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A rundown of what you’ll need for Data scraping, what you might need When Scraping with Python & software, and what you don’t need.

Data scraping is the great shortcut for anyone looking for a large amount of data from specific websites. The term ‘data scraping’ encompasses the use of a ‘爬虫,"它可以从一个网页导航到另一个网页,甚至在一个网页中导航到另一个子网页;'蜘蛛在网站所有者认为法律允许的范围内提取数据",以及 "在网站所有者认为法律允许的范围内提取数据"。刮板这可能指的是让机器为你收集和存储数据的整个功能。

By data, we do not mean a html file or two: scrapers can extract millions of data points in a short amount of time. Even better, they can be instructed to extract specifically the type of data that is being sought after. This is, in many cases, how such massive datasets are built: no human can collect data at a fraction of the rate and efficiency that a machine can. This is the essence, is what data scraping is when people talk about data scraping.

But first, know that from any single personal computer, there is a steadfast limit to data scraping capabilities. The computational power needed to scrape data at an industrial level requires servers and data centers. This is also why many of the services listed below have pricing options. With the ones that don’t, the barrier comes from the

Data Scraping Protocols

No matter what tools you use for data scraping, there are customary protocols websites put in place. Failing to abide by these protocols can have a few different negative results. If the site is small enough, scraping at a high volume could impact their server could come across as a server attack, which could have legal ramifications. Medium-sized or larger-sized websites would be less susceptible to this, but there are other consequences instead.

Your IP address could get banned pretty quickly for failing to acknowledge the rules laid out by the websites. If the websites, has an API, then using the API is encouraged. But scraping is more about extracting raw data from webpages, so using an API technically collects data using an entirely different method. In any case, there are restrictions in what you can scrape, how often you can scrape, and the size of what you can scrape. As you learn about data scraping you may become familiar with the terms workers or spiders (defined later). These will need to follow those guidelines, but where can those guidelines be found?


Wherever information exists on a network of webpages under a single domain, the larger website usually has a landing page with instructions on what data scrapers can and cannot do on their websites. This information can commonly be found under the URL of the homepage, followed by a ‘/robots.txt’.

例如,以限制比同行多而著称的社交媒体网络 Facebook,在其网站上标记了所有被禁止的行为。 facebook.com/robots.txt.下面是直接从他们自己的 "robots.txt "子页面上截取的一些他们不允许您搜刮的内容。这里也是检查网站是否包含网站地图的好地方。有的网站有,有的网站没有,但如果有,搜刮者的工作就容易多了。

facebook 机器人

Note that this doesn’t mean scraping this type of data from this particular website is impossible (or from any website, for that matter). It simply means that the administrators have made it immensely more difficult in a number of ways: easier to get IP banned, harder to use simple, popular web scraping tools, among other barriers. Of course, these things can always be circumvented to some extent with some programming chops.

Scraping with Python

无论如何,我想下面的前两个软件包已经融入了 Python 的大多数网络搜索工作中。它们配合得天衣无缝,都为网络搜索提供了宝贵的任务。


Beautiful Soup(可通过 pip 或 conda 安装为 "bs4")是一个非常有用的数据搜刮软件包。它之所以有用,是因为它可以轻松地处理刮取的数据,从典型的网页中提取出需要的内容,剔除不需要的内容。


因此,"美丽汤 "可以告诉搜索工具从网页中提取哪些特定的数据点,例如,表格中某一列的单元格内的条目。

对于那些有 Python 使用经验并想根据自己的喜好用 Beautiful Soup 编写脚本的人来说,其他地方有更好的教程。(插入超链接)。不过,对于 HTML/CSS/JS 网页上的常见提取,也有许多脚本是公开可用的。


If you already use Python, chances are you already have ‘requests,’ installed. If not, do so immediately because its usefulness is remarkable, even outside the scope of this subject. In short, requests allows you to interact with web pages in a number of useful and flexible ways. Requests can interact with webpages, crawl an entire sitemap of a certain webpage, and even log in when prompted, as Beautiful Soup extracts the necessary data. It’s the script’s tour guide to the internet: taking it where it needs to go, granting it access to places it could not get to on its own, and moving from place to place as needed, providing information on the site and sitemap all the while.


该包裹在功能上与 "美丽汤 "类似,都具有数据搜刮功能。简单地说,它能像 Beautiful Soup 一样,将刮擦器导向它想要的数据。它的优势在于可以自动浏览 XPath 和其他常见的 CSS 容器,因为这些容器通常会隐藏数据。

Beautiful Soup 也可以做到这一点,不过,在编写 Beautiful Soup 脚本时,必须让 Beautiful Soup 知道在哪里可以找到隐藏在 Xpath 中的数据。

您只需在网页上的 Xpath 中找到一个数据示例,但这需要右键单击页面,单击 "检查元素",然后查看 div,以便找到隐藏在其中的数据示例。下面是我写的一个脚本示例,实际上我必须使用 Selenium(稍后讨论):

My sample to use Soup - find element by xpath

td "是数据隐藏在 Xpath "tr "下的地方。一旦我找到了,剩下的就交给汤了。

不过,Parcel 可以跳过首先找到元素的步骤,因为它可以自己找到元素,如下所示 的部分.看起来必然的情况是,这个软件包比 Beautiful Soup 更笨重,可能会导致运行速度变慢,但它提供了更多的功能。

虽然《美丽汤》也相当简单,但它可能更适合初学者。两个都试试,看看哪个适合你。但对于初学者来说 这里是 一个使用请求和 Parcel 的公开爬虫演示。

更好的办法是 这里是 网站上有许多使用 Parcel 脚本的链接,并对 Parcel 进行了解释。由于 "美汤 "已经存在了很长时间,因此没有像这里一样有那么多现成的分配脚本。


JSON 作为 Python 软件包可能是保存受惊数据的最有效方法。无处不在的 "pandas "包提供了相同的功能,在此也值得一提。使用内置的 "re "包,脚本需要将刮擦到的数据写入一个文件,并将该文件保存在计算机上的某个位置。


Selenium is clunky and inefficient but can serve an important purpose if a web page is written in a way that the programs previously mentioned don’t understand. Selenium isn’t a scraper of any kind, but can be used for navigating a scraper to where it needs to go in a pinch. Selenium is a web browser condensed into a Python package.

它需要一个驱动程序,通常是一个 ChromeDriver,它将使用谷歌 Chrome 浏览器。这意味着,当您使用硒运行脚本时,您选择的浏览器会自动打开,它可以自行点击页面的某些部分、在某些表单中输入按键等。

它看起来很酷,但对于网络搜刮来说,它的主要目的是将网络搜刮器导航到网页上所需数据所在的特定区域。这是一种 "如果全部失败,那么其他全部失败 "的机制,因为上述软件包可以处理绝大多数网页。


许多这类工具的问题是使用起来很麻烦。Scrapy 需要 Visual Studio 14.0,你可以通过下载 Visual Studio Build Tools 获得。像这样的扩展要求会增加遇到困难的几率。

编程环境、每个软件包以及您使用的机器或服务器的具体情况都会影响兼容性。不过请注意,Mac 和 Linux 在使用 PyPi 下载 Scrapy 时不会有问题。如果你不熟悉这个过程,下面是你的终端应该显示的样子:

安装 Scrapy


Scraping with software

有几种不同类型的 data scraping software tools 有的需要一点编程知识,有的则完全不需要。有些需要一点编程知识,有些则完全不需要。

有些工具比其他工具具有更强的功能(例如,扫描多媒体,即 PDF、图像、音频和视频文件)。下面我们就来介绍几种比较流行的工具。


免费数据挖掘网站 Orange 发现 这里 以及 Github 上的工具,是我个人最喜欢的从 Twitter 等特定网站抓取内容的工具。




Using Orange for data scraping comes with a few benefits. First, storing, processing, and saving the data becomes incredibly simple. Short and informative trials are publicly available with instructions on how to do this. Software must abide by the terms of conditions of third parties, which is a burden when it comes to web scraping. With scraping Twitter, for example, you will need an API key — find out how to get one 这里 (anyone can get one), but using an API limits the amount of data allows for scraping over a certain amount of time. An advantage, on the other hand, is that Orange allows their users to implement any script they’d like into the workflow, so everything mentioned above can be incorporated into Orange.





Octoparse is a mostly-free web scraping program available to every major OS. Since it offers premium packages to industries that can afford them, the software is excellent at what it can do. Unlike other software that’s free for a very very limited amount of scraping power, Octoparse offers a generous package to it’s free users: unlimited pages per crawl, 10 crawlers at a time, and 10,000 records per export. The number of records is the make-or-break limit to the free plan: depending on the project, 10,000 entries could be either more than enough or nowhere near enough.

Regardless, its as effective as the Python packages listed above, and perhaps even more so. Their 产品概述 并不夸大其能力。只是要注意其局限性。

Paid Scrapers

There is web scraping software that’s easy to find, but not easy to stomach paying for. These programs are suitable for businesses. The pricing is exorbitant for an individual project, but you do get what you pay for; Import.io, Mozenda, and Helium all get rave reviews, but all cost a pretty penny. Since I have no experience with them, I will not cover them, but the choice is yours. Just know they are out there.

Beware: 100% Free Data Scraping software almost never do as promised!

The most professional, iciest, and competent methods of data scraping is to do t using hand-programmed scripts using a machine with a server or GPU with high computational power. There are hundreds of data scraping programs out there, just see a quick search on Sourceforge:

If the descritis programs like these bast capabilities that seem unrealistic, chances are they are unrealistic. Always check if there are healthy, recent and recent updates, whether the reviews back up what the developers say the software can do, and that there are a health number of weekly downloads. These programs that don’t pass such criteria are probably not malicious, but they may just turn out to be junk.

So, there are two routes to take with regard to data scraping, crudely speaking. The programming, scripting route, which offers more freedoms, more personalization, and more customization. Then there’s software routes, which offers ease-of-use and extra computing power. The viability of either option depends on the amount of programming one wishes to do versus the amount of paying out of pocket one wishes to do. As discussed, this is inevitably the case with web scraping software that actually works.




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