Uses of Python in Major Industries: The Ultimate Guide

Python is a high-level programming language that’s interpreted and supports object-oriented programming, making it easy to reuse code throughout your project. Many developers and software development companies adopt it. Hence, it has more uses for Python in major industries. In this blog, we will elaborate on the uses of python in real-world applications.

Why Python?  

When building an application, the first thing that comes to mind is selecting the best programming language or Framework. Before we find out what python is used for, we should raise a question about why we should use Python.

When selecting a programming language, it’s best to focus on its core features, including: 

  • Syntax
  • Compiler 
  • Library and Frameworks it supports. 

Python has a straightforward syntax, almost like the English language, making it extremely beginner friendly. In addition, it also makes it easier to find bugs in your code and code written by other developers. Its readability also helps developing teams focus on building rather than spending time understanding each other’s code. As a result, this decreases production time, and developers can make their applications scalable.

It has extensive documentation available on the internet with contributions from companies like Google. It makes the language dependable and future proof making it the first choice for most software developers.

Python has an extensive library and frameworks like Flask and Django, enabling its use in software engineering, web development, automation, etc.

Example Code

The following code snippet requires the Python compiler and flash library should be installed on the system.

from flask import Flask
app = Flask(__name__)

@app.route('/')
def hello():
    return 'Hello World!'

if __name__ == '__main__':
   app.run(debug=True)

Explanation

  • Line#1: We import the Flask library.
  • Line#2: The ‘Flask’ application object by the name of app is created to access the ‘Flask’ library.
  • Line#4–6: We create a route ‘/’ which redirects to a webpage, and we return the string ‘Hello World!’ which is printed on the webpage. 
  • Line#9–10: Part of the syntax needed to compile the code.

Real World Uses of Python in Major Industries: What & How

We’ve already discussed why Python is the best choice for most projects and applications. Now let’s take a look at some use cases of Python:

We’ve already discussed why Python is the best choice for most projects and applications. Now let’s take a look at some use cases of Python:

Web Development

Modern web development trends require core concepts like data analytics and machine learning to add functionalities like chatbots. Python enables developers to use web frameworks to connect backend code with frontend, handle database connections, and use Machine Learning tools such as tensor flow and Scikit-Learn to add core functionalities.

  • Flawless mobile & Desktop versions: The commencement of the latest technology stacks, especially Python Django, results in robust, scalable, and more efficient web applications with less probability of technical errors and website downtime. These applications operate flawlessly on mobile as well as on desktop versions with different abilities to automatically reshape website interfaces according to underlying screen dimensions.
  • Asymmetrical layouts: Python, via its pre-build libraries and frameworks, including Flask, Django, Pyramid, and Web2py used to create asymmetric designs and website layouts with fewer resources and codes.
  • Progressive web apps: Python is a simple programming language used to create robust web applications integrated with essential technologies, including HTML, CSS, and JavaScript. Python Django is renowned for progressive web applications due to built-in designs and libraries to create robust, scalable, and resource-efficient applications.
  • Integrated animations: Python has capabilities to create animations and 3D graphics that meet business needs seamlessly. It has a wide range of pre-built frameworks, libraries, and modules to create avant-garde UI/UX for end-users.
  • ML-powered chatbots: The innovation of artificial intelligence changed everything from self-driving cars to industrialization. Natural language processing has given machines the ability to understand human queries and respond to them accordingly. Intelligent chatbots deployed on web applications have increased customer experience and retention via state-of-the-art 24/7 support.  

Internet of Things

The internet of Things involves hardware that is controlled through embedded systems attached to these devices. These embedded systems can access the internet and be coded to respond to requests made over the network. The involvement of IoT devices has made human life more accessible than it was. 

The IoT devices are designed to automate systems via the commencement of artificial intelligence and machine learning. In the commodity sector, these devices collect real-time data that is forecasted to draw fruitful insights using predictive or prescriptive data analytics. Moreover, IoT circuits are used in real life to automate homes, business operations, and monitoring through AI sensors.

Benefits of using Python

  • Supported by a large community and maintained documentation make it a reliable choice.
  • It is an interpreted language making it portable as it is not system dependent. As a result, it is also compatible with most IoT hardware in the market, regardless of the architecture.
  • IoT applications are renowned for generating a large amount of data, and Python is known for data management.
  • Python enables writing clean and shortcode and, as a result, takes less space on IoT hardware with limited space.

How Python Evolving Business With Machine Learning

Machine Learning algorithms make predictive analyses without human intervention through studying data and statistics.

Python is preferred for machine learning due to its extensive library support, including Sckit-learn, TensorFlow, Nilearn, and mlpy.

Supervised Learning

In supervised machine learning, algorithms are trained on large labeled datasets to predict the outcome accurately. It enables organizations to solve real-world problems at scale, such as classifying spam emails for their customers. 

Unsupervised Learning

Unsupervised machine learning algorithms are employed to analyze and cluster unlabelled datasets. As a result, these algorithms can find similarities and differences in data and are used in data analysis and computer vision.

Therefore, Python is preferred for machine learning due to its abundant ML algorithms accessible through its extensive libraries.

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Uses of Python in Major Industries

We’ve already discussed why Python is so popular. Let’s look at some industries that use Python to power their products.

Fintech Sector

This industry requires dealing with a large amount of financial data that needs to be analyzed, processed, and displayed through data visualization.

  • Data Analysis: Python provides machine learning-based solutions that can automate fintech databases or fintech data warehouses, such as sorting data into tables or DataFrames, Data transformation into meaningful insights using Seaborn, Matplotlib. It also comes with libraries such as Pandas, TensorFlow, PyTorch, and Apache Spark to perform statistical analysis, visualization, and data transformation.
  • Cryptocurrency Forecasting: Businesses dealing in cryptocurrency always require solutions to monitor cryptocurrency market volatility by forecasting and analyzing this data. Machine learning models written in Python enable developers to predict cryptocurrency prices and tools to analyze and display their results.
  • Data Engineering on Financial Data: Data engineers deal with building applications processing large amounts of data, building complex algorithms, conducting complex data analysis, and data visualization. In addition, they require their applications to communicate with cloud services and databases for storing all this data. Let’s take a look at how Python deals with these:
  • Data Ingestion: Python provides access to an extensive list of libraries and frameworks for processing data. SQLalchemy is one framework enabling connection to many databases such as MSSQL, MySQL, and PostgreSQL. Another popular library for data processing is Pandas, which allows reading data from multiple file formats such as CSV, HTML, JSON, SQL, Docx, etc. In addition, it provides a library of functions for scientific calculations and data structures enabling developers to gain valuable insights from their data.

HealthCare Sector

Technology has revolutionized the healthcare industry in recent years, especially medical diagnostics. Furthermore, Python provides software solutions based on machine learning and National Language processing. There are limitless advantages of python based applications in the healthcare or e-health sector.

Advantages of Python in the Healthcare Sector

  • Hospital Management Optimization: Python-powered applications can be used to prevent overstaffing in hospitals by training machine learning algorithms on staff shifts. This helps hospitals save revenue that can be utilized in other essential areas such as equipment. 
  • Optimizing the ICU Resources: Another application of Python-powered applications is maximizing the use of ICU resources in hospitals. These are already limited in hospitals, training ML models using techniques such as Deep Learning and Gradient Boosting Decision Tree on the stay of ICU patients can help save ICU resources.
  • Optimize management Costs: To achieve this optimization level, data scientists use ML tools such as Tensor flow and Scikit-Learn. These applications can help hospitals optimize their management of costs and resources.
  • Image Diagnostics: Humans find it hard to evaluate multiple images simultaneously.  This is where deep learning comes into the scene and assists human powers. Deep learning algorithms written in python can process numerous images from MRIs and X-rays precisely to detect tumors, cardiovascular diseases,  bone fractures, and novice cancer. In addition, pathologists can assess machine learning models to achieve an accuracy of up to 99.5% in disease diagnosis.
  • NLP as Health Service: Hospital patient records compromise unstructured data that can be processed using NLP algorithms to group patients based on their history. It can provide doctors with data that can be modeled to gain insights, such as how long cancer takes to spread in patients of different ages. The natural language processing (NLP) algorithms implemented in python can assist in operating medical equipment.  

Business Analytics & Business Intelligence

Excel was the go-to application for companies when dealing with small data sets and real-time data. This trend is changing with the advancement in technology and the increase in the magnitude of data collected. That’s where python comes in!

Data Scientists deal with fields like machine learning, artificial intelligence, and deep learning that involve analyzing data using complex algorithms. Python provides dedicated libraries and frameworks that help data scientists manage complex problems such as: 

  • Data visualization
  • Data modeling
  • Data exploration 
  • Data Collection and Cleaning

Advantages of Python in Business Intelligence

  • Descriptive Analysis Using BI & Dashboards: Business Analytics involves understanding the trends across data through visual analysis. Data Scientists prefer using Python as it provides libraries such as Pandas for manipulating data and Matplotlib for creating visualizations.
  • Predictive Analysis Using ML/DL: Machine Learning is used in Business Analytics to predict future events using data modeling and statistical algorithms. Most entertainment businesses such as Netflix and Youtube have a recommended section based on predictive Analysis. Data Scientists use Tensor flow, a popular python library that provides access to supervised and unsupervised ML algorithms. 

Finance Sector

The finance industry deals with a lot of data. If analyzed and processed, it provides helpful insight that can help increase revenue. The analysis and processing are done through AI-supported data analysis, and as a result, data scientists choose python as their go-to language for such applications. Lets us look at an example of how python helps in predicting stocks prices:

Advantages of Python in the Finance Sector

  • Convenient Financial Data Analysis: Data Scientists use algorithms of ML from Scikit-learn, a python library, for training ML models on the data gathered. These models use statistical data, company announcements, and revenue results to make a statistical prediction about future stock prices. The accuracy depends on the magnitude of data and the type of algorithm used.  
  • More Accurate Financial Data Visualization: The results are displayed in visual representation in the form of charts and graphs using the Matplotlib library. As a result, python makes it easier for users to get relevant data, analyze it and produce visual insights in the same language. It helps developers to reduce production time for their applications.
  • Efficient Data Pipelining: Python supports Pandas web data reader extensions to get financial data from remote databases like Google Finance and Enigma.

The Bottom Line

Python is a reliable, sustainable, well-documented language with a secure future due to the growing community of developers supporting it.

We’ve already seen the reason why developers support python. In case you missed any, here’s a concise list:

  • Python enables decreased production time of applications due to its clean syntax.
  • Developers can understand each other’s code written in python due to its high readability.
  • Python provides large libraries and frameworks catering to projects in diverse fields, from web and software development to machine learning and IoT app development.
  • Companies like Google support Python by contributing to its documentation and spreading awareness through workshops on the benefits of using python.
  • Its reliability is seen as tech giants like Youtube, Google, and Reddit use it to power their applications.
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