If you’ve ever clicked on a list of recommended products or searched for information online, you’ve used a system powered by Python.
This powerful programming language is used throughout web development projects. From triggering automatic emails to creating dynamic webpages, Python is behind it all.
In this article, we'll demonstrate how using Python in web development can save developers time, money, and effort. We’ll then cover some examples so you can see the range of experiences Python can build. Let’s dive in!
How to Use Python in Web Development
Python is one of the most popular languages used for web development. Its clean syntax makes it easier to write and read. It can be used for back-end development, creating dynamic websites, data analytics, machine learning, and more.
Here are some of the most common ways that Python is used in web development.
1. Creating Websites
Using Python libraries such as Django or Flask allows developers to create fully functioning websites from scratch. These libraries give you complete control over the look, feel, and features of your website. You can skip adding extra code or plugins.
With these frameworks, developers can also build the back end of a website. That includes setting the infrastructure for data processing, database management, and authentication.
2. Automating Tasks
Python can be used to automate tasks on a website through a process called web scraping. Web scraping involves extracting data from websites in an automated manner.
Python provides powerful libraries like Beautiful Soup and Selenium, which enable developers to navigate website pages, locate specific elements, and extract desired information. This can be particularly useful for tasks such as gathering data from multiple web pages, monitoring website changes, or extracting data for analysis or reporting.
Automation saves time and reduces the manual effort required for routine maintenance tasks so developers can focus on more impactful projects.
3. Analyzing Websites and Gathering Data
Data analysis has become an essential part of web development. Having access to real-time analytics lets you make informed decisions about how users interact with your site. These insights enable ongoing optimization).
With the help of libraries like NumPy and Pandas, it’s easy for developers to sort through large datasets quickly and extract valuable information.
Accurately analyzing user behavior means that you can quickly address any issues they may have while on your site. You can then provide the best possible experience when using it.
4. Building REST APIs
Representational State Transfer (REST) APIs are extremely useful when it comes to building custom sites with separate front-end (user interface/UI) and back-end (server) components. APIs allow developers to communicate between these two parts easily.
Python makes building REST APIs easy by providing access to various HTTP methods such as GET, POST, and DELETE. These elements are needed for developing basic features, including authentication, SEO, and caching.
Using APIs ensures that all the requests made by users get responded to quickly, which results in a better user experience overall.
Examples of Python in Web Development
Now, let's look at some real-world examples where Python is currently being used. Check out 14 examples below from different industries, demonstrating how versatile this language really is.
If you loved the smash-hit Stranger Things, you might love the classic 1980s flick The Goonies. If you’ve ever looked for the perfect Netflix movie and browsed your recommendations, you’re using a feature powered by Python.
Netflix employs machine learning models and data analysis techniques to understand user preferences and provide accurate content suggestions. Python's libraries, like scikit-learn and TensorFlow, enable the development and training of these recommendation models.
Moreover, Python is utilized to improve the streaming performance on Netflix. The company utilizes Python-based tools for monitoring and analyzing network traffic and system performance. This helps in optimizing the streaming quality and reducing buffering issues.
What we like: Netflix’s use of Python shows the language's machine-learning capabilities.
If you’ve ever used a filter on Instagram to give yourself a makeover or change your hair color, you’ve benefited from Python. The platform heavily relies on Python-based libraries like OpenCV for tasks such as image filtering, face detection, and object recognition. That’s how filters know where your eyes, lips, and nose are. That makes image mapping seamless for the user.
Python is also used in content moderation and security mechanisms. Instagram utilizes machine learning algorithms built with Python to detect and prevent spam. These systems also flag inappropriate content and abusive behavior. This helps maintain a safe environment for users.
What we like: Instagram uses Python for both fun features and essential security functions.
Dropbox has been around since 2008 and uses Python extensively for both server-side and client-side applications.
The Dropbox server-side components, including the storage systems, data processing, and authentication mechanisms, heavily rely on Python. The Flask web framework, built using Python, is leveraged for routing requests and handling API endpoints.
Moreover, Python is utilized for implementing search functionality within Dropbox. Python-based libraries and tools, such as Elasticsearch, are used to power the search feature, enabling users to easily find files and folders based on various criteria.
What we like: Python powers the experience for both users and the server.
Spotify has millions of musical artists on its platform. Every time you search for a Taylor Swift album or a new track from your favorite indie artist, Python is put to work. Python helps manage and process this enormous data through frameworks like Django, enabling efficient database management, authentication, and content delivery.
If you’re a fan of your Discover playlists or personalized Rewind, you also have Python to thank. Python's machine learning libraries, such as scikit-learn and TensorFlow, aid in analyzing user data, identifying patterns, and generating music recommendations just for you.
What we like: Spotify shows the power of Python when parsing through large datasets.
When I’m looking for new restaurants, I trust Yelp. Or, more accurately, I trust Yelps users to post honestly about their experiences dining out. Python makes it easy to see the stars and know good dives from the bad.
Through Natural Language Processing (NLP) techniques, Python allows Yelp to analyze the sentiment and tone of user-generated reviews, distinguishing positive from negative. This helps in providing accurate ratings and useful insights.
Additionally, Python is employed in Yelp's back-end infrastructure. It helps manage the massive amount of data generated by Yelp users, including business information, user profiles, and reviews.
What we like: Yelp utilizes Python for API development, enabling third-party developers to access Yelp's data and integrate it into their applications.
What’s for dinner? Let’s ask Pinterest. If I search for chicken recipes, how does the platform know what I want? Hashtags help, but Python helps us find relevant content that is not tagged.
Python-based libraries like OpenCV support tasks such as image recognition, object detection, and visual search. These capabilities enable features like related pins, visual search, and content curation.
Python’s web development frameworks, notably Django and Flask, contribute to maintaining Pinterest's back-end infrastructure. These frameworks aid in handling user requests, managing databases, and facilitating interactions with the front end.
What we like: Python libraries like scikit-learn and TensorFlow are leveraged to develop and train these recommendation models.
Quora is a hub for questions and answers. Users can come to the community with their queries, then get insights from real people. Python enables Quora to index a vast amount of user-generated content efficiently, making it easy to discover relevant questions and answers. Python libraries like Elasticsearch make it easier to implement robust search capabilities.
Python is also employed for user authentication, security, and session management on Quora. Python frameworks like Flask and Django facilitate the secure handling of user login and account management.
What we like: Python parses through large quantities of data to identify spam and inappropriate content.
Whenever you hit the road, there are multiple routes that will take you to the right destination. Uber uses Python to find the most efficient option. Python libraries like GeoAlchemy provide tools for efficient geodata management. The platform can then handle GPS data, calculate distances, and identify optimal routes for drivers.
Python is also employed for fraud detection within Uber. Python's machine learning libraries, coupled with anomaly detection techniques, help identify and prevent sketchy activities.
What we like: Python's data processing and analysis libraries are employed to generate predictive models and optimize operations, like surge pricing and ETA.
Reddit is powered by online communities, but human moderates get busy, too. Mod bots can take on easy tasks, like reminding folks of the subreddit guidelines. Python's ease of use and versatile libraries make it a popular choice for creating bots on Reddit.
Python's powerful libraries, like BeautifulSoup and Scrapy, are commonly used to scrape data from external websites. Reddit can use these tools to fetch information from external sources and display it within its interface.
What we like: Python allows developers to write, test, and rewrite code quickly, allowing them to create and revise content quickly for Reddit.
Google is one of the most well-known and well-established companies continuously using Python for their entire products, even newer ones such as Google Assistant.
Python's simplicity and versatility make it ideal for automating various tasks at Google. From managing servers and deploying code to scheduling and executing repetitive tasks, Python scripts help improve efficiency and streamline processes.
Python's extensive libraries process and analyze large volumes of data within Google. These libraries enable tasks, like data cleaning, manipulation, statistical analysis, and visualization.
What we like: Python provides an easy-to-use interface for Google’s cloud services.
If you just bought a teacup, you might like an electric kettle or a box of green tea. After all, other users purchased these products together. Python powers this recommendation system. The language’s machine learning and natural language processing capabilities help suggest relevant products based on customer preferences.
Python is also used for dynamically managing pricing and inventory levels on Amazon. Algorithms written in Python help determine optimal prices based on factors like demand, competition, and pricing history.
What we like: If you rely on reviews before you buy, that’s powered by Python, too. Python-based sentiment analysis techniques help understand and summarize customer opinions.
When you’re looking for an Airbnb, you need to know when the spot is available. Python is behind that helpful calendar view. Python helps facilitate smooth transactions between hosts and guests, from managing availability to processing payments.
Airbnb also uses Python to manage pricing strategies. Python's data processing libraries, such as Pandas, help determine optimal pricing and identify price fluctuation patterns. This enables hosts to adjust their price strategies based on demand, seasonality, and market conditions.
What we like: Python even helps manage the images uploaded on Airbnb. Python libraries, like Pillow, facilitate image manipulation and ensure optimal image display on Airbnb's interface.
Is it time to leave the A/C and stand outside in the sun? Well, let’s see where the Lyft is. Python's asynchronous programming capabilities to provide real-time updates to users. This includes information on ride availability, driver location tracking, and updates on estimated arrival times.
Further, Python‘s rich ecosystem of machine learning libraries plays a role in Lyft’s interface. Machine learning models built with Python enable fraud detection, personalized recommendations, and demand prediction.
What we like: By relying heavily on Python’s robust programming capabilities, Lyft was able to take on the taxi industry giants to become one of the most popular ride-hailing services.
Twilio, a cloud communications platform, utilizes Python to help businesses level up. Python is used to build interactive voice response (IVR) systems and messaging applications on Twilio's interface. These scripts allow developers to define call flows, handle user input, and generate dynamic responses with text-to-speech or voice recordings.
Additionally, Python libraries such as OpenCV or Pillow enable media processing tasks. That includes image manipulation, video encoding/decoding, and applying real-time filters during video conversations.
What we like: The Twilio-python helper library allows you to interact with Twilio API from your Python application.
The Power of Python
With its robust programming capabilities and scalability, Python remains a top choice when building anything — from simple static websites to complicated AI systems. So, if you’re looking to develop something amazing, this can be your go-to language!