Scrapy Basic

Required Input

• Parsing code (Python program that defines what data to be extracted from HMTL)

  https://docs.scrapy.org/en/latest/intro/tutorial.html

• URL or a list of URLs (You can use a text file as well)

Output/Return Value

• A CSV will be returned. (Preferred)

  Headers will be defined in the Parsing code.

• Any string is possible for the user’s purpose.

Advanced Feature

• Parameters

  You can pass on “values” to either your Parsing Code or to the URL(s).

Syntax is the standard Python String Named Placeholder rule.

https://riptutorial.com/python/example/13577/named-placeholders

https://pyformat.info/

How to set parameters.

 Text from Image   

Sample Spider Code

import sys

import csv

import scrapy

from random import randint

################################################################################

class MySpider(scrapy.Spider):

    name = 'finance_yahoo_most_active'

    start_urls = START_URLS

    custom_settings = {{

    }}

    header = (

        '{symbol}', '{name}', 'price',

        'change', 'p_change', 'volume',

        'avg_vol_3m', 'market_cap', 'pe_ratio'

    )

    csv_writer = csv.writer(sys.stdout, lineterminator='\n')

    csv_writer.writerow(header)

    # --------------------------------------------------------------------------

    # noinspection PyMethodOverriding

    def parse(self, response):

        texts = response.xpath('//*[@id="scr-res-table"]/div[1]/table/tbody/tr//text()').getall()

        n_rows = len(texts) // 9

        for i in range(n_rows):

            row = (

                texts[i*9 + 0], texts[i*9 + 1], texts[i*9 + 2],

                texts[i*9 + 3], texts[i*9 + 4], texts[i*9 + 5],

                texts[i*9 + 6], texts[i*9 + 7], texts[i*9 + 8],

            )

            row_info = {{

                '{symbol}': texts[i*9 + 0],

                '{name}': texts[i * 9 + 1],

                'price': texts[i * 9 + 2],

                'change': texts[i * 9 + 3],

                'p_change': texts[i * 9 + 4],

                'volume': texts[i * 9 + 5],

                'avg_vol_3m': texts[i * 9 + 6],

                'market_cap': texts[i * 9 + 7],

                'pe_ratio': texts[i * 9 + 8],

            }}

            self.csv_writer.writerow(row)

            yield row_info