Zachary Palmer

Zachary Palmer

1560786280

Web Scraping with Python and BeautifulSoup

There is more information on the Internet than any human can absorb in a lifetime. What you need is not access to that information, but a scalable way to collect, organize, and analyze it.

You need web scraping.

Web scraping automatically extracts data and presents it in a format you can easily make sense of. In this tutorial, we’ll focus on its applications in the financial market, but web scraping can be used in a wide variety of situations.

If you’re an avid investor, getting closing prices every day can be a pain, especially when the information you need is found across several webpages. We’ll make data extraction easier by building a web scraper to retrieve stock indices automatically from the Internet.

Getting Started

We are going to use Python as our scraping language, together with a simple and powerful library, BeautifulSoup.

  • For Mac users, Python is pre-installed in OS X. Open up Terminal and type python --version. You should see your python version is 2.7.x.
  • For Windows users, please install Python through the official website.

Next we need to get the BeautifulSoup library using pip, a package management tool for Python.

In the terminal, type:

easy_install pip  
pip install BeautifulSoup4

Note: If you fail to execute the above command line, try adding sudo in front of each line.

The Basics

Before we start jumping into the code, let’s understand the basics of HTML and some rules of scraping.

HTML tags

If you already understand HTML tags, feel free to skip this part.

<!DOCTYPE html>  
<html>  
    <head>
    </head>
    <body>
        <h1> First Scraping </h1>
        <p> Hello World </p>
    <body>
</html>

This is the basic syntax of an HTML webpage. Every <tag> serves a block inside the webpage:

  1. <!DOCTYPE html>: HTML documents must start with a type declaration.

  2. The HTML document is contained between <html> and </html>.

  3. The meta and script declaration of the HTML document is between <head> and </head>.

  4. The visible part of the HTML document is between <body> and </body> tags.

  5. Title headings are defined with the <h1> through <h6> tags.

  6. Paragraphs are defined with the <p> tag.

Other useful tags include <a> for hyperlinks, <table> for tables, <tr> for table rows, and <td> for table columns.

Also, HTML tags sometimes come with id or class attributes. The id attribute specifies a unique id for an HTML tag and the value must be unique within the HTML document. The class attribute is used to define equal styles for HTML tags with the same class. We can make use of these ids and classes to help us locate the data we want.

For more information on HTML tags, id and class, please refer to W3Schools Tutorials.

Scraping Rules

  1. You should check a website’s Terms and Conditions before you scrape it. Be careful to read the statements about legal use of data. Usually, the data you scrape should not be used for commercial purposes.
  2. Do not request data from the website too aggressively with your program (also known as spamming), as this may break the website. Make sure your program behaves in a reasonable manner (i.e. acts like a human). One request for one webpage per second is good practice.
  3. The layout of a website may change from time to time, so make sure to revisit the site and rewrite your code as needed

Inspecting the Page

Let’s take one page from the Bloomberg Quote website as an example.

As someone following the stock market, we would like to get the index name (S&P 500) and its price from this page. First, right-click and open your browser’s inspector to inspect the webpage.

Try hovering your cursor on the price and you should be able to see a blue box surrounding it. If you click it, the related HTML will be selected in the browser console.

From the result, we can see that the price is inside a few levels of HTML tags, which is <div class="basic-quote"><div class="price-container up"><div class="price">.

Similarly, if you hover and click the name “S&P 500 Index”, it is inside <div class="basic-quote"> and <h1 class="name">.

Now we know the unique location of our data with the help of class tags.

Jump into the Code

Now that we know where our data is, we can start coding our web scraper. Open your text editor now!

First, we need to import all the libraries that we are going to use.

# import libraries
import urllib2
from bs4 import BeautifulSoup

Next, declare a variable for the url of the page.

# specify the url
quote_page = ‘http://www.bloomberg.com/quote/SPX:IND'

Then, make use of the Python urllib2 to get the HTML page of the url declared.

# query the website and return the html to the variable ‘page’
page = urllib2.urlopen(quote_page)

Finally, parse the page into BeautifulSoup format so we can use BeautifulSoup to work on it.

# parse the html using beautiful soup and store in variable `soup`
soup = BeautifulSoup(page, ‘html.parser’)

Now we have a variable, soup, containing the HTML of the page. Here’s where we can start coding the part that extracts the data.

Remember the unique layers of our data? BeautifulSoup can help us get into these layers and extract the content with find(). In this case, since the HTML class name is unique on this page, we can simply query <div class="name">.

# Take out the <div> of name and get its value
name_box = soup.find(‘h1’, attrs={‘class’: ‘name’})

After we have the tag, we can get the data by getting its text.

name = name_box.text.strip() # strip() is used to remove starting and trailing
print name

Similarly, we can get the price too.

# get the index price
price_box = soup.find(‘div’, attrs={‘class’:’price’})
price = price_box.text
print price

When you run the program, you should be able to see that it prints out the current price of the S&P 500 Index.

Export to Excel CSV

Now that we have the data, it is time to save it. The Excel Comma Separated Format is a nice choice. It can be opened in Excel so you can see the data and process it easily.

But first, we have to import the Python csv module and the datetime module to get the record date. Insert these lines to your code in the import section.

import csv
from datetime import datetime

At the bottom of your code, add the code for writing data to a csv file.

# open a csv file with append, so old data will not be erased
with open(‘index.csv’, ‘a’) as csv_file:
 writer = csv.writer(csv_file)
 writer.writerow([name, price, datetime.now()])

Now if you run your program, you should able to export an index.csv file, which you can then open with Excel, where you should see a line of data.

So if you run this program everyday, you will be able to easily get the S&P 500 Index price without rummaging through the website!

Going Further (Advanced uses)

Multiple Indices

So scraping one index is not enough for you, right? We can try to extract multiple indices at the same time.

First, modify the quote_page into an array of URLs.

quote_page = [‘http://www.bloomberg.com/quote/SPX:IND', ‘http://www.bloomberg.com/quote/CCMP:IND']

Then we change the data extraction code into a for loop, which will process the URLs one by one and store all the data into a variable data in tuples.

# for loop
data = []
for pg in quote_page:
 # query the website and return the html to the variable ‘page’
 page = urllib2.urlopen(pg)
# parse the html using beautiful soap and store in variable `soup`
 soup = BeautifulSoup(page, ‘html.parser’)
# Take out the <div> of name and get its value
 name_box = soup.find(‘h1’, attrs={‘class’: ‘name’})
 name = name_box.text.strip() # strip() is used to remove starting and trailing
# get the index price
 price_box = soup.find(‘div’, attrs={‘class’:’price’})
 price = price_box.text
# save the data in tuple
 data.append((name, price))

Also, modify the saving section to save data row by row.

# open a csv file with append, so old data will not be erased
with open(‘index.csv’, ‘a’) as csv_file:
 writer = csv.writer(csv_file)
 # The for loop
 for name, price in data:
 writer.writerow([name, price, datetime.now()])

Rerun the program and you should be able to extract two indices at the same time!

Advanced Scraping Techniques

BeautifulSoup is simple and great for small-scale web scraping. But if you are interested in scraping data at a larger scale, you should consider using these other alternatives:

  1. You should check a website’s Terms and Conditions before you scrape it. Be careful to read the statements about legal use of data. Usually, the data you scrape should not be used for commercial purposes.
  2. Do not request data from the website too aggressively with your program (also known as spamming), as this may break the website. Make sure your program behaves in a reasonable manner (i.e. acts like a human). One request for one webpage per second is good practice.
  3. The layout of a website may change from time to time, so make sure to revisit the site and rewrite your code as needed

Adopt the DRY Method

DRY stands for “Don’t Repeat Yourself”, try to automate your everyday tasks like this person. Some other fun projects to consider might be keeping track of your Facebook friends’ active time (with their consent of course), or grabbing a list of topics in a forum and trying out natural language processing (which is a hot topic for Artificial Intelligence right now)!

Thanks for reading ❤

#python #web-development

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Web Scraping with Python and BeautifulSoup
Ray  Patel

Ray Patel

1619518440

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

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Sival Alethea

Sival Alethea

1624402800

Beautiful Soup Tutorial - Web Scraping in Python

The Beautiful Soup module is used for web scraping in Python. Learn how to use the Beautiful Soup and Requests modules in this tutorial. After watching, you will be able to start scraping the web on your own.
📺 The video in this post was made by freeCodeCamp.org
The origin of the article: https://www.youtube.com/watch?v=87Gx3U0BDlo&list=PLWKjhJtqVAbnqBxcdjVGgT3uVR10bzTEB&index=12
🔥 If you’re a beginner. I believe the article below will be useful to you ☞ What You Should Know Before Investing in Cryptocurrency - For Beginner
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Thanks for visiting and watching! Please don’t forget to leave a like, comment and share!

#web scraping #python #beautiful soup #beautiful soup tutorial #web scraping in python #beautiful soup tutorial - web scraping in python

Ray  Patel

Ray Patel

1619510796

Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Osiki  Douglas

Osiki Douglas

1624595434

How POST Requests with Python Make Web Scraping Easier

When scraping a website with Python, it’s common to use the

urllibor theRequestslibraries to sendGETrequests to the server in order to receive its information.

However, you’ll eventually need to send some information to the website yourself before receiving the data you want, maybe because it’s necessary to perform a log-in or to interact somehow with the page.

To execute such interactions, Selenium is a frequently used tool. However, it also comes with some downsides as it’s a bit slow and can also be quite unstable sometimes. The alternative is to send a

POSTrequest containing the information the website needs using the request library.

In fact, when compared to Requests, Selenium becomes a very slow approach since it does the entire work of actually opening your browser to navigate through the websites you’ll collect data from. Of course, depending on the problem, you’ll eventually need to use it, but for some other situations, a

POSTrequest may be your best option, which makes it an important tool for your web scraping toolbox.

In this article, we’ll see a brief introduction to the

POSTmethod and how it can be implemented to improve your web scraping routines.

#python #web-scraping #requests #web-scraping-with-python #data-science #data-collection #python-tutorials #data-scraping

Shardul Bhatt

Shardul Bhatt

1626775355

Why use Python for Software Development

No programming language is pretty much as diverse as Python. It enables building cutting edge applications effortlessly. Developers are as yet investigating the full capability of end-to-end Python development services in various areas. 

By areas, we mean FinTech, HealthTech, InsureTech, Cybersecurity, and that's just the beginning. These are New Economy areas, and Python has the ability to serve every one of them. The vast majority of them require massive computational abilities. Python's code is dynamic and powerful - equipped for taking care of the heavy traffic and substantial algorithmic capacities. 

Programming advancement is multidimensional today. Endeavor programming requires an intelligent application with AI and ML capacities. Shopper based applications require information examination to convey a superior client experience. Netflix, Trello, and Amazon are genuine instances of such applications. Python assists with building them effortlessly. 

5 Reasons to Utilize Python for Programming Web Apps 

Python can do such numerous things that developers can't discover enough reasons to admire it. Python application development isn't restricted to web and enterprise applications. It is exceptionally adaptable and superb for a wide range of uses.

Robust frameworks 

Python is known for its tools and frameworks. There's a structure for everything. Django is helpful for building web applications, venture applications, logical applications, and mathematical processing. Flask is another web improvement framework with no conditions. 

Web2Py, CherryPy, and Falcon offer incredible capabilities to customize Python development services. A large portion of them are open-source frameworks that allow quick turn of events. 

Simple to read and compose 

Python has an improved sentence structure - one that is like the English language. New engineers for Python can undoubtedly understand where they stand in the development process. The simplicity of composing allows quick application building. 

The motivation behind building Python, as said by its maker Guido Van Rossum, was to empower even beginner engineers to comprehend the programming language. The simple coding likewise permits developers to roll out speedy improvements without getting confused by pointless subtleties. 

Utilized by the best 

Alright - Python isn't simply one more programming language. It should have something, which is the reason the business giants use it. Furthermore, that too for different purposes. Developers at Google use Python to assemble framework organization systems, parallel information pusher, code audit, testing and QA, and substantially more. Netflix utilizes Python web development services for its recommendation algorithm and media player. 

Massive community support 

Python has a steadily developing community that offers enormous help. From amateurs to specialists, there's everybody. There are a lot of instructional exercises, documentation, and guides accessible for Python web development solutions. 

Today, numerous universities start with Python, adding to the quantity of individuals in the community. Frequently, Python designers team up on various tasks and help each other with algorithmic, utilitarian, and application critical thinking. 

Progressive applications 

Python is the greatest supporter of data science, Machine Learning, and Artificial Intelligence at any enterprise software development company. Its utilization cases in cutting edge applications are the most compelling motivation for its prosperity. Python is the second most well known tool after R for data analytics.

The simplicity of getting sorted out, overseeing, and visualizing information through unique libraries makes it ideal for data based applications. TensorFlow for neural networks and OpenCV for computer vision are two of Python's most well known use cases for Machine learning applications.

Summary

Thinking about the advances in programming and innovation, Python is a YES for an assorted scope of utilizations. Game development, web application development services, GUI advancement, ML and AI improvement, Enterprise and customer applications - every one of them uses Python to its full potential. 

The disadvantages of Python web improvement arrangements are regularly disregarded by developers and organizations because of the advantages it gives. They focus on quality over speed and performance over blunders. That is the reason it's a good idea to utilize Python for building the applications of the future.

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