graph metadata
print(scraper.open_graph()) # {"og:title": "...", "og:description": "...", ...}
# Custom filter: find all div.card elements and extract child tags
print(scraper.filter(
element="div",
attributes={"class": "card"},
multiple=True,
extract=["h1", "p", ".title", "#desc"]
))
```
---
## 🧪 Available Methods
### 🔹 Page Metadata
```python
scraper.title()
scraper.description()
scraper.keywords()
scraper.keyword_string()
scraper.charset()
scraper.canonical()
scraper.content_type()
scraper.author()
scraper.csrf_token()
scraper.image()
```
### 🔹 Open Graph & Twitter Card
```python
scraper.open_graph()
scraper.open_graph("og:title")
scraper.twitter_card()
scraper.twitter_card("twitter:title")
```
### 🔹 Headings & Text
```python
scraper.h1()
scraper.h2()
scraper.h3()
scraper.h4()
scraper.h5()
scraper.h6()
scraper.p()
```
### 🔹 Lists
```python
scraper.ul()
scraper.ol()
```
### 🔹 Images
```python
scraper.images()
scraper.image_details()
```
### 🔹 Links
```python
scraper.links()
scraper.link_details()
```
---
## 🔍 Custom DOM Filtering
Use `filter()` to target specific DOM elements and extract nested content.
#### ▸ Single element
```python
scraper.filter(
element="div",
attributes={"id": "main"},
multiple=False,
extract=[".title", "#description", "p"]
)
```
#### ▸ Multiple elements
```python
scraper.filter(
element="div",
attributes={"class": "card"},
multiple=True,
extract=["h1", ".subtitle", "#meta"]
)
```
> The `extract` argument accepts tag names, class selectors (e.g., `.title`), or ID selectors (e.g., `#meta`).
> Output keys are automatically normalized:
> `.title` → `class__title`, `#meta` → `id__meta`
#### ▸ Clean Text Output
You can also disable raw HTML output:
```python
scraper.filter(
element="p",
attributes={"class": "dark-text"},
multiple=True,
return_html=False
)
```
---
## 📦 Output Example
```python
scraper.title()
# "Welcome to Example.com"
scraper.h1()
# ["Main Heading", "Another Title"]
scraper.open_graph("og:title")
# "Example OG Title"
```
---
## 🤝 Contributing
Contributions are welcome!
Found a bug or want to request a feature? Please open an [issue](https://github.com/riodevnet/snakyscraper/issues) or submit a pull request.
---
## 📄 License
MIT License © 2025 — SnakyScraper
---
## 🔗 Related Projects
- [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup/)
- [Requests](https://docs.python-requests.org/)
- [lxml](https://lxml.de/)
---
## 💡 Why SnakyScraper?
> Think of it as your Pythonic sniper — targeting HTML content with precision and elegance.
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"description": "\r\n# \ud83d\udc0d SnakyScraper\r\n\r\n**SnakyScraper** is a lightweight and Pythonic web scraping toolkit built on top of BeautifulSoup and Requests. It provides an elegant interface for extracting structured HTML and metadata from websites with clean, direct outputs.\r\n\r\n> Fast. Accurate. Snake-style scraping. \ud83d\udc0d\ud83c\udfaf\r\n\r\n---\r\n\r\n## \ud83d\ude80 Features\r\n\r\n- \u2705 Extract metadata: title, description, keywords, author, and more\r\n- \u2705 Built-in support for Open Graph, Twitter Card, canonical, and CSRF tags\r\n- \u2705 Extract HTML structures: `h1`\u2013`h6`, `p`, `ul`, `ol`, `img`, links\r\n- \u2705 Powerful `filter()` method with class, ID, and tag-based selectors\r\n- \u2705 `return_html` toggle to return clean text or raw HTML\r\n- \u2705 Simple return values: string, list, or dictionary\r\n- \u2705 Powered by BeautifulSoup4 and Requests\r\n\r\n---\r\n\r\n## \ud83d\udce6 Installation\r\n\r\n```bash\r\npip install snakyscraper\r\n```\r\n\r\n> Requires Python 3.7 or later\r\n\r\n---\r\n\r\n## \ud83d\udee0\ufe0f Basic Usage\r\n\r\n```python\r\nfrom snakyscraper import SnakyScraper\r\n\r\nscraper = SnakyScraper(\"https://example.com\")\r\n\r\n# Get the page title\r\nprint(scraper.title()) # \"Welcome to Example.com\"\r\n\r\n# Get meta description\r\nprint(scraper.description()) # \"This is the example meta description.\"\r\n\r\n# Get all <h1> elements\r\nprint(scraper.h1()) # [\"Welcome\", \"Latest News\"]\r\n\r\n# Extract Open Graph metadata\r\nprint(scraper.open_graph()) # {\"og:title\": \"...\", \"og:description\": \"...\", ...}\r\n\r\n# Custom filter: find all div.card elements and extract child tags\r\nprint(scraper.filter(\r\n element=\"div\",\r\n attributes={\"class\": \"card\"},\r\n multiple=True,\r\n extract=[\"h1\", \"p\", \".title\", \"#desc\"]\r\n))\r\n```\r\n\r\n---\r\n\r\n## \ud83e\uddea Available Methods\r\n\r\n### \ud83d\udd39 Page Metadata\r\n\r\n```python\r\nscraper.title()\r\nscraper.description()\r\nscraper.keywords()\r\nscraper.keyword_string()\r\nscraper.charset()\r\nscraper.canonical()\r\nscraper.content_type()\r\nscraper.author()\r\nscraper.csrf_token()\r\nscraper.image()\r\n```\r\n\r\n### \ud83d\udd39 Open Graph & Twitter Card\r\n\r\n```python\r\nscraper.open_graph()\r\nscraper.open_graph(\"og:title\")\r\n\r\nscraper.twitter_card()\r\nscraper.twitter_card(\"twitter:title\")\r\n```\r\n\r\n### \ud83d\udd39 Headings & Text\r\n\r\n```python\r\nscraper.h1()\r\nscraper.h2()\r\nscraper.h3()\r\nscraper.h4()\r\nscraper.h5()\r\nscraper.h6()\r\nscraper.p()\r\n```\r\n\r\n### \ud83d\udd39 Lists\r\n\r\n```python\r\nscraper.ul()\r\nscraper.ol()\r\n```\r\n\r\n### \ud83d\udd39 Images\r\n\r\n```python\r\nscraper.images()\r\nscraper.image_details()\r\n```\r\n\r\n### \ud83d\udd39 Links\r\n\r\n```python\r\nscraper.links()\r\nscraper.link_details()\r\n```\r\n\r\n---\r\n\r\n## \ud83d\udd0d Custom DOM Filtering\r\n\r\nUse `filter()` to target specific DOM elements and extract nested content.\r\n\r\n#### \u25b8 Single element\r\n\r\n```python\r\nscraper.filter(\r\n element=\"div\",\r\n attributes={\"id\": \"main\"},\r\n multiple=False,\r\n extract=[\".title\", \"#description\", \"p\"]\r\n)\r\n```\r\n\r\n#### \u25b8 Multiple elements\r\n\r\n```python\r\nscraper.filter(\r\n element=\"div\",\r\n attributes={\"class\": \"card\"},\r\n multiple=True,\r\n extract=[\"h1\", \".subtitle\", \"#meta\"]\r\n)\r\n```\r\n\r\n> The `extract` argument accepts tag names, class selectors (e.g., `.title`), or ID selectors (e.g., `#meta`). \r\n> Output keys are automatically normalized: \r\n> `.title` \u2192 `class__title`, `#meta` \u2192 `id__meta`\r\n\r\n#### \u25b8 Clean Text Output\r\n\r\nYou can also disable raw HTML output:\r\n\r\n```python\r\nscraper.filter(\r\n element=\"p\",\r\n attributes={\"class\": \"dark-text\"},\r\n multiple=True,\r\n return_html=False\r\n)\r\n```\r\n\r\n---\r\n\r\n## \ud83d\udce6 Output Example\r\n\r\n```python\r\nscraper.title()\r\n# \"Welcome to Example.com\"\r\n\r\nscraper.h1()\r\n# [\"Main Heading\", \"Another Title\"]\r\n\r\nscraper.open_graph(\"og:title\")\r\n# \"Example OG Title\"\r\n```\r\n\r\n---\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nContributions are welcome! \r\nFound a bug or want to request a feature? Please open an [issue](https://github.com/riodevnet/snakyscraper/issues) or submit a pull request.\r\n\r\n---\r\n\r\n## \ud83d\udcc4 License\r\n\r\nMIT License \u00a9 2025 \u2014 SnakyScraper\r\n\r\n---\r\n\r\n## \ud83d\udd17 Related Projects\r\n\r\n- [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup/)\r\n- [Requests](https://docs.python-requests.org/)\r\n- [lxml](https://lxml.de/)\r\n\r\n---\r\n\r\n## \ud83d\udca1 Why SnakyScraper?\r\n\r\n> Think of it as your Pythonic sniper \u2014 targeting HTML content with precision and elegance.\r\n",
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