CrawlFlow 0.0.3

An assistant for web crawling

CrawlFlow

This module is a tool for web scrap handling.

How to use: Importing packages

import sys
#Adding crawl_handler as a python path
sys.path.append("../crawl_handler/")

from CrawlFlow.handler import Handler
from CrawlFlow import types
import json
import pandas as pd

Crawl types if you are getting an API request

self.type=types.API()

and if you are getting an HTML request and using BS4

self.type=types.BS4()

Defining the crawler class

class TestCrawler(Handler):
    def __init__(self, name):
        super().__init__(name)
        self.type=CrawlTypes.API()

  def data2rows(self, request_data):
          '''you scrap code here'''

          tables_rows={'videoList': videoList}
          
          return tables_rows

You need to define a method in the class called "data2rows". in this function you will get a variable called "request_data" that is your desired data (in API, json data/ in BS4, BS4 object)

Output of this function must be a python dictionary in this format:

tables_rows={'table1': table1,
'table2': table2, ...}

tables are list of dicts like below:

[ {'col1': 'col1_data', 'col2': 'col2_data'},
{'col1': 'col1_data', 'col2': 'col2_data'},
...]

Now we use our scrap class

  scraper=TestCrawler('name of your scrap query')
  scraper.request.sleep_time=1.5
  scraper.chk_point_interval=20
  scraper.init()
  url_list=['list of URLs that you want to scrap']
  scraper.run(url_list, resume=False, dynamic=False)
  scraper.export_tables()

Setting Headers, Cookies and Params

#setting manullay:
Handler.request.headers=Headers
Handler.request.cookies=Cookies
Handler.request.params=Params

#Or you can read from json file
Handler.request.read_headers(headers_path)
Handler.request.read_cookies(cookies_path)

Handler.chk_point_interval

Request interval for checkpoint data

Handler.directory

Director for saving the data

Hanlder.run(url_list, resume=False, dynamic=False)

url_list: List ==> list of URLs

resume: False==> new start / True==> continue from last checkpoint

dynamic: False==> standard scraping due to url_list / True==> for cases that have next url keys

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Dynamic Scraping:

By setting dynamic = True in Hanlder.run method, you can use dynamic scraping

"url_list" should contian first url that starts scrping

class TestCrawler(Handler):
    def __init__(self, name):
        super().__init__(name)
        self.type=CrawlTypes.API()
 
    def data2rows(self, request_data):
        self.data=request_data
        if request_data['data'][0]['link'] is  None:
            self.vars.done=True
            return {}
        self.vars.next_url = f'https://www.aparat.com/api/fa/v1/video/video/search/text/'+self.get(request_data['data'][0], ['link', 'next']).split('text/')[1]
        
        videoList=self.get(request_data['data'][0], ['video', 'data']) 
        tables_rows={'videoList': videoList}
        return tables_rows


crawler=TestCrawler('aparat')
crawler.request.params={'type_search': 'search'}
crawler.init()
url_list=['https://www.aparat.com/api/fa/v1/video/video/search/text/game']
crawler.run(url_list, resume=False, dynamic=True)
crawler.export_tables()

Note: Hanlder.vars is an object that will be save in every ckeckpoint. so you set your variables here

If you set Hanlder.vars.done=True scraping process will be finished after that.

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