Consuming data with API | Web Scraping: Python, JavaScript

我们使用 Python 和 Django 构建了一个应用程序，并使用它来转换 API 发送的数据，因此我们的应用程序充当客户端并使用数据。

Web 抓取是以自动化方式收集结构化 Web 数据的过程。它也称为网络数据提取。网络抓取的一些主要用例包括价格监控、价格情报、新闻监控、潜在客户生成和市场研究等。

通常，希望利用大量公开可用的 Web 数据做出更明智决策的个人和企业使用 Web 数据提取。

如果您曾经从网站复制和粘贴信息，那么您执行的功能与任何网络抓取工具相同，只是在微观的手动范围内。与手动提取数据的平凡、令人麻木的过程不同，网络抓取使用智能自动化从互联网看似无穷无尽的前沿检索数百、数百万甚至数十亿个数据点。

Web 数据提取（也被广泛称为数据抓取）具有广泛的应用范围。数据抓取工具可以帮助您快速准确地自动化从其他网站提取信息的过程。它还可以确保您提取的数据井井有条，便于分析和用于其他项目。

在电子商务领域，网络数据抓取被广泛用于竞争对手的价格监控。这是品牌检查竞争对手产品和服务定价的唯一实用方法，使他们能够微调自己的价格策略并保持领先地位。它还被用作制造商的工具，以确保零售商遵守其产品的定价指南。市场研究机构和分析师依靠网络数据提取，通过跟踪在线产品评论、新闻文章和反馈来衡量消费者情绪。

金融界有大量的数据提取应用程序。数据抓取工具用于从新闻故事中提取洞察力，使用这些信息来指导投资策略。同样，研究人员和分析师依靠数据提取来评估公司的财务状况。保险和金融服务公司可以挖掘从网络上抓取的大量替代数据，为他们的客户设计新产品和政策。

Published 06/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 52 lectures (3h 50m) | Size: 1.17 GB

Consume data using API and Scrape data from the web

What you’ll learn

• Scrape data from a website and save extracted data to a text file
• Scrape YouTube data into Google Sheets
• Scrape and email amazon data
• Build an application to consume data with API
• Build scrapers with Python
• Build scrapers with JavaScript and API

Requirements
Basic knowledge of Python advised
Basic knowledge of JavaScript advised.

Description

We build an application with Python and Django and use it to transform the data that an API sends, so our application is acting as a client and consuming the data.

Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.

In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.

If you’ve ever copied and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.

Web data extraction – also widely known as data scraping – has a huge range of applications. A data scraping tool can help you automate the process of extracting information from other websites, quickly and accurately. It can also make sure the data you’ve extracted is neatly organized, making it easier to analyze and use for other projects.

In the world of e-commerce, web data scraping is widely used for competitor price monitoring. It’s the only practical way for brands to check the pricing of their competitors’ products and services, allowing them to fine-tune their own price strategies and stay ahead of the game. It’s also used as a tool for manufacturers to ensure retailers are compliant with pricing guidelines for their products. Market research organizations and analysts depend on web data extraction to gauge consumer sentiment by keeping track of online product reviews, news articles, and feedback.

There’s a vast array of applications for data extraction in the financial world. Data scraping tools are used to extract insight from news stories, using this information to guide investment strategies. Similarly, researchers and analysts depend on data extraction to assess the financial health of companies. Insurance and financial services companies can mine a rich seam of alternative data scraped from the web to design new products and policies for their customers.