Python Tutorial

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Python Conditional Statements

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Python Loops

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Python Arrays

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Python Strings

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Python Built-in Data Structure

Python Lists Python Tuples Python Lists vs Tuples Python Dictionary Python Sets

Python Functions

Python Function Python min() function Python max() function Python User-define Functions Python Built-in Functions Python Recursion Anonymous/Lambda Function in Python apply() function in python Python lambda() Function

Python File Handling

Python File Handling Python Read CSV Python Write CSV Python Read Excel Python Write Excel Python Read Text File Python Write Text File Read JSON File in Python

Python Exception Handling

Python Exception Handling Python Errors and exceptions Python Assert

Python OOPs Concept

OOPs Concepts in Python Classes & Objects in Python Inheritance in Python Polymorphism in Python Python Encapsulation Python Constructor Python Super function Python Static Method Static Variables in Python Abstraction in Python

Python Iterators

Iterators in Python Yield Statement In Python Python Yield vs Return

Python Generators

Python Generator

Python Decorators

Python Decorator

Python Functions and Methods

Python Built-in Functions Python String Methods Python List Methods Python Dictionary Methods Python Tuple Methods Python Set Methods

Python Modules

Python Modules Python Datetime Module Python Math Module Python Import Module Python Time ModulePython Random Module Python Calendar Module CSV Module in Python Python Subprocess Module

Python MySQL

Python MySQL Python MySQL Client Update Operation Delete Operation Database Connection Creating new Database using Python MySQL Creating Tables Performing Transactions

Python MongoDB

Python MongoDB

Python SQLite

Python SQLite

Python Data Structure Implementation

Python Stack Python Queue Python Linked List Python Hash Table Python Graph

Python Advance Topics

Speech Recognition in Python Face Recognition in Python Python Linear regression Python Rest API Python Command Line Arguments Python JSON Python Subprocess Python Virtual Environment Type Casting in Python Python Collections Python Attributes Python Commands Python Data Visualization Python Debugger Python DefaultDict Python Enumerate

Python 2

What is Python 2

Python 3

Anaconda in Python 3 Anaconda python 3 installation for windows 10 List Comprehension in Python3

How to

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Sorting

Python Sort List Sort Dictionary in Python Python sort() function Python Bubble Sort

Programs

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Misc

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Scraping data in python

Data scraping is a technique in which one program extracts a set of data from the output of another program. Web scraping is the most common application of this technique. A website contains all of the information you seek. However, you may not always have the time or energy to read every page and take detailed notes. You can obtain all the information you require with just one tool.

There are three types of Data Scraping they are:

  1. Report mining: Website data is collected by programs and incorporated into user-generated reports. It functions similarly to printing a page, except that the printer is the user's report.
  2. Screen scraping: This tool transfers data from legacy machines to modern machines.
  3. Web scraping: Tools collect data from websites and convert it into customizable user reports.

People frequently confuse data scraping and web crawling, but the two are different. Data scraping tools ignore most code, and those tools pay no attention to programmer requests. A web crawler closely examines the code on the page, and if the programmer includes the appropriate tag, the device may skip over pages entirely. These results assist sites such as Google determine what to include on search results pages.

A screen scraper can come in handy if you're working with an old computer that won't work with a new system. Instead of recording or updating the old piece, take inspiration from it and begin again with modern technology. Data scraping can tell you how much your product should cost and how many people are interested in purchasing it. A competitive company publishes all of a product's colors, sizes, and prices online.

Web scraping using python

Web scraping is a technique for obtaining large amounts of information from multiple websites. "Scraping" refers to obtaining information from another source and saving it to a local file. Assume you're working on a project called "Phone comparing website," and you need to compare mobile phone prices, ratings, and model names. Visiting various websites will take a long time to gather these details. Web scraping is handy in this case, allowing you to achieve the desired results with just a few lines of code.

Web scraping is the extraction of unstructured data from websites. It is beneficial to collect unstructured data and convert it to structured data.

Start-ups prefer web scraping because it is a low-cost and effective method of obtaining large amounts of data without forming a partnership with a data-selling company.

Why we use web scraping?

As previously stated, web scraping extracts data from websites. That raw data can be used in a variety of applications. But we need to know how to put that raw data to use. Let us now examine how web scraping is used.

1) Dynamic Price Monitoring 

It is frequently used to gather information from various online shopping sites, compare product prices, and make profitable pricing decisions. Price monitoring with web scraped data allows businesses to understand market conditions and implement dynamic pricing. It ensures that the businesses always outperform the competition.

2) Market Research

Scrapping is a great way to analyze market trends. It is gaining knowledge of a specific market. A large organization necessitates a large amount of data, and web scraping ensures the reliability and accuracy of the data.

3) Email Gathering

For email marketing, many businesses use personal email addresses. They can     market to a specific group of people.

4) News and Content Monitoring

A single news cycle can significantly impact your company or pose a genuine threat. News articles and social media platforms can immediately impact the stock market. If your company relies on a news analysis organization, it will frequently appear in the news. As a result, web scraping is the most effective method for tracking and parsing the most important stories.

5) Social Media Scrapping

To identify trending topics, web scraping is necessary for extracting data from social media websites such as Twitter, Facebook, and Instagram.

6) Research and Development

Many data types, such as general information, statistics, and temperature, are scraped from websites and analyzed before being used in surveys or R&D. There are other popular programming languages, but why did we choose Python over them for web scraping? Below is a list of Python features that make it the best programming language for web scraping.

7) Dynamically Typed

In Python, we don't need to define data types for variables; we can use the variable wherever it is needed. It saves time and speeds up a task. Python defines classes to identify variable data types.

8) A vast collection of libraries

Python includes many libraries, such as NumPy, Matplotlib, Pandas, Scipy, and  others, that allow it to be used for various purposes. It is appropriate for almost every emerging field and web scraping for data extraction and manipulation.

Web scraping comprises two components: a web crawler and a web scraper. Simply put, a web crawler is a horse, and a scrapper is a chariot. The scrapper is led by the crawler, which extracts the requested data. Let's learn about these two aspects of web scraping. A web crawler is commonly referred to as a "spider." It is an artificial intelligence technology that uses links to search the internet for content. It looks for the information requested by the programmer.

A web scraper is a dedicated tool designed to extract data from multiple websites quickly and effectively. Web scraper design and complexity vary greatly depending on the project. To begin, import 'urllib3', 'facebook', and requests if they are not already present. If not, you should get these libraries. Define a variable token and set its value to "User Access Token" from above.

Steps for web scraping

Step 1: First, you should understand the data requirements for your project. A website or webpage contains much information. That is why only relevant information is scraped. In other words, the developer should be aware of the data requirements.

Step 2: To reduce noise in the raw data, the data is extracted in raw HTML format, which must be parsed carefully. Sometimes, data can be as simple as a name and address or as complex as high-dimensional weather and stock market data.

Step 3: Make a program to extract the information, provide relevant information, and run it.

Step 4: Save the data in the appropriate CSV, XML, or JSON format.

Installing Beautifulsoup library

BeautifulSoup is a data extraction tool for HTML and XML files. It includes a parse tree and functions for navigating, searching, and modifying it.

Beautifulsoup necessitates the installation of the pip package on Windows, Linux, or any other operating system. PIP Installation - Windows || Linux walks you through installing pip on your operating system. Run the following command in the terminal.

pip install beautifulsoup4

We must first understand the page’s structure before extracting any information from its HTML. This is required to retrieve the desired data from the entire page. This is accomplished by right-clicking the page to be scraped and selecting inspect element.

Example:

# importing the libraries  
from bs4 import BeautifulSoup  
import requests  
url="https://www.facebook.com/"  
Make a GET request to fetch the raw HTML content  
html_content = requests.get(url).text  
# Parse the html content  
soup = BeautifulSoup(html_content, "html5lib")  
print(soup.prettify()) # print the parsed data of html 

OUTPUT:

The above code will display all HTML code from the Facebook homepage.

Scraping data in python

How is data scraping mitigated

Several things can be done to limit bot attempts. The bot's attempts will be visible to the visitor. The following are some methods for reducing data scraping.

Decrease the limit

This method allows users to prevent scraping by limiting the number of times a user or scraper can operate on the website. We can, for example, limit the number of searches conducted per second from a specific IP address. This renders scraping useless. Furthermore, we can use a ReCaptcha entry if any task is completed faster than real-world user speed.

Detect any theft activity

Searching for multiple website pages, similar requests from the same IP address, an unusual number of searches, and so on are all examples of theft activities.

Miscellaneous indicators

Other indicators consider how quickly a user fills out a form. Javascript can identify users, HTTP headers, orders, and so on. For example, if we repeatedly receive the same request from a user, the button clicked on the form is in the same location, and the screen sizes are the same, it is most likely a scraper bot.