# Python Web Scraping Toolkit (`fipper_ai/python-web-scraping-toolkit`) Actor
- **URL**: https://apify.com/fipper\_ai/python-web-scraping-toolkit.md
- **Developed by:** [Josh Baker](https://apify.com/fipper_ai) (community)
- **Categories:** Automation, Developer tools, E-commerce
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
Pay per usage
This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
### PyScrappy: robust, all-in-one Python web scraping toolkit
[](https://www.python.org/downloads/)
[](https://pypi.org/project/PyScrappy/)
[](https://github.com/mldsveda/PyScrappy/blob/main/LICENSE)
PyScrappy is a Python toolkit for web scraping that works out of the box. Point it at any URL and get structured data back — or use built-in scrapers for Wikipedia, IMDB, Yahoo Finance, news feeds, and more.
#### Key features
- **Generic scraper** — give it any URL, get back structured text, links, images, tables, and metadata
- **Auto-pagination** — automatically follows "next page" links
- **JS rendering** — optional Playwright backend for JavaScript-heavy sites
- **Custom selectors** — pass CSS selectors to extract exactly what you need
- **Built-in scrapers** — Wikipedia, IMDB, Yahoo Finance, news (RSS), image search, Amazon, LinkedIn
- **Clean API** — every scraper returns a `ScrapeResult` with `.to_dataframe()` and `.to_json()`
- **Retry & rate-limiting** — built-in exponential backoff and per-domain rate limiting
- **Type-safe** — full type hints, `py.typed` marker
### Installation
```sh
pip install pyscrappy
````
**Optional extras:**
```sh
## Browser support (for JS-rendered pages)
pip install 'pyscrappy[browser]'
playwright install chromium
## DataFrame support
pip install 'pyscrappy[dataframe]'
## Everything
pip install 'pyscrappy[all]'
```
### Quick start
#### Scrape any URL (one-liner)
```python
from pyscrappy import scrape
result = scrape("https://en.wikipedia.org/wiki/Web_scraping")
print(result.data[0]["metadata"]["title"])
print(result.data[0]["text"]["word_count"])
```
#### Custom CSS selectors
```python
from pyscrappy import GenericScraper
with GenericScraper() as gs:
result = gs.scrape(
url="https://news.ycombinator.com",
selectors={"title": ".titleline a", "score": ".score"},
)
for item in result.data:
print(item["title"], item.get("score", ""))
```
#### Wikipedia
```python
from pyscrappy import WikipediaScraper
with WikipediaScraper() as ws:
result = ws.scrape(query="Python (programming language)", mode="summary")
print(result.data[0]["text"])
```
#### Stock data
```python
from pyscrappy import StockScraper
with StockScraper() as ss:
result = ss.scrape(symbol="AAPL", mode="history", period="1mo")
df = result.to_dataframe()
print(df.head())
```
#### IMDB
```python
from pyscrappy import IMDBScraper
with IMDBScraper() as scraper:
result = scraper.scrape(genre="sci-fi", max_pages=2)
df = result.to_dataframe()
print(df[["title", "year", "rating"]])
```
#### News (RSS feeds)
```python
from pyscrappy import NewsScraper
with NewsScraper() as ns:
result = ns.scrape(feed_url="https://rss.nytimes.com/services/xml/rss/nyt/World.xml")
for article in result.data[:5]:
print(article["title"])
```
#### Image search
```python
from pyscrappy import ImageSearchScraper
with ImageSearchScraper() as iss:
result = iss.scrape(query="golden retriever", max_images=10, download_to="./dogs")
```
### Configuration
```python
from pyscrappy import ScraperConfig, GenericScraper
config = ScraperConfig(
timeout=20.0, ## request timeout in seconds
max_retries=3, ## retry failed requests
rate_limit=2.0, ## seconds between requests per domain
proxy="http://...", ## HTTP/SOCKS proxy
headless=True, ## browser runs headless
render_js="auto", ## auto-detect if JS rendering is needed
)
with GenericScraper(config) as gs:
result = gs.scrape(url="https://example.com")
```
#### YouTube
```python
from pyscrappy import YouTubeScraper
with YouTubeScraper() as scraper:
result = scraper.scrape(query="python tutorial", max_results=10)
for video in result.data:
print(video["title"], video.get("views", ""))
```
#### SoundCloud
```python
from pyscrappy import SoundCloudScraper
with SoundCloudScraper() as scraper:
result = scraper.scrape(query="lo-fi beats", max_results=10)
```
#### E-Commerce (Alibaba, Flipkart, Snapdeal)
```python
from pyscrappy import AlibabaScraper, FlipkartScraper, SnapdealScraper
with FlipkartScraper() as scraper:
result = scraper.scrape(query="laptop", max_pages=2)
df = result.to_dataframe()
```
#### Food Delivery (Swiggy, Zomato)
```python
from pyscrappy import SwiggyScraper, ZomatoScraper
## These are JS-heavy — use render_js=True for best results
with SwiggyScraper() as scraper:
result = scraper.scrape(city="bangalore", render_js=True)
```
### Built-in scrapers
| Scraper | What it does | Needs browser? |
|---------|-------------|----------------|
| `GenericScraper` | Scrape any URL with auto-extraction | Optional |
| **Data / Research** | | |
| `WikipediaScraper` | Articles, sections, infoboxes | No |
| `IMDBScraper` | Movies by genre, search, charts | No |
| `StockScraper` | Quotes, history, profiles (Yahoo Finance) | No |
| `NewsScraper` | RSS/Atom feeds, article extraction | No |
| `ImageSearchScraper` | Image search + download | No |
| `LinkedInJobsScraper` | Public job listings | No |
| **E-Commerce** | | |
| `AmazonScraper` | Product search | No |
| `AlibabaScraper` | Product search | No |
| `FlipkartScraper` | Product search | No |
| `SnapdealScraper` | Product search | No |
| **Social Media** | | |
| `YouTubeScraper` | Video search, channel scraping | Optional |
| `InstagramScraper` | Profiles, hashtag posts | Recommended |
| `TwitterScraper` | Tweet search | Recommended |
| **Music** | | |
| `SpotifyScraper` | Track/playlist search | Recommended |
| `SoundCloudScraper` | Track search | Optional |
| **Food Delivery** | | |
| `SwiggyScraper` | Restaurant listings | Recommended |
| `ZomatoScraper` | Restaurant listings | Recommended |
### Dependencies
**Required:** `httpx`, `beautifulsoup4`, `lxml`
**Optional:** `playwright` (JS rendering), `pandas` (DataFrames)
### License
[MIT](https://github.com/mldsveda/PyScrappy/blob/main/LICENSE)
### Contributing
All contributions welcome. See [Issues](https://github.com/mldsveda/PyScrappy/issues).
**This package is for educational and research purposes.**
## Actor input object example
```json
{}
```
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {};
// Run the Actor and wait for it to finish
const run = await client.actor("fipper_ai/python-web-scraping-toolkit").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {}
# Run the Actor and wait for it to finish
run = client.actor("fipper_ai/python-web-scraping-toolkit").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{}' |
apify call fipper_ai/python-web-scraping-toolkit --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=fipper_ai/python-web-scraping-toolkit",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Python Web Scraping Toolkit",
"version": "0.0",
"x-build-id": "owi8ux4dJALuxgupr"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/fipper_ai~python-web-scraping-toolkit/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-fipper_ai-python-web-scraping-toolkit",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/fipper_ai~python-web-scraping-toolkit/runs": {
"post": {
"operationId": "runs-sync-fipper_ai-python-web-scraping-toolkit",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/fipper_ai~python-web-scraping-toolkit/run-sync": {
"post": {
"operationId": "run-sync-fipper_ai-python-web-scraping-toolkit",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```