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1. Using Python For Web Scraping Pdf
2. Using Python For Web Scraping
3. Using Python For Web Scraping Methods
4. Creating A Web Scraper Python
5. Using Python For Web Scraping Using

The need for extracting data from websites is increasing. When we are conducting data related projects such as price monitoring, business analytics or news aggregator, we would always need to record the data from websites. However, copying and pasting data line by line has been outdated. In this article, we would teach you how to become an “insider” in extracting data from websites, which is to do web scraping with python.

Step 0: Introduction

Web scraping with Python is easy due to the many useful libraries available A barebones installation isn’t enough for web scraping. One of the Python advantages is a large selection of libraries for web scraping. For this Python web scraping tutorial, we’ll be using three important libraries – BeautifulSoup v4, Pandas, and Selenium. Dec 27, 2020 Python Web scraping is nothing but the process of collecting data from the web. Web scraping in Python involves automating the process of fetching data from the web. In order to fetch the web data, all we need is the URL or the web address that we want to scrape from. The fetched data will be found in an unstructured form.

Web scraping is a technique that could help us transform HTML unstructured data into structured data in a spreadsheet or database. Besides using python to write codes, accessing website data with API or data extraction tools like Octoparse are other alternative options for web scraping.

For some big websites like Airbnb or Twitter, they would provide API for developers to access their data. API stands for Application Programming Interface, which is the access for two applications to communicate with each other. For most people, API is the most optimal approach to obtain data provided from the website themselves.

What you'll learn. Python,web scraping using python,python json parsing,scrapy. Live demonstration of web scraping using all latest python tricks from popular websites like myntra, cardekho, snapdeal, shopclues,yellowpages,bigbasket,grofers,espncricinfo and many more. Probably 80% of all the Python Web Scraping tutorials on the Internet uses the BeautifulSoup4 library as a simple tool for dealing with retrieved HTML in the most human-preferable way. Selectors, attributes, DOM-tree, and much more. The perfect choice for porting code to or from Javascript's Cheerio or jQuery.

However, most websites don’t have API services. Sometimes even if they provide API, the data you could get is not what you want. Therefore, writing a python script to build a web crawler becomes another powerful and flexible solution.

So why should we use python instead of other languages?

• Flexibility: As we know, websites update quickly. Not only the content but also the web structure would change frequently. Python is an easy-to-use language because it is dynamically imputable and highly productive. Therefore, people could change their code easily and keep up with the speed of web updates.
• Powerful: Python has a large collection of mature libraries. For example, requests, beautifulsoup4 could help us fetch URLs and pull out information from web pages. Selenium could help us avoid some anti-scraping techniques by giving web crawlers the ability to mimic human browsing behaviors. In addition, re, numpy and pandas could help us clean and process the data.

Now let's start our trip on web scraping using Python!

Step 1: Import Python library

In this tutorial, we would show you how to scrape reviews from Yelp. We will use two libraries: BeautifulSoup in bs4 and request in urllib. These two libraries are commonly used in building a web crawler with Python. The first step is to import these two libraries in Python so that we could use the functions in these libraries.

Step 2: Extract the HTML from web page

We need to extract reviews from “https://www.yelp.com/biz/milk-and-cream-cereal-bar-new-york?osq=Ice+Cream”. So first, let’s save the URL in a variable called URL. Then we could access the content on this webpage and save the HTML in “ourUrl” by using urlopen() function in request.

Then we apply BeautifulSoup to parse the page.

Now that we have the “soup”, which is the raw HTML for this website, we could use a function called prettify() to clean the raw data and print it to see the nested structure of HTML in the “soup”.

Step 3: Locate and scrape the reviews

Next, we should find the HTML reviews on this web page, extract them and store them. For each element in the web page, they would always have a unique HTML “ID”. To check their ID, we would need to INSPECT them on a web page.

After clicking 'Inspect element' (or 'Inspect', depends on different browsers), we could see the HTML of the reviews.

In this case, the reviews are located under the tag called ”p”. So we will first use the function called find_all() to find the parent node of these reviews. And then locate all elements with the tag “p” under the parent node in a loop. After finding all “p” elements, we would store them in an empty list called “review”.

Now we get all the reviews from that page. Let’s see how many reviews have we extracted.

Step 4: Clean the reviews

You must notice that there are still some useless texts such as “<p lang=’en’>” at the beginning of each review, “<br/>” in the middle of the reviews and “</p>” at the end of each review.

“<br/>” stands for a single line break. We don’t need any line break in the reviews so we will need to delete them. Also, “<p lang=’en’>” and “</p>” are the beginning and ending of the HTML and we also need to delete them.

Finally, we successfully get all the clean reviews with less than 20 lines of code.

Here is just a demo to scrape 20 reviews from Yelp. But in real cases, we may need to face a lot of other situations. For example, we will need steps like pagination to go to other pages and extract the rest reviews for this shop. Or we will also need to scrape down other information like reviewer name, reviewer location, review time, rating, check-in......

To implement the above operation and get more data, we would need to learn more functions and libraries such as selenium or regular expression. It would be interesting to spend more time drilling into the challenges in web scraping.

However, if you are looking for some simple ways to do web scraping, Octoparse could be your solution. Octoparse is a powerful web scraping tool which could help you easily obtain information from websites. Check out this tutorial about how to scrape reviews from Yelp with Octoparse. Feel free to contact us when you need a powerful web-scraping tool for your business or project!

Author: Jiahao Wu

日本語記事：PythonによるWebスクレイピングを解説
Webスクレイピングについての記事は 公式サイトでも読むことができます。
Artículo en español: Web Scraping con Python: Guía Paso a Paso
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Python is a high-level programming language that is used for web development, mobile application development, and also for scraping the web.

Python is considered as the finest programming language for web scraping because it can handle all the crawling processes smoothly. When you combine the capabilities of Python with the security of a web proxy, then you can perform all your scraping activities smoothly without the fear of IP banning.

In this article, you will understand how proxies are used for web scraping with Python. But, first, let’s understand the basics.

What is web scraping?

Web scraping is the method of extracting data from websites. Generally, web scraping is done either by using a HyperText Transfer Protocol (HTTP) request or with the help of a web browser.

Web scraping works by first crawling the URLs and then downloading the page data one by one. All the extracted data is stored in a spreadsheet. You save tons of time when you automate the process of copying and pasting data. You can easily extract data from thousands of URLs based on your requirement to stay ahead of your competitors.

Example of web scraping

An example of a web scraping would be to download a list of all pet parents in California. You can scrape a web directory that lists the name and email ids of people in California who own a pet. You can use web scraping software to do this task for you. The software will crawl all the required URLs and then extract the required data. The extracted data will be kept in a spreadsheet.

Why use a proxy for web scraping?

• Proxy lets you bypass any content related geo-restrictions because you can choose a location of your choice.
• You can place a high number of connection requests without getting banned.
• It increases the speed with which you request and copy data because any issues related to your ISP slowing down your internet speed is reduced.
• Your crawling program can smoothly run and download the data without the risk of getting blocked.

Now that you have understood the basics of web scraping and proxies. Let’s learn how you can perform web scraping using a proxy with the Python programming language.

Configure a proxy for web scraping with Python

Scraping using Python starts by sending an HTTP request. HTTP is based on a client/server model where your Python program (the client) sends a request to the server for seeing the contents of a page and the server returns with a response.

The basic method of sending an HTTP request is to open a socket and send the request manually:

***start of code***

import socket

HOST = ‘www.mysite.com’ # Server hostname or IP address

PORT = 80 # Port

client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

server_address = (HOST, PORT)

client_socket.connect(server_address)

request_header = b’GET / HTTP/1.0rnHost: www.mysite.comrnrn’

client_socket.sendall(request_header)

response = ”

while True:

recv = client_socket.recv(1024)

if not recv:

break

response += str(recv)

print(response)

client_socket.close()

***end of code***

You can also send HTTP requests in Python using built-in modules like urllib and urllib2. However, using these modules isn’t easy.

Hence, there is a third-option called Request, which is a simple HTTP library for Python.

You can easily configure proxies with Requests.

Here is the code to enable the use of proxy in Requests:

***start of code***

import requests

proxies = {

“http”: “http://10.XX.XX.10:8000”,

“https”: “http://10.XX.XX.10:8000”,

}

r = requests.get(“http://toscrape.com”, proxies=proxies)

***end of code***

In the proxies section, you have to specify the proxy address and the port address.

If you wish to include sessions and use a proxy at the same time, then you need to use the below code:

***start of code***

import requests

s = requests.Session()

s.proxies = {

“http”: “http://10.XX.XX.10:8000”,

“https”: “http://10.XX.XX.10:8000”,

}

r = s.get(“http://toscrape.com”)

***end of code***

Sometimes, you might need to create a new session and add a proxy. To do this, a session object should be created.

***start of code***

import requests

s = requests.Session()

s.proxies = {

“http”: “http://10.XX.XX.10:8000”,

“https”: “http://10.XX.XX.10:8000”,

}

r = s.get(“http://toscrape.com”)

Using Python For Web Scraping Pdf

***end of code***

However, using the Requests package might be slow because you can scrape just one URL with every request. Now, imagine you have to scrape 100 URLs then you will have to send the request 100 times only after the previous request is completed.

To solve this problem and to speed up the process, there is another package called the grequests that allows you to send multiple requests at the same time. Grequests is an asynchronous Python API that is widely used for building web apps.

Here is the code that explains the working of grequests. In this code, we are using an array to keep all the URLs to scrape in an array. Let’s suppose we have to scrape 100 URLs. We will keep all the 100 URLs in an array and use the grequest package specifying the batch length to 10. This will require sending just 10 requests to complete the scraping of 100 URLs instead of sending 100 requests.

***start of code***

import grequests

BATCH_LENGTH = 10

# An array having the 100 URLs for scraping

urls = […]

# results will be stored in this empty results array

results = []

while urls:

# this is the first batch of 10 URLs

batch = urls[:BATCH_LENGTH]

# create a set of unsent Requests

rs = (grequests.get(url) for url in batch)

# send all the requests at the same time

batch_results = grequests.map(rs)

# appending results to our main results array

results += batch_results

# removing fetched URLs from urls

Using Python For Web Scraping

urls = urls[BATCH_LENGTH:]

Using Python For Web Scraping Methods

print(results)

# [<Response [200]>, <Response [200]>, …, <Response [200]>, <Response [200]>]

Creating A Web Scraper Python

***end of code***

Final Thoughts

Using Python For Web Scraping Using

Web scraping is a necessity for several businesses, especially eCommerce websites. Real-time data needs to be captured from a variety of sources to make better business decisions at the right time. Python offers different frameworks and libraries that make web scraping easy. You can extract data fast and efficiently. Moreover, it is crucial to use a proxy to hide your machine’s IP address to avoid blacklisting. Python along with a secure proxy should be the base for successful web scraping.