Python has good options to scrape the web. The best one with a framework is scrapy. It can be a little tricky for beginners, so here is a little help. Install python above 3.5 (lower ones till 2.7 will work). Create a environment in conda ( I did this). Install scrapy at a location and run in from there. BeautifulSoup is a Python library that is used to pull data of HTML and XML files. It is mainly designed for web scrapping. It works with the parser to provide a natural way of navigating, searching, and modifying the parse tree. The latest version of BeautifulSoup is 4.8.1. How to scrape a website that requires login with Python. I’ve recently had to perform some web scraping from a site that required login. It wasn’t very straight forward as I expected so I’ve decided to write a tutorial for it. For this tutorial we will scrape a list of projects from our bitbucket account. Loading Web Pages with 'request' The requests module allows you to send HTTP.

What is Web Scraping?

Web Scraping is a technique to extract a large amount of data from several websites. The term 'scraping' refers to obtaining the information from another source (webpages) and saving it into a local file. For example: Suppose you are working on a project called 'Phone comparing website,' where you require the price of mobile phones, ratings, and model names to make comparisons between the different mobile phones. If you collect these details by checking various sites, it will take much time. In that case, web scrapping plays an important role where by writing a few lines of code you can get the desired results.

Web Scrapping extracts the data from websites in the unstructured format. It helps to collect these unstructured data and convert it in a structured form.

Startups prefer web scrapping because it is a cheap and effective way to get a large amount of data without any partnership with the data selling company.

Is Web Scrapping legal?

Here the question arises whether the web scrapping is legal or not. The answer is that some sites allow it when used legally. Web scraping is just a tool you can use it in the right way or wrong way.

Web scrapping is illegal if someone tries to scrap the nonpublic data. Nonpublic data is not reachable to everyone; if you try to extract such data then it is a violation of the legal term.

There are several tools available to scrap data from websites, such as:

• Scrapping-bot
• Scrapper API
• Octoparse
• Import.io
• Webhose.io
• Dexi.io
• Outwit
• Diffbot
• Content Grabber
• Mozenda
• Web Scrapper Chrome Extension

Why Web Scrapping?

As we have discussed above, web scrapping is used to extract the data from websites. But we should know how to use that raw data. That raw data can be used in various fields. Let's have a look at the usage of web scrapping:

• Dynamic Price Monitoring

It is widely used to collect data from several online shopping sites and compare the prices of products and make profitable pricing decisions. Price monitoring using web scrapped data gives the ability to the companies to know the market condition and facilitate dynamic pricing. It ensures the companies they always outrank others.

• Market Research

eb Scrapping is perfectly appropriate for market trend analysis. It is gaining insights into a particular market. The large organization requires a great deal of data, and web scrapping provides the data with a guaranteed level of reliability and accuracy.

• Email Gathering

Many companies use personals e-mail data for email marketing. They can target the specific audience for their marketing.

• News and Content Monitoring

A single news cycle can create an outstanding effect or a genuine threat to your business. If your company depends on the news analysis of an organization, it frequently appears in the news. So web scraping provides the ultimate solution to monitoring and parsing the most critical stories. News articles and social media platform can directly influence the stock market.

• Social Media Scrapping

Web Scrapping plays an essential role in extracting data from social media websites such as Twitter, Facebook, and Instagram, to find the trending topics.

• Research and Development

The large set of data such as general information, statistics, and temperature is scrapped from websites, which is analyzed and used to carry out surveys or research and development.

Why use Python for Web Scrapping?

There are other popular programming languages, but why we choose the Python over other programming languages for web scraping? Below we are describing a list of Python's features that make the most useful programming language for web scrapping.

• Dynamically Typed

In Python, we don't need to define data types for variables; we can directly use the variable wherever it requires. It saves time and makes a task faster. Python defines its classes to identify the data type of variable.

• Vast collection of libraries

Python comes with an extensive range of libraries such as NumPy, Matplotlib, Pandas, Scipy, etc., that provide flexibility to work with various purposes. It is suited for almost every emerging field and also for web scrapping for extracting data and do manipulation.

• Less Code

The purpose of the web scrapping is to save time. But what if you spend more time in writing the code? That's why we use Python, as it can perform a task in a few lines of code.

• Open-Source Community

Python is open-source, which means it is freely available for everyone. It has one of the biggest communities across the world where you can seek help if you get stuck anywhere in Python code.

The basics of web scraping

The web scrapping consists of two parts: a web crawler and a web scraper. In simple words, the web crawler is a horse, and the scrapper is the chariot. The crawler leads the scrapper and extracts the requested data. Let's understand about these two components of web scrapping:

• The crawler

A web crawler is generally called a 'spider.' It is an artificial intelligence technology that browses the internet to index and searches for the content by given links. It searches for the relevant information asked by the programmer.

The scrapper

A web scraper is a dedicated tool that is designed to extract the data from several websites quickly and effectively. Web scrappers vary widely in design and complexity, depending on the projects.

How does Web Scrapping work?

These are the following steps to perform web scraping. Let's understand the working of web scraping.

Step -1: Find the URL that you want to scrape

First, you should understand the requirement of data according to your project. A webpage or website contains a large amount of information. That's why scrap only relevant information. In simple words, the developer should be familiar with the data requirement.

Step - 2: Inspecting the Page

The data is extracted in raw HTML format, which must be carefully parsed and reduce the noise from the raw data. In some cases, data can be simple as name and address or as complex as high dimensional weather and stock market data.

Winutils exe hadoop for maclastevil. Step - 3: Write the code

Web Scraping In Python Tutorial

Write a code to extract the information, provide relevant information, and run the code.

Step - 4: Store the data in the file

Store that information in required csv, xml, JSON file format.

Getting Started with Web Scrapping

Python has a vast collection of libraries and also provides a very useful library for web scrapping. Let's understand the required library for Python.

Library used for web scrapping

• Selenium- Selenium is an open-source automated testing library. It is used to check browser activities. To install this library, type the following command in your terminal.

Note - It is good to use the PyCharm IDE.

• Pandas

Pandas library is used for data manipulation and analysis. It is used to extract the data and store it in the desired format.

• BeautifulSoup

BeautifulSoup is a Python library that is used to pull data of HTML and XML files. It is mainly designed for web scrapping. It works with the parser to provide a natural way of navigating, searching, and modifying the parse tree. The latest version of BeautifulSoup is 4.8.1.

Let's understand the BeautifulSoup library in detail.

Installation of BeautifulSoup

You can install BeautifulSoup by typing the following command:

Installing a parser

BeautifulSoup supports HTML parser and several third-party Python parsers. You can install any of them according to your dependency. The list of BeautifulSoup's parsers is the following:

Parser	Typical usage
Python's html.parser	BeautifulSoup(markup,'html.parser')
lxml's HTML parser	BeautifulSoup(markup,'lxml')
lxml's XML parser	BeautifulSoup(markup,'lxml-xml')
Html5lib	BeautifulSoup(markup,'html5lib')

We recommend you to install html5lib parser because it is much suitable for the newer version of Python, or you can install lxml parser.

Type the following command in your terminal:

BeautifulSoup is used to transform a complex HTML document into a complex tree of Python objects. But there are a few essential types object which are mostly used: Nascar 2016 camping world truck series.

• Tag

A Tag object corresponds to an XML or HTML original document.

Output:

Tag contains lot of attributes and methods, but most important features of a tag are name and attribute.

• Name

Every tag has a name, accessible as .name:

• Attributes

A tag may have any number of attributes. The tag <b id = 'boldest'> has an attribute 'id' whose value is 'boldest'. We can access a tag's attributes by treating the tag as dictionary.

We can add, remove, and modify a tag's attributes. It can be done by using tag as dictionary.

• Multi-valued Attributes

In HTML5, there are some attributes that can have multiple values. The class (consists more than one css) is the most common multivalued attributes. Other attributes are rel, rev, accept-charset, headers, and accesskey.

• NavigableString

A string in BeautifulSoup refers text within a tag. BeautifulSoup uses the NavigableString class to contain these bits of text.

A string is immutable means it can't be edited. But it can be replaced with another string using replace_with().

In some cases, if you want to use a NavigableString outside the BeautifulSoup, the unicode() helps it to turn into normal Python Unicode string.

• BeautifulSoup object

The BeautifulSoup object represents the complete parsed document as a whole. In many cases, we can use it as a Tag object. It means it supports most of the methods described in navigating the tree and searching the tree.

Output:

Web Scrapping Example:

Let's take an example to understand the scrapping practically by extracting the data from the webpage and inspecting the whole page.

First, open your favorite page on Wikipedia and inspect the whole page, and before extracting data from the webpage, you should ensure your requirement. Consider the following code:

Output:

In the following lines of code, we are extracting all headings of a webpage by class name. Here front-end knowledge plays an essential role in inspecting the webpage.

Output:

In the above code, we imported the bs4 and requested the library. In the third line, we created a res object to send a request to the webpage. As you can observe that we have extracted all heading from the webpage.

Webpage of Wikipedia Learning

Clan wars: goblin forestgamefort. Let's understand another example; we will make a GET request to the URL and create a parse Tree object (soup) with the use of BeautifulSoup and Python built-in 'html5lib' parser.

Here we will scrap the webpage of given link (https://www.javatpoint.com/). Consider the following code:

The above code will display the all html code of javatpoint homepage.

Using the BeautifulSoup object, i.e. soup, we can collect the required data table. Let's print some interesting information using the soup object:

• Let's print the title of the web page.

Output: It will give an output as follow:

• In the above output, the HTML tag is included with the title. If you want text without tag, you can use the following code:

Output: It will give an output as follow:

• We can get the entire link on the page along with its attributes, such as href, title, and its inner Text. Consider the following code:

Output: It will print all links along with its attributes. Here we display a few of them:

Demo: Scraping Data from Flipkart Website

In this example, we will scrap the mobile phone prices, ratings, and model name from Flipkart, which is one of the popular e-commerce websites. Following are the prerequisites to accomplish this task:

Prerequisites:

• Python 2.x or Python 3.x with Selenium, BeautifulSoup, Pandas libraries installed.
• Google - chrome browser
• Scrapping Parser such as html.parser, xlml, etc.

Step - 1: Find the desired URL to scrap

The initial step is to find the URL that you want to scrap. Here we are extracting mobile phone details from the flipkart. The URL of this page is https://www.flipkart.com/search?q=iphones&otracker=search&otracker1=search&marketplace=FLIPKART&as-show=on&as=off.

Step -2: Inspecting the page

It is necessary to inspect the page carefully because the data is usually contained within the tags. So we need to inspect to select the desired tag. To inspect the page, right-click on the element and click 'inspect'.

Step - 3: Find the data for extracting

Extract the Price, Name, and Rating, which are contained in the 'div' tag, respectively.

Step - 4: Write the Code

Output:

We scrapped the details of the iPhone and saved those details in the CSV file as you can see in the output. In the above code, we put a comment on the few lines of code for testing purpose. You can remove those comments and observe the output.

In this tutorial, we have discussed all basic concepts of web scrapping and described the sample scrapping from the leading online ecommerce site flipkart.

I’ve recently had to perform some web scraping from a site that required login.It wasn’t very straight forward as I expected so I’ve decided to write a tutorial for it.

For this tutorial we will scrape a list of projects from our bitbucket account.

The code from this tutorial can be found on my Github.

We will perform the following steps:

1. Extract the details that we need for the login
2. Perform login to the site
3. Scrape the required data

Web Scraping In Python Datacamp Github

For this tutorial, I’ve used the following packages (can be found in the requirements.txt):

Open the login page

Go to the following page “bitbucket.org/account/signin” .You will see the following page (perform logout in case you’re already logged in)

Check the details that we need to extract in order to login

In this section we will build a dictionary that will hold our details for performing login:

1. Right click on the “Username or email” field and select “inspect element”. We will use the value of the “name” attribue for this input which is “username”. “username” will be the key and our user name / email will be the value (on other sites this might be “email”, “user_name”, “login”, etc.).
2. Right click on the “Password” field and select “inspect element”. In the script we will need to use the value of the “name” attribue for this input which is “password”. “password” will be the key in the dictionary and our password will be the value (on other sites this might be “user_password”, “login_password”, “pwd”, etc.).
3. In the page source, search for a hidden input tag called “csrfmiddlewaretoken”. “csrfmiddlewaretoken” will be the key and value will be the hidden input value (on other sites this might be a hidden input with the name “csrf_token”, “authentication_token”, etc.). For example “Vy00PE3Ra6aISwKBrPn72SFml00IcUV8”.

Web Scraping In Python Libraries

We will end up with a dict that will look like this:

Keep in mind that this is the specific case for this site. While this login form is simple, other sites might require us to check the request log of the browser and find the relevant keys and values that we should use for the login step.

For this script we will only need to import the following:

First, we would like to create our session object. This object will allow us to persist the login session across all our requests.

Web Scraping In Python Part 3

Second, we would like to extract the csrf token from the web page, this token is used during login.For this example we are using lxml and xpath, we could have used regular expression or any other method that will extract this data.

** More about xpath and lxml can be found here.

Next, we would like to perform the login phase.In this phase, we send a POST request to the login url. We use the payload that we created in the previous step as the data.We also use a header for the request and add a referer key to it for the same url.

Now, that we were able to successfully login, we will perform the actual scraping from bitbucket dashboard page

In order to test this, let’s scrape the list of projects from the bitbucket dashboard page.Again, we will use xpath to find the target elements and print out the results. If everything went OK, the output should be the list of buckets / project that are in your bitbucket account.

You can also validate the requests results by checking the returned status code from each request.It won’t always let you know that the login phase was successful but it can be used as an indicator.

for example:

That’s it.

Full code sample can be found on Github.