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Top 10 Python Applications in the Real World | Uses of Python | Edureka

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Top 10 Python Applications in the Real World | Uses of Python | Edureka

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Python is free and simple to learn. Its primary features are that it is high-level, dynamically typed and interpreted.

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Dynamic Typing, Built-In Data Structures, Powerful Libraries, Frameworks, Community Support are just some of the reasons which make Python an attractive language for rapidly developing any sort of application. In this article, we will see some of the areas where Python excels in application development.

The following topics are to be covered in this article of Python Applications:

Introduction to Python

to code with. Python is free and simple to learn. Its primary features are that it is high-level, Python was developed in 1989 by Guido Van Rossum and emphasizes on the DRY (Don't Repeat Yourself) principle and readability. dynamically typed and interpreted. This makes debugging of errors easy and encourages the rapid development of application prototypes, marking itself as the language

With that out of the way, let's jump straight into the Python Applications. :)

Python Applications

Python supports cross-platform operating systems which makes building applications with it all the more convenient. Some of the globally known applications such as YouTube, BitTorrent, DropBox, etc. use Python to achieve their functionality.
Find out our Python Training in Top Cities/Countries
1. Web Development

Python can be used to make web-applications at a rapid rate. Why is that? It is because of the frameworks Python uses to create these applications. There is common-backend logic that goes into making these frameworks and a number of libraries that can help integrate protocols such as HTTPS, FTP, SSL etc. and even help in the processing of JSON, XML, E-Mail and so much more.

Some of the most well-known frameworks are Django, Flask, Pyramid. Why use a framework? The , scalability, convenience that they provide is commendable if we compare it to starting the development of a website from scratch.

2. Game Development

Python is also used in the development of interactive games. There are libraries such as PySoy which is a 3D game engine supporting Python 3, PyGame which provides functionality and a library for game development. Games such as Civilization-IV, Disney's Toontown Online, Vega Strike etc. have been built using Python.

3. Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence are the talks of the town as they yield the most promising careers for the future. We make the computer learn based on past experiences through the data stored or better yet, create algorithms which makes the computer learn by itself. The programming language that mostly everyone chooses? It's Python. Why? Support for these domains with the libraries that exist already such as Pandas, Scikit-Learn, NumPy and so many more.

Learn the algorithm, use the library and you have your solution to the problem. It is that simple. But if you want to go the hardcore way, you can design your own code which yields a better solution, which still is much easier when we compare it to other languages.

4. Data Science and Data Visualization

Data is money if you know how to extract relevant information which can help you take calculated risks and increase profits. You study the data you have, perform operations and extract the information required. Libraries such as Pandas, NumPy help you in extracting information.

You can even visualize the data libraries such as Matplotlib, Seaborn, which are helpful in plotting graphs and much more. This is what Python offers you to become a Data Scientist.

5. Desktop GUI

We use Python to program desktop applications. It provides the Tkinter library that can be used to develop user interfaces. There are some other useful toolkits such as the wxWidgets, Kivy, PYQT that can be used to create applications on several platforms.

You can start out with creating simple applications such as Calculators, To-Do apps and go ahead and create much more complicated applications.

6. Web Scraping Applications

Python is a savior when it comes to pull a large amount of data from websites which can then be helpful in various real-world processes such as price comparison, job listings, research and development and much more.

Python has BeautifulSoup which we use to pull such data. Here's a full-fledged guide to learn Web scraping with Python.

7. Business Applications

Business Applications are different than our normal applications covering domains such as e-commerce, ERP and many more. They require applications which are scalable, extensible and easily readable and Python provides us with all these features. Platforms such as Tryton is available to develop such business applications.

8. Audio and Video Applications
We use Python to develop applications that can multi-task and also output media. Video and audio applications such as TimPlayer, Cplay have been developed using Python libraries. They provide better stability and performance in comparison to other media players.

9. CAD Applications

Computer-Aided Designing is quite challenging to make as many things have to be taken care of. Objects and their representation, functions are just the tip of the iceberg when it comes to something like this. Python makes this simple too and the most well-known application for CAD is Fandango.

10. Embedded Applications

Python is based on C which means that it can be used to create Embedded C software for embedded applications. This helps us to perform higher-level applications on smaller devices which can compute Python.

The most well-known embedded application could be the Raspberry Pi which uses Python for its computing. We can also use it as a computer or like a simple embedded board to perform high-level computations.

We use python in a variety of applications. No matter what field you take up, Python is rewarding. So I hope you have understood the Python Applications and what sets Python apart from every other programming language. To know more you can enroll with our Master in Python programming course.

Now that you have understood Python Applications, check out the Python certification course by Edureka, a trusted online learning company with a network of more than 250,000 satisfied learners spread across the globe.

Got a question for us? Please mention it in the comments section of this "Top 10 Python Applications You Need to Know" blog and we will get back to you as soon as possible

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Top 10 Python Applications in the Real World | Uses of Python | Edureka

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Dynamic Typing, Built-In Data Structures, Powerful Libraries, Frameworks, Community Support are just some of the reasons which make Python an attractive language for rapidly developing any sort of application. In this article, we will see some of the areas where Python excels in application development.

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Top 10 Uses of Python in Real World with Examples

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Top 10 Uses of Python in Real World with Examples

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Ever since it was created in 1991 by Guido Van Rossum, Python has evolved and adapted to new technologies and today, it is one of the most popular programming languages.

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Great Learning Blog: Free Resources what Matters to shape your Career!

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Ever since it was created in 1991 by Guido Van Rossum, Python has evolved and adapted to new technologies and today, it is one of the most popular programming languages. The uses of Python in different areas can be attributed to the simplicity of its syntax, scalability, being an open-source, object-oriented, and easy to learn programming language. Let's know more about the uses of Python in the real-world which has made this language popular and the preference of almost every coder today especially those in the field ofArtificial Intelligence and Data Science.

Top 10 uses of Python in the real world -

Let us take a look at some of the real-world uses of Python. You can also gain some Python Project Ideas with the help of this course. You can also check out the best free python course with certificate to brush up on your Python fundamentals.

1. Web Development

Famously known as the go-to programming language for web development, Python has an important role to play in it. Python comes with multiple web development frameworks like Pyramid, Django, and Flask. These frameworks are packed with standard libraries that allow easy protocol integration and lead to a reduction in development time.

2. Data Science

We all know that data science is one of the most in-demand skills in the market. Knowledge of data science is a sought-after skill in IT, manufacturing, or eCommerce. This is where Python steps in. Its multiple libraries, such as Pandas, TensorFlow, NumPy, etc help in extracting valuable information from the data. Libraries like Matplotib and Seaborn further allow a data science professional to focus on data visualization through graphs and charts. It won't be an exaggeration to say that Python is the first thing that any data science professional needs to know.

3. Artificial Intelligence and Machine Learning

One of the most important uses of Python is in AI. The reason for it is that Python is a stable language that has the capacity to handle the computations required to build machine learning models. Its libraries like Keras, Pandas, NumPy and others are suitable for machine learning applications. Furthermore, it is used in multiple AI solutions like advanced computing, image recognition, data processing, and more.

4. Enterprise Applications

Enterprise applications are used to serve the needs of an organization rather than individual users. The use of Python in building enterprise applications is done as it is a robust language that can handle multiple requests of databases at once. Even though the use of Python varies from one enterprise to another, its core functionalities like its readability, functionality, and scalability remain the same. Enterprise applications are one of the most notable uses of Python. Tryton and Odoo are platforms that help in developing such enterprise applications.

5. Education Sector

One of the other important uses of Python is seen in developing online courses and education programs. It is an easy to learn programming language for beginners since its syntax matches that of English. It offers a novice, a standard library, and a variety of resources to understand the language, thus making the learning curve easier. This is one of the many reasons why Python is the preferred programming language for beginners for the education program's development at both basic and advanced levels.

6. Web Scraping Applications

It refers to the scraping of huge quantities of data by companies for extracting customer information to make profitable decisions. Tools like PythonRequest, Selenium, MechanicalSoup are used in Python programming for building web scraping applications. Thanks to Python's ability to build software that can process large amounts of data, this language is a clear winner in making web scraping applications.

7. Game Development

This one had to be featured in the top 10 uses of Python. And why not? Python has time and again displayed its capacity of contributing to the gaming industry in a massive way. Remember Battlefield 2; one of the most popular games in the early 2000s? It was developed using the Python programming language. Some of the top Python frameworks that are used in game development include Pygame, PyKyra, Pyglet, PyOpenGL, Kivy, Panda3D, Cocos2D, and more!

8. Software Development

One of the prime uses of Python is that it is used by software developers. Python simplifies the software development process for complex apps. It is used forproject management, as a support programming language, to build control, and testing.

9. Desktop GUI

One of the top uses of Python is developing a desktop GUI. We know that Python is a simple, stable, easy to learn, open-source, and platform-independent programming language. These factors work in their favour of being used for developing desktop GUI. Toolkits like PyQt, PyGUI, and WxPython are widely used for building high-quality GUIs efficiently.

10. Operating Systems

Python is a powerful programming language and so is C. When these two are combined together, many operating systems are developed. The use of Python in creating operating systems came to life with Ubuntu's Ubiquity, and Red Hat's Anaconda and Fedora. OS made with Python are running billions of computers today.

Summing up

As you'd know by now, the top 10 uses of Python in the real world and the fact that it is one of the most versatile programming languages. As you read above, the use of Python is at its peak in data science and AI, then why not start getting into these domains? Their professionals are amongst the highest paid individuals in the market today.

You can decide about it by reading more about the career options, salary insights, and industries where the data science domain is used in the career guide.

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Top 10 Uses of Python in Real World with Examples

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Ever since it was created in 1991 by Guido Van Rossum, Python has evolved and adapted to new technologies and today, it is one of the most popular programming languages. The uses of Python in different areas can be attributed to the simplicity of its syntax, scalability, being an open-source, object-oriented, and easy to learn programming language.

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Unlike CSS, HTML and JavaScript, Python is a general use coding script that can be used beyond web development for tasks such as software development and programming.

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Python is now the second most popular coding language among developers, stealing the spot from Java in 2020. It's recognized as the official coding language of tech giant Google and is responsible for creating some of the biggest applications in the world today (Reddit and Spotify to name a few).

Unlike CSS, HTML and JavaScript, Python is a general use coding script that can be used beyond web development for tasks such as software development and programming. It's versatile, easy to get to grips with, and is a highly demanded skill for many data-driven organizations.

But what exactly is Python used for in practice? And why has it become so popular?

What is Python used for? 5 use cases

Python is renowned for its broad range of use cases. We've listed the 5 top examples to give you a taste of what the coding language can do.

1. Web applications

Python streamlines web application development with its built-in frameworks:

Ultimately, these frameworks enable speedy application builds with minimal development time. You can read more about their individual nuances here.

2. Software development

Python is used to develop many different applications and platforms across industries. Notable examples include Instagram and Spotify.

The FinTech industry, in particular, favours Python over other coding languages to create competitive applications (such as Venmo). This is due to Python's ability to handle a range of complex mathematical tasks, of which the financial sector has many.

3. Data science

Data scientists use Python to help them extract, process and manipulate data via various Python libraries, such as Pandas and SciPy.

The coding language is relatively easy to learn when used in conjunction with data. As such, Python skills are increasingly in demand across both big and small data-driven organizations.

4. AI applications

The number of businesses adopting artificial intelligence grew by 270 percent in four years. It's no wonder, then, that AI and machine learning project development is a top priority for many organizations.

Python can easily handle the computations necessary to create machine learning models and is responsible for creating many advanced applications, such as facial recognition software.

5. Game development

Though not the most popular language for game development, Python is often used by developers for building tree-based algorithms and designing game levels.

In addition to this, some developers use Python for scripting games or automating repetitive tasks (such as batching game textures).

Why is Python so popular?

Now that we've explored the key use cases of Python, let's delve into why it's such a popular coding language.

1. It's simple to learn

Well, we say simple, but what we really mean is it's easy to learn how to solve your unique problems with Python. (As with any coding language, it takes a true guru to learn everything there is to know.)

If you're looking to become a Python guru, it may take you months or years to learn the ropes. But, if you're in a non-technical role and you're looking to analyze some key marketing data (for instance), learning some Python coding to solve your problem may only take a handful of weeks.

There are plenty of free online materials for learning the language, which helps a great deal!

2. It's versatile

Python can be used in a number of different development use cases, making it a go-to coding language for many developers, whether they work for a tech giant such as Facebook or a smaller games development company.

3. It's readable

Not all code is completely illegible. Python was built with readability in mind:

Its language is built upon English keywords rather than symbols

It prioritises whitespace and singular statements per line

As a result, developers can skim read code much easier when choosing to update applications and software.

3. It's an out of the box language

It's straight forward to get on the ground running with Python, as it comes with a standard library full of pre-built code modules.

This means your developers can start building applications immediately, without having to worry about coding certain modules.

4. It allows you to automate wherever possible

Python is perfect for organizations who champion DataOps ideology or are looking to boost their operational efficiency. This is because the coding language allows you to write programs in minutes that can automate hour-long tedious tasks.

Whether it's automating email notifications or updating Excel spreadsheets without manual intervention, Python encourages both technical and non-technical users to save time through automation.

A coding solution for the modern business

With its broad range of use cases, from web application development to AI project building, Python's soaring popularity doesn't really come as a surprise.

Its versatility, readability and accessibility make Python the coding language of the modern-day developer and want-to-be developer. That, and its reliability for heavily data-driven or statistical tasks, which opens the door for brilliant data science projects and DataOps driven processes.

If you'd like to find out how CloverDX supports Python, please get in touch with our team today.

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Python is now the second most popular coding language among developers, stealing the spot from Java in 2020. It's recognized as the official coding language of tech giant Google and is responsible for creating some of the biggest applications in the world today (Reddit and Spotify to name a few).

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We’ve been building Python since 1999. Since then millions of enterprise developers worldwide have downloaded and deployed our commercial offering of Python, Active Python.

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We've been building Python since 1999. Since then millions of enterprise developers worldwide have downloaded and deployed our commercial offering of Python, ActivePython.
activestate top 10 Python uses cases customer successes

Over the past two decades, the use cases for Python have grown, as has its usage and adoption. ActiveState has seen our customers' use of Python grow from a scripting solution for sysadmins to web development for programmers to the driving force behind machine learning. This paper covers 10 of the most common use cases by industry for Python that we've witnessed implemented by our customers:

#1 Insurance - Creating Business Insights

The Problem: Polyglot Disruption

An American multinational finance and insurance corporation was facing a number of challenges with their insurance business as smaller, more nimble competitors were gaining an advantage by introducing services driven by Machine Learning (ML). Rather than be relegated to a follower, the customer wanted to reinvent their internal processes, improve client-facing offerings, and ultimately disrupt the wider insurance industry through their own use of ML.

To that end, the insurer empowered multiple teams to develop new applications and services that use ML to inform and assist insurance employees. Unfortunately, each team chose their own set of data science tools leading to multiple different versions of Python across the organization. The result was support issues; compatibility issues when teams interacted, and fears of open source license lawsuits as teams installed third-party Python packages with little oversight.

The Solution: Commercial Support + Indemnification

A single, data science-focused build of ActiveState's Python, ActivePython, that delivered all of the data processing, NLP and Deep Learning capabilities each of the customers' ML teams required. Additionally, ActiveState's indemnification protected the customer from lawsuits against improper use of Python packages within their ML projects.

The Benefits: Reduce Legal Risk + Eliminate Compatibility Issues

Simplify support by providing one throat to choke.

Reduce legal risk by providing protection from lawsuits.

Eliminate compatibility issues both across teams (eg, when data scientists hand off their work to software developers) and with them (eg, less "works on my machine" occurrences).

#2 Retail Banking - Data Transformation And Manipulation

The Problem: Excessive Overhead Decreases Productivity

This large American department store chain with an in-store banking arm thrives on customer data. While the data is collected centrally in a Teradata warehouse, it needs to be shared with multiple applications that enable their supply chain (SAP), retail banking (SF.com) and analytics/reporting needs (IBM DB2).

The company standardized on Python for their data manipulation needs, but allowed each team to create their own version of Python specific to their requirements. As a result, the teams spent a lot of time and resources creating and maintaining their own Python distributions on AIX, a platform that is notoriously complex to build. And internal support teams were challenged to be able to provide each team with good technical support.

The Solution: Standard Python Build

A single, data engineering-focused build of ActiveState's Python, ActivePython, that delivered all of the data processing and manipulation each of the customers' teams required. ActivePython built on AIX provided all users with the Extract, Transform and Load (ETL) capabilities they need: multiple business units; multiple data migration targets; multiple use cases.

The Benefits: Increase Engineering Velocity + Decrease Support Costs

A single, standard solution across the org decrease support & maintenance costs.

Reclaim developer hours dedicated to Python support & maintenance.

#3 Aerospace - Meeting Deadlines For Software Systems

The Problem: Meeting Critical Deadlines

An American multinational aerospace, military and defense corporation was contracted to provide multiple systems for the International Space Station (ISS). Initially responsible for just the critical safety systems onboard the station, the contract was subsequently expanded to include multiple non-critical systems, providing more opportunity, but also more challenges.

Aerospace software that is focused on critical safety systems is typically written in stable, proven programming languages, such as Ada. But these older languages do not lend themselves well to scripting tasks, GUI creation or data science analysis. Additionally, cargo run dates to the ISS are fixed, which means that updates to existing systems or provisioning of new systems need to be created, vetted and delivered in a timely manner.

The Solution: Commercial Off-The-Shelf (COTS) Python

A single, data science-focused build of ActiveState's Python, ActivePython that delivered all of the data processing, NLP and Deep Learning capabilities each of the customers' ML teams required. Additionally, ActiveState's indemnification protected the customer from lawsuits against improper use of Python packages within their ML projects.

The Benefits: Bigger Contract Value + No Exposure

"Cost-plus" Uplift - For contracts where performance, quality or delivery time is a much higher concern than cost, purchasing a COTS solution speeds time to solution while increasing contract price.

Timely Support - Private, commercial support avoids the need to post issues on public sites, avoiding exposure of software flaws and decreasing time to solution.

#4 Finance - Data Mining For Cross-Selling Opportunities

The Problem - Business Transformation

An American multinational financial services corporation headquartered in New York City wanted to accelerate their digital transformation in order to put themselves at the forefront of the digital revolution. By mining complex digital customer and prospect behavioral data, the customer hoped to transform it into actionable information.

But such a major business transformation would require a corresponding technology transformation. To that end, the customer initiated a number of data science and machine learning projects to examine the structured data they've been collecting for years. The customer then correlated the structured data with unstructured data from web and social media.

The Solution - Python For Machine Learning

A single, standard, data science-focused build of ActiveState's Python distribution, ActivePython, for AIX, provided all of the data engineering and data modeling capabilities required. Using ActiveState's Python, ActivePython, ActivePython, the customer was able to combine their transactional data with social media (such as Facebook and Foursquare) data in order to identify when a customer was preparing for a vacation. Those customers were then offered cross-sell services such as travel insurance, foreign exchange, etc.

The Benefits - Increase Cross-Selling & Reclaim Resources

A marked increase in cross-selling opportunities.

Free up resources by alleviating pressure on their build team who would normally be tasked with creating, maintaining and supporting Python builds for each project.

#5 Business Services - API Access To Financial Info

The Problem: Enabling Partner Applications

This privately held financial data and media company headquartered in New York City had provided partners with access to their financial information through various electronic means. And with the popularity of Web APIs on the rise, the customer had also exposed their data via a set of APIs that partners could access directly with a simple REST call.

But many partners wanted to build desktop applications in a variety of languages, including Python. These partners wanted to incorporate the customer's API directly into their own applications.

The Solution: Python SDK

The customer used ActiveState's Python, ActivePython, ActivePython on Windows to create a Python Software Development Kit (SDK) for their financial information API. As a result, the customer can provide their API not only as a loosely coupled service, but also as a package that can be simply pip-installed into any Python project.

The Benefits: Increased Revenue & Customer Satisfaction

An SDK opens the door to new revenue opportunities through third-party installations of the customer's API.

World class support and timely updates to outdated/vulnerable packages from a commercial vendor increases customer and partner satisfaction.

#6 Hardware - Automating Network Administration

The Problem: Device Configuration At Scale

This American technology leader based in Silicon Valley develops, manufactures and sells networking hardware. A key enterprise demographic for them is network administrators, who are often confronted with managing the vendor's multiple devices across extended corporate networks.

The challenge lies in the fact that network admins aren't typically developers. Yet they want to improve their efficiency by being able to programmatically make configuration changes to multiple network devices at once. While sample scripts are easy for any network admin to use and modify, the vendor did not want to be on the hook for creating all the scripts their user base might require.

The Solution: Python Scripting

Enter Python, which is simple enough for any technical person to learn and start using effectively. ActiveState's Python, ActivePython, allows the customer to quickly create sample Python scripts to cover the majority of typical network admin use cases. And it also allows network admins to create Python libraries they can leverage to create their own, custom scripts in order to manipulate the configuration, operational state, or event data from network devices.

The Benefits: Lower TCO + Increased Security

Programmatic access to physical networking devices and virtual network resources, lowers the cost of administering enterprise networks by making network admins dramatically more efficient.

ActiveState's ability to deliver timely Python updates is also of paramount importance since Python code is executed directly on each network device, posing potential security risks.

#7 Healthcare - Disease Prognosis Prediction

The Problem: Predicting Terminal Illness Progression

This California-based medical center is consistently ranked as one of the best hospitals in the US, and also serves as a teaching hospital. But even the best medical practitioners struggle with predicting how diseases will progress. In fact, it's more guesswork than science, especially when it comes to terminal illness.

Existing solutions help improve patient treatment by better predicting disease prognosis. But these solutions are either too costly or too time-consuming to implement in a practical manner. What's required is a solution that provides better predictions, more cheaply and more quickly than existing methods.

The Solution: Deep Neural Networks

With recent breakthroughs in Artificial Intelligence (AI), predictive prognosis solutions have turned to Python-based Machine Learning (ML) techniques for an answer. In this case, ML was used to predict the mortality of a patient within 12 months of a given date based on their existing Electronic Health Record (EHR) data.

ActiveState's Python, ActivePython, was used to create a Deep Neural Network (DNN) using Pytorch and Scikit-Learn in order to predict death dates for patients with terminal illnesses. Each patient's EHR was input to the DNN, including current diagnosis, medical procedures and prescriptions.

The Benefits: Better, Timely Care

The DNN provides results that allow doctors to bring in palliative care teams in a more timely manner.

ActivePython allows researchers to focus on their R&D, not managing open source, so they can be more efficient and productive.

#8 Consulting Services - Bespoke Web Development

The Problem: Customization + Negligible Human Interaction

This global professional services firm focuses primarily on Human Resource (HR) consulting, based on a customizable self-service Employee Benefits & Pension Administration System. The firm is typically contracted to tailor the Benefits System for each of their blue chip clients, and also provide end-user support.

Although the firm bundled end user support into the solution, their goal is to automate everything a user can do (from enrolment to retirement) in such an intuitive way as to minimize support calls, improve customer satisfaction, and ultimately maximize profits.

The Solution: Web Specific Python Packages + Simplified Web App Dev

Creating a complex, fully customizable Web Portal that's simple to use requires a framework that offers clean, pragmatic design. ActiveState's Python, ActivePython, provides Web development frameworks like Django and Flask that help simplify the creation of Web applications.

The Benefits: Decreased Costs & Time To Market

An automated, self-serve Web Portal that emphasizes usability reduces the need to spend time administering & supporting it, decreasing overall costs.

For fixed time and materials contracts, the speed offered by an off-the-shelf solution like ActivePython far outweighs the time to create, maintain and update your own Python runtime environment.

#9 Infotech - IT Modernization

The Problem: Managing Remote Workers With EOL Tooling

supporting personnel who work on premise and/or remotely. As a result, the legislature's IT team is challenged to be able to remotely manage thousands of user desktops. One of the key tools that empowered the team was Windows Powershell.

The IT team used it to run scripts when users logged in, installed or updated applications, periodically collected system information, etc. Unfortunately, Powershell is unsupported, and no longer works under newer Microsoft operating systems.

The Solution: New Scripting Tools

Rewriting any application that's a cost center (i.e. any application that doesn't generate revenue) is always hard to justify. As a result, rewrites are rarely budgeted, planned and resourced appropriately. But the alternative was much more costly in terms of capital and resources: dispatching IT personnel to physically install, update and manage each user's desktop, as needed.

Python is famously easy to get started with, and easy to develop with, as well. While there was no way around a rewrite of their desktop management scripts, the IT team could at least minimize the time and resources required. ActiveState's Python, ActivePython, was chosen to provide the development and runtime environment for the new scripts.

The Benefits: Increased Productivity + Support Safety Net

Python's productivity allowed Powershell script rewrites to be done incrementally in IT's "spare time," lowering costs and reducing the risk of a "big bang" rip and replace project.

ActiveState's commercial support provided a safety net during the learning and implementation phase, allowing IT to proceed with their modernization project with confidence.

#10 Software - Adding Extensibility To Legacy Applications

The Problem: Custom Distribution & Installation of Python

This US-based public company has a 30+ year history of providing a suite of Electronic Design Automation (EDA) tools to their more than 5,000 customers worldwide. Their suite of tools has been historically built in C++, and is packaged for deployment on premise.

Because each customer has their own instance, it's natural for them to want to customize it to their needs. The customer has leveraged Python to wrap some of their C++ APIs in order to expose key functionality, thereby making their software solutions not only more extensible by customers. But ensuring Python gets deployed properly at customer sites meant including it within their existing installer, rather than relying on their customers to install Python correctly.

The Solution: Python OEM + Indemnification

The OEM distribution of ActiveState's Python, ActivePython OEM, allows the customer to include a standard version of Python within their application, and deploy it into a custom manner to ensure all EDA tool teams can take advantage of it. Additionally, ActiveState's indemnification eliminates the legal risk of incorporating open source libraries in the customer's products.

The Benefits: Decreased Support & Opportunity Cost

A standardized Python version deployed across all customer sites and developer desktops means the customer can:

Decrease support costs via a custom installer that deployed a portable version of ActivePython.

Speed time to market by eliminating the need for legal department reviews.

Eliminate the opportunity cost of managing their own Python build.

Better identify issues, bugs and performance issues through ActiveState's debug build.

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[Data Sheet] Top 10 Python Use Cases - ActiveState

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We've been building Python since 1999. Since then millions of enterprise developers worldwide have downloaded and deployed our commercial offering of Python, ActivePython. activestate top 10 Python uses cases customer successes Over the past two decades, the use cases for Python have grown, as has its usage and adoption.

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What Is Python Used For? 9 Use Cases [Overview Guide]

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What Is Python Used For? 9 Use Cases [Overview Guide]

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Programming is a great career choice for many reasons. And while many will agree that it’s best to start by mastering a single programming language, most don’t agree on which language you should learn first.

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Sakshi Gupta | 9 minute read | September 14, 2022

Programming is a great career choice for many reasons. And while many will agree that it's best to start by mastering a single programming language, most don't agree on which language you should learn first. That's why we've created this guide introducing you to Python, one of the most popular languages. In this guide, we'll tell you all about Python, what it's used for, and why many companies use it and seek candidates who are familiar with it.

What Is Python?

Python is a general-purpose programming language that is intuitive to learn. As a high-level, interpreted programming language, it can be read more like plain English. It can be used for anything from web applications, automating redundant tasks, object-oriented programming, machine learning projects, and more.

Why Is Python So Popular?

Python is popular because of its emphasis on plain English and simple syntax. Syntax refers to rules around how the programmer must use the language when writing code. There aren't as many cryptic symbols you need to worry about, and you can write code easily that makes sense to humans thanks to its high-level nature.

Below, we have two blocks of code that show how to define a function in Python (left) and JavaScript (right). Functions take in inputs (in the below example a and b) and produce outputs (in the below example a and b multiplied together).

Defining a Function:

Python generally requires fewer and simpler symbols. It is also less verbose, which makes it enjoyable to code in. Python also has very helpful line space and line break rules (the return statement is under the definition statement and indented over one). In the JavaScript code block, by contrast, I could have not indented and it would still run. This is just one reason why it's so popular.

Because Python is a general-purpose language, it has many applications. Coupling its versatility with its easy-to-understand syntax means it's both powerful and beginner friendly. When a programming language is popular, it also means there is a more active community that is willing to teach and learn how to debug and fix issues, advance methodologies, and more. This, in turn, ensures continued popularity and demand.

With this popularity comes packages built into Python as well. A package is a means of abstracting away some of the details of developing for a particular goal. For example, you actually don't need to define the function that I screenshotted above, because the NumPy package already has a multiplication function created among many other mathematical functions that people use all the time. With packages, you can work smarter not harder, and only create custom functions you really need to create. The popularity of Python means there are also many frameworks available in Python, which you can think of as systems and boilerplate projects for building a certain type of product like a website. For example, Django is a popular framework for developing fully featured web applications, and it is even used by the likes of Pinterest, Instagram, Spotify, and more.
Related Read: Why Are Python Programmers in Such High Demand
What Is Python Used For?

Source: Net Solution

Here are some of the ways that python is used:
Related Read: 17 Data Analyst Skills Employers Expect You to Have
You can perform all sorts of analyses using Python from basic arithmetic to more complex calculations. If you find yourself repeating your efforts over and over, then you can use Python's ability to define custom functions. In the previous screenshotted code snippets example, the manipulation was multiplying a and b together, which you get to declare the values of when you run the function. But the magic of Python and its large community is that there are so many packages available to use. NumPy is an example of a package that has many core functions valuable to data analysis such as min, max, mean, standard deviation, and anything else you would expect in a program like Excel. In addition to using NumPy to perform array arithmetic (mathematical operations down a column of data basically), you can also learn about lambda expressions, a super useful operation that comes out of the box in Python.
Related Read: A Full Introduction to Data Science With Python

Data science is the practice of capturing, analyzing, and using data to predict outcomes and derive insights. Python is the most popular programming language for data science, and as such, it has many of the popular packages used in data science.

Get To Know Other Data Science Students

Jasmine Kyung

Diana Xie

Machine Learning Engineer at IQVIA

Aaron Pujanandez

Machine Learning, Deep Learning, and Artificial Intelligence
Related Read: 15 Best Python Libraries for Deep Learning and Machine Learning
Machine learning, artificial intelligence, and deep learning are used to improve product experiences and deliver on business needs in real-time. The ultimate output of work in these areas is usually a predictive model which, usually in real-time, takes in data (generally created out of online users' behavior) and produces predictions and/or suggestions. An example of machine learning in action is Amazon's recommended products.

Python is used for all three of these fields, and programmers utilize libraries and frameworks to take some of the redundant work out of getting results. This is because similar problems have been solved by many smart people before.

Web Development

Python also has many frameworks available for producing fully functional websites and web applications. You could make anything with these frameworks. Examples include Django and Flask, which are the more popular ones. You can combine Python's excellent ability to perform calculations quickly with one of these frameworks to create a web application that can handle user data and inputs quickly to produce results. An example of this is Uber, which needs to perform ride time, arrival predictions, and make price changes on the fly.

Data Visualization & Scientific Figures

Python also has many excellent data visualization packages that give it powerful graphing capability. If you think visualizations in Excel are good, wait until you see Python-based ones. Seaborn and bokeh are two such examples. Seaborn has many great visualizations, and bokeh is a great option if you want to get into the world of interactive visualizations. You can drag sliders, change input values, colors, and more.

Here are just a few examples from the Seaborn package:

Desktop Graphical User Interfaces (GUIs)

Though cloud-based software has eclipsed GUIs in popularity, there are still GUIs out there that are tied to the cloud (Adobe Creative Cloud and Ableton to name a couple). And even GUIs can be made using Python. If that is your goal or if that is an area you are interested in, then you have this angle from which to learn Python as well.

Game Development

If you are interested in game development, you'd know that the biggest frameworks are Unity and the Unreal engine. But did you know that you can also develop games in Python using PyGame? If you love games and want to build your Python knowledge doing what you love, this framework would allow you to do so.

Real-world Python Use Cases

It should be no surprise that many businesses have taken a liking to Python. Let's take a look at how some well-known companies employ python:

Amazon

Amazon is heavily driven by machine learning and data science, and Python is the dominant language for creating machine learning models. Amazon has a recommendation engine under the hood that takes in data about your purchases and surfaces products similar to ones you have purchased before.

Instagram

Python is scalable for web applications, so it can handle lots of traffic and information (in the form of photos being sent and received in this case), making it perfect for Instagram's needs. Instagram also uses python to suggest content you'll like, just as Amazon does.

Uber

Uber uses Python's fast calculation times in order to produce real-time price fluctuations based on ride demand and driver supply, calculate totals and arrival times based on destination and origin, and more. Uber Eats uses similar complex and quick calculations to suggest add-ons to your order.

How To Get Started Learning and Using Python

Related Read: How to Write Data Structures and Algorithms in Python
If that all sounds exciting to you, and you want to learn how to use Python, then check out these tips and tricks for getting started:

Learn Basic Programming Concepts (Using Python)

Regardless of the programming language, programming has core concepts you can familiarize yourself with. If your goal is to learn Python, then commit to learning them using Python examples and make sure to run the code to observe the outputs. Try a course in understanding variables, boolean operators, mathematical operations (+, -, *, /), data types (integers, floats, strings), structures (arrays, lists, dataframes, tuples, etc.), and class definitions. As you develop experience and interest, you will understand more about what to learn next.

Read Coding Tutorials

Coding tutorials are often broken down by specialty or area. So if you're interested in learning python for data science applications, then try searching for "python data science tutorials" or whatever specialty you're interested in.

Have a Goal and Area of Interest in Mind

The road to learning Python is long, and so it's good to keep in mind why you want to learn it. Maybe you are a musician in your free time. If that's the case, check out some tutorials of people using Python to program a synthesizer or compose a track. Do you love video games? Well, there are tutorials on making games via the PyGame Python package. Tap into what you like to do and do it with Python, and you will be rewarded.

Python Usage FAQs

We've got the answers to your most frequently asked questions.

Since you're here...Are you a future data scientist? Investigate with our free guide to what a data scientist actually does. When you're ready to build a CV that will make hiring managers melt, join our Data Science Bootcamp that guarantees a job or your tuition back!

About Sakshi Gupta

Sakshi is a Senior Associate Editor at Springboard. She is a technology enthusiast who loves to read and write about emerging tech. She is a content marketer and has experience working in the Indian and US markets.

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What Is Python Used For? 9 Use Cases [Overview Guide]

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Sakshi Gupta | 9 minute read | September 14, 2022 Programming is a great career choice for many reasons. And while many will agree that it's best to start by mastering a single programming language, most don't agree on which language you should learn first.

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11 Real World Applications for Python Skills - Dataquest

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11 Real World Applications for Python Skills - Dataquest

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Python is one of the most frequently-recommended programming languages.

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Python is one of the most frequently-recommended programming languages. You've probably heard people say that's because it's relatively easy to learn - and that's true! But is Python actually useful? What are some of the real-world applications for Python skills once you've got them?

In this post, we'll look at some of the most common use-cases for Python. We'll also look at a few situations where Python probably isn't the best choice.

That said, it's important to keep in mind that Python is an incredibly versatile language. People use it for all kinds of things. The broad real-world use cases we'll cover here are really just the tip of the iceberg!

Who uses Python today?

The short answer: millions of developers, along with a lot of other folks. A 2019 estimate put the number of Python developers at 8.2 million. StackOverflow's 2020 developer survey ranks Python as one of the most popular and widely-used languages among developers. And as of April 2021, Indeed.com is listing nearly 100,000 open jobs that require Python.

Of course, there are also quite a lot of people who use Python that wouldn't be captured by these sorts of statistics and surveys. Python isn't just used by developers! It's used by marketers, researchers, data scientists, kids, hobbyists, IT professionals, and all sorts of other people. You don't have to make your entire living writing Python to get some real benefits from learning it!

At a professional level, though, Python is very useful. For example:

What companies use Python?

Here's just a short list of a few of the companies that use Python:

1. Google and subsidiaries like Youtube use Python for a wide variety of things. In fact, Youtube was built using mostly Python!

2. Industrial Light and Magic, the company behind the special effects of Star Wars and hundreds of other films, has been using Python for years for its CGI and lighting work.

3. Facebook and subsidiaries like Instagram use Python for various elements of their infrastructure. Instagram is built entirely using Python and its Django framework.

4. iRobot, the folks who make the Roomba vacuum, use Python to develop the software for their robots.

5. NASA and associated institutions like the Jet Propulsion Lab use Python for research and scientific purposes.

6. Netflix uses Python for server-side data analysis and for a wide variety of back-end apps that help keep the massive streaming service online.

7. Reddit runs on Python and its web.py framework.

8. IBM, Intel, and a variety of other hardware companies use Python for hardware testing.

9. Chase, Goldman Sachs, and many other financial firms use Python for financial analysis and market forecasting.

10. Quora is yet another huge social media platform that's built using lots of Python.

And that's just the tip of the iceberg! In fact, these days most large companies are probably using Python at some level. A good way to check is to search a job site like LinkedIn or Indeed for company name + python. Often, you'll find that companies are looking for people with Python skills.

So, everybody's using Python. What are they using Python to do? Let's dive into some real-world applications for Python.

How is Python used in the real world?

1. Data Analytics

As companies across every industry collect more and more data, they need people who can make sense of it. Often, that means hiring data analysts with Python skills.

Python is popular for data analysis work because of powerful libraries like numpy and pandas, which make data cleaning and analysis tasks relatively straightforward, even when working with massive datasets. There are also Python libraries that support a wide variety of other data analytics tasks, from scraping the web with Beautiful Soup to visualizing data with Matplotlib.

Software tools like Jupyter Notebook make it easy for data analysts to create easy-to-repeat analyses, or add text and visualizations that make their work understandable even to people without coding skills.

Example use case: An ecommerce website wants to understand its users better. A data analyst at the company could use Python to analyze the company's sales, highlight predictable trends, and uncover areas for improvement.

2. Data Science/"AI"

Python is also incredibly popular for more advanced data work in the realm of machine learning. Powerful libraries like scikit-learn and TensorFlow make implementing popular machine learning algorithms very straightforward, and more specialized libraries exist to help with a wide variety of specific machine learning tasks from image recognition to content generation.

Almost anything you see being discussed as "AI" in the news is some sort of machine learning implementation. And an awful lot of that machine learning is being done with Python.

Example use case: A video streaming platform wants to increase user engagement and stickiness. A data science team could use Python to build a predictive model that recommends videos to users based on factors such as their watch history, viewership habits, what videos other users with similar habits watched, etc.

3. Web Development

As evidenced in the list of companies above, Python is a very popular language for web app development. Many of the websites you use every day were built using Python and popular Python web frameworks such as Django and Flask. Although the pages themselves are are rendered with HTML and CSS, Python underlies these visual elements on many sites, driving functionality, managing databases, user accounts, and much more.

Example use case: A company needs to build a new version of its website with specific features. A web developer could build the new site with Python and Django, using the flexibility and power they offer to support any specific or custom features the company needs.

4. Game Development

Python is used in the development of indie video games, thanks to the existence of convenient libraries such as PyGame. (Noticing a pattern? Whatever your use case is, there's probably already a few Python libraries out there designed to help with it).

Python isn't used as frequently in the development of higher-budget games - if your goal is to build a photorealistic 3D world, Python's relatively slow speed and relatively high memory usage mean it's not the most ideal language for doing that. Python is sometimes used to build the systems that underlie these games, though. Games including Battlefield 2, Eve Online, The Sims 3, Civilization IV, and World of Tanks use Python, although none of them was coded entirely in Python.

Example use case: A small team wants to build a creative indie side-scrolling game. The devs could choose to work with Python to take advantage of the convenience of PyGame, and the relative ease of learning how to do new things in Python.

5. Software Development

Python is widely used in software development, across a wide variety of real-world applications. The line between software development and web development is a bit blurry these days, since almost all software is built to work on the web even when there's also a desktop app. Dropbox is a good example of a modern software development company that does both, and Python was used to build Dropbox's desktop app. Similarly, Spotify has both web and desktop apps, and Python was used to build a number of the background services that make them work.

Of course, Python is also used at many companies to develop internal software tools.

Example use case: A company plans to build a new email client. The developers choose to use Python because they know they'll be able to create web and desktop clients using Python and its relevant libraries.

Learn Python the Right Way.

6. Data Engineering

Many of the Python libraries that make it a great option for data analysts and data scientists also make Python an important language for data engineers. Data engineers use Python for tasks such as building pipelines, combining datasets, cleaning data, working with APIs, automating various data processes, etc.

Example use case: A company has a lot of data, but it's stored in various formats and databases, making it time-consuming for analysts to find and work with. A data engineer could use their Python skills to build a pipeline that automates collection from the various sources, joins and cleans the data, and makes it easier for analysts to access and filter.

7. Robotics

Python is a popular language in the field of robotics, both among hobbyists and professionals. On the hobbyist end of the spectrum, Python is frequently used together with the Raspberry Pi hardware platform, which allows for flexible and affordable experimentation. In business, Python is one of the languages commonly used for robotic process automation (RPA), and it's been used to do things like code industrial robot arms that can work in tandem with each other.

Example use case: A company orders a number of robotic arms for a manufacturing facility. Engineers could use Python to program their behavior, taking advantage of the language's high-level readability to make it easier for everyone to understand what the arms are meant to be doing.

8. Automation

Python is great for automating repetitive tasks, and there are almost endless real world use cases for Python automation. For example, Python is a popular tool in DevOps because it makes automating systems and processes efficient and transparent. But outside the realm of software development, it's also widely used to automate everything from complex systems to simple, personal processes like filling in a spreadsheet or responding to emails.

Example use case: A company reports its sales in monthly Excel spreadsheets from each region that have to be manually combined to build company-wide quarterly reports. Rather than do this time-intensive task by hand, an employee writes a Python script that combines all of the spreadsheets and produces each quarterly report automatically.

9. Hardware interfacing and control

Python's ability to control hardware goes beyond robotics - in fact, it is used in all sorts of real world hardware-control applications. For example, this convenient Python library makes using Python for a variety of industrial control applications possible.

Example use case: An engineer at a company needs to write software to control a complex HVAC system. They could write code in Python that can send commands to and receive data from the system's sensors and hardware controllers.

10. Education and training

Because it's a very high-level, "readable" language that also has a variety of practical uses, Python is a very popular first language for people who want to learn programming. The wide variety of Python tutorials, videos, interactive courses, and other educational materials available for Python make it arguably the easiest programming language to learn.

Example use case: A company wants its data analysis team to be able to move beyond the limitations of Excel and SQL. They choose to get the team training in Python, knowing that they will have a wide variety of learning resources to choose from.

11. Personal convenience

In this article, we've been mostly focused on business use cases for Python. But many of Python's commercial use cases are also applicable on a personal level, too. Python can be used to analyze your own data, to automate boring or repetitive elements of your job, or even to create art!

Example use case: Here's a very personal example - when I wanted to stop myself from sitting for long hours at a stretch, I used my beginner Python skills to write a little script that would pop up alerts at whatever interval I wanted, play a sound of my choosing, and prompt me to do a little exercise based on parameters that I could tweak.

What is Python not good for?

Python is a great and versatile language, but it's not the best solution for everything. Here are a couple examples of areas where Python might not be the best choice or the most common choice for real-world commercial applications.

Mobile app development

While you certainly can develop mobile apps for Python, you'll need to make use of third-party layers to make them work across Android and iOS phones. These extra layers can make Python apps less efficient, which means Python isn't always the best choice for mobile app development (although depending on your specific app's requirements, it might be fine).

If you are interested in developing mobile apps with Python, a variety of options exist. One of the most popular is the Kivy framework.

Of course, you can always use the web development power of Python together with a framework like Django to make web apps that work well in mobile browsers, too.

Things that require high speed or high memory usage

In part because Python is a high-level language, it's not always the fastest or most efficient option. For many use cases, this distinction won't matter - you'll never notice the extra tenth of a millisecond you might gain from using C++. But if, for example, you're working on a high-speed 3D-rendered video game, Python's speed and memory constraints will probably be too limiting.

Similarly, if you're doing something like writing an operating system, Python isn't a great choice because it's inefficiencies will be layered on top of each other as users run programs within the main program that is the OS.

When high speed and memory performance is critical, Python probably isn't the best option. However, in many cases - including all of the use cases described above - the minor sacrifices we make in speed and efficiency by using Python are far outweighed by the conveniences it offers.

Where can you learn Python skills?

Youtube. There are thousands of free Python tutorials on Youtube, covering almost every conceivable use case.

Dataquest. Interactive courses are a great option that makes it easier to get started, since you don't have to figure out how to install and run Python locally.

Udemy. If you learn well from video lectures, there are hundreds of Python courses to learn from here.

Coursera. University-branded video lecture courses that cover a number of different Python topics are available.

EdX. University-branded Python video lecture courses are available here too.

Books. Many Python books, including popular ones like this, are available for free if you don't mind reading them on a device.

Classes and bootcamps. There are many in-person learning options for Python, too, although these tend to be the most expensive way to learn.

Learn Python the Right Way.

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11 Real World Applications for Python Skills - Dataquest

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Python is one of the most frequently-recommended programming languages. You've probably heard people say that's because it's relatively easy to learn - and that's true! But is Python actually useful? What are some of the real-world applications for Python skills once you've got them?

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In this blog, I have covered the use cases for Python. Here are real-world use cases for Python across different industries and businesses.

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