Scrapyio is a web scraping framework that is asynchronous, fast, and easy to use.

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Install scrapyio using pip:

$ pip install scrapyio

Let's begin with the most basic examples.

from scrapyio import BaseSpider

class Spider(BaseSpider):
    start_requests = ["https://scrapyio-example.com"]

    async def parse(self, response):
        print("I have received the response!!", response)
        yield None

First, create your BaseSpider subclass and name it (in this case, Spider). You must instruct scrapyio on where to begin the requests, i.e. the urls to be scraped.

Things to keep in mind

• start_requests is a class variable that instructs scrapyio to scrape these urls during the first iteration.
• parse is an asynchronous generator method with a single argument, response, which is an HTTP response object containing the page content to be scraped.
• iteration Scrapyio is an asynchronous framework, so if you populate the start requests variable with a thousand URLs, they will be handled concurrently by the Python asyncio framework. So let's call the url sending process an iteration.

Requests

As you know, you can pass the url string in start requests to tell Scrapyio which content you want to download; however, some websites require additional headers to function properly; therefore, we can pass Request objects explicitly rather than only strings.

so we can replace

start_requests = ["https://scrapyio-example.com"]

with

start_requests = [Request(url="https://scrapyio-example.com", method="GET", headers={"custom-header": "test"})]

If we use the Request object directly, we can specify many request-specific parameters such as proxies, authorization, headers, cookies, whether to follow-redirect or not, and so on.

There is more complex Request object.

from scrapyio import Request
start_request = [
    Request(
        url="https://scrapyio-example.com",
        headers={"custom-key": "custom-value"},
        proxies={"http": "...", "https": "..."},
        cookies={"cookie-key": "cookie-value"},
        follow_redirects=True,
        verify=False
    )
]

Parsing

After you've built your Request with all of the necessary headers, data, and so on, you should write your parsing logic; every response that is successfully installed will call your spider's parse method, which will return the response that was downloaded.

class Spider(BaseSpider):
    start_requests = ["https://www.scrapethissite.com/pages/simple/"]

    async def parse(self, response):
        print("I have received the response!!", response)
        yield None

Scrapyio is a parser free framework and let's you use any parser you want, in our case, we will use beautifulsoup library

class Spider(BaseSpider):
    start_requests = ["https://www.scrapethissite.com/pages/simple/"]

    async def parse(self, response):
        soup = BeautifulSoup(response.text, "html.parser")
        countries = soup.select(".country")

        for country in countries:
            country_name = country.select_one(".country-name").text.strip()
            country_population = int(
                country.select_one(".country-population").text.strip()
            )
            country_capital = country.select_one(".country-capital").text.strip()
            country_area = float(country.select_one(".country-area").text.strip())

            yield Country(
                name=country_name,
                population=country_population,
                capital=country_capital,
                area=country_area,
            )

It's natural to be confused if you've never used beautifulsoup before, but the parsing process isn't particularly important; you can parse however you want, using python regex, parsers, or any other tool.

In this case, we're using Python's yield syntax to tell Scrapyio which Item to process and possibly save in the future.

So we get a Country instance, which is also a pydantic subclass. Scrapyio validates and serializes the data parsed from the website using Pydantic.

from scrapyio import Item
class Country(Item):
    tablename: str = "MyTable" 
    name: str
    population: int
    capital: str
    area: float

ANDDDDD..... THAT'S ALL.

Running

Now you can run scrapyio and tell him to save the items as JSON, CSV, or in any SQLAlchemy-supported database. For this, you can use the scrapyio command line tool.

$ scrapyio run --help
Usage: scrapyio run [OPTIONS] SPIDER

Options:
  -j, --json TEXT  Json file path
  -c, --csv TEXT   Csv file path
  -s, --sql TEXT   SQL URI supported by SQLAlchemy
  --help           Show this message and exit.

Let's run scrapyio and export data in JSON and CSV formats.

$ scrapyio run Spider --json data.json --csv data.csv

data.json

[
{
  "name": "Andorra",
  "population": 84000,
  "capital": "Andorra la Vella",
  "area": 468.0
},
{
  "name": "United Arab Emirates",
  "population": 4975593,
  "capital": "Abu Dhabi",
  "area": 82880.0
},
...
]

data.csv

name,population,capital,area
Andorra,84000,Andorra la Vella,468.0
United Arab Emirates,4975593,Abu Dhabi,82880.0
Afghanistan,29121286,Kabul,647500.0
Antigua and Barbuda,86754,St. John's,443.0
Anguilla,13254,The Valley,102.0

Alternatively, we can save the data to a PostgreSQL database.

$ scrapyio run Spider --sql postgresql+asyncpg://test:test@127.0.0.1:5432/postgres

Then select it in the psql command tool.

postgres=# select * from "Mytable";
 id  |                     name                     | population |       capital       |   area   
-----+----------------------------------------------+------------+---------------------+----------
   1 | Andorra                                      |      84000 | Andorra la Vella    |      468
   2 | United Arab Emirates                         |    4975593 | Abu Dhabi           |    82880
   3 | Afghanistan                                  |   29121286 | Kabul               |   647500
   4 | Antigua and Barbuda                          |      86754 | St. John's          |      443
   5 | Anguilla                                     |      13254 | The Valley          |      102
   6 | Albania                                      |    2986952 | Tirana              |    28748
   7 | Armenia                                      |    2968000 | Yerevan             |    29800
   8 | Angola                                       |   13068161 | Luanda              |  1246700
   9 | Antarctica                                   |          0 | None                | 14000000