Python is a free, open-source, high-level programming language created by Guido van Rossum and first released in 1991. It is used to build web back ends, analyse data, train AI models, automate repetitive tasks and test software. Its readable syntax and a large library ecosystem let the same language serve beginners writing their first script and companies running services for hundreds of millions of users.

Python is the most widely referenced programming language in 2026:

• It ranks No. 1 on the TIOBE Index, at 17.76% in September 2026.
• In the Stack Overflow 2025 Developer Survey, 57.9% of developers use it, up 7 percentage points in one year. Stack Overflow attributes that growth to Python’s role in AI, data science and back-end development.

The Python Software Foundation maintains the language and released Python 3.15 on 9 October 2026.

This guide explains what Python is used for and the main applications of Python by industry. It also covers real companies that use Python, Python for automation and testing, and Python for data analytics and AI. A closing note covers where Python is not the best choice, such as mobile apps.

Quick answer: what is Python used for? Python is mainly used for:

• web back-end development (Django, Flask, FastAPI)
• data analysis and visualisation (pandas, Matplotlib)
• machine learning and AI (scikit-learn, PyTorch, LLM APIs)
• automation and scripting
• software testing (pytest, Selenium, Playwright)
• DevOps and cloud tooling

It is less common for mobile apps and high-performance games.

Table of contents

1. 10 Uses of Python Programming Language
2. Python Uses by Industry
3. Companies That Use Python
4. Python for Automation, Scripting and Testing
5. Python for Data Analytics and AI
6. Learn Python at Cyber Success
7. Frequently Asked Questions

10 Uses of Python Programming Language

Editor note: this replaces “Top 8 Uses of Python Programming Language”. It keeps the original eight uses, corrected, and adds automation and testing. Data analytics is split from AI.

The main uses of Python are back-end web development, data analytics, artificial intelligence, automation, testing and scientific computing. Python is used less for mobile apps and high-performance games. Each use below names the libraries professionals actually use.

1. Web development (back end)

Python powers the server side of websites and APIs: business logic, databases and authentication. The main frameworks are Django for full-featured sites, Flask for lightweight apps, and FastAPI for high-performance APIs. Large platforms combine Python with other languages; YouTube, for example, uses Python alongside C++, Java and Go.

2. Data analytics

Analysts use pandas to clean and summarise data, NumPy for numerical work, and Matplotlib or Seaborn for charts. This is one of the most common entry-level uses of Python.

3. Artificial intelligence and machine learning

Python is the standard language for machine learning, deep learning and generative AI, through scikit-learn, PyTorch, TensorFlow and Hugging Face. The heavy computation runs in optimised C++ and CUDA underneath. See the role of Python in artificial intelligence.

4. Automation and scripting

Python scripts automate repetitive work: renaming files, filling spreadsheets, sending reports, scraping websites and calling APIs.

5. Software testing

QA engineers write automated tests in Python with pytest, Selenium and Playwright.

6. FinTech and finance

Banks, trading firms and fintech start-ups use Python for risk models, fraud detection, quantitative analysis and reporting, with pandas, NumPy, SciPy and scikit-learn.

7. System scripting and DevOps

Python does not build operating systems; OS kernels are written mainly in C. It is widely used to manage them, though. Linux tools, configuration scripts, Ansible automation and cloud SDKs such as AWS boto3 are Python.

8. Desktop applications

Python can build cross-platform desktop apps with Tkinter, PyQt or PySide. Dropbox’s desktop client grew to over one million lines of Python, which Dropbox migrated to Python 3 in 2017–2018.

9. Game development and scripting

Pygame is a popular way to learn game programming. In commercial games, Python is mostly used for scripting and tools, while engines are written in C++ or C#.

10. Startups and prototyping

Start-ups use Python to build and test products quickly. Python’s large library ecosystem means features such as payments, email and analytics often need only a few lines of code.

Mobile apps: where Python is not the first choice. Native mobile apps are mainly built with Kotlin (Android), Swift (iOS), Flutter or React Native. Python can run on phones through BeeWare and Kivy, but it is mainly used for the back-end APIs that mobile apps call. The FAQ below covers this in detail.

Python Uses by Industry

Python’s real-life applications span nearly every industry that handles data or software. The table shows what Python does in each sector, the libraries commonly used, and the roles that use it.

In Pune, Python hiring is concentrated in IT services, banking and fintech, global capability centres (GCCs), and product companies. Open roles are listed on the Python developer jobs in Pune page.

Companies That Use Python

Instagram, Netflix, Spotify and Dropbox all use Python in core parts of their products, as their own engineering teams have described. Each uses it for a different job: Instagram for its web back end, Netflix for infrastructure and machine learning, Spotify for back-end services and data pipelines, and Dropbox for its desktop app.

Instagram: Python and Django at massive scale

Instagram’s back end is built on Django, a Python web framework. Its engineering team has described running the largest Django deployment in the world while serving more than 500 million users. The team also reported deploying back-end code 30 to 50 times a day. Instagram’s mobile apps are native; Python runs the servers behind them.

Netflix: Python across infrastructure and machine learning

Netflix engineers have said Python is used throughout the content lifecycle. Uses include operating the content delivery network that streams video, regional failover and traffic tools, security automation, alerting and auto-remediation, and machine learning for recommendations and artwork personalisation. A/B test analysis runs in Jupyter notebooks.

Spotify: back-end services and data pipelines

In 2013, Spotify’s engineering blog reported that about 80% of its back-end services were written in Python, alongside Java and some C/C++. About 90% of its MapReduce data jobs were also in Python. Spotify built and open-sourced Luigi, a Python tool for batch data pipelines, which powered features such as Radio and Discover. These figures are from 2013 and describe Spotify’s architecture at that time.

Dropbox: a desktop app built in Python

Dropbox’s desktop client grew to over one million lines of Python, and the company carried out one of the largest Python 2 to Python 3 migrations. Guido van Rossum, Python’s creator, worked at Dropbox from 2013 to 2019.

Other well-known users: Google, Reddit and Pinterest have also used Python in significant parts of their systems, usually alongside other languages.

None of these companies uses Python alone. Large products combine languages, and Python’s role is usually the back end, data, automation or machine learning.

Python for Automation, Scripting and Testing

Python is one of the most practical languages for automation because a short script can rename files, process spreadsheets, call web APIs or control a browser. The same strengths make it popular for software testing, where pytest, Selenium and Playwright let QA engineers automate test cases in readable code.

Automation means using code to perform a repetitive task without manual effort. Scripting usually means short programs written to do one job, often run on a schedule or from the command line.

Everyday automation with Python

• Files and folders: renaming, sorting and backing up files with pathlib and shutil
• Excel and reports: reading and writing spreadsheets with pandas or openpyxl, and generating PDF or email reports
• Web and APIs: downloading data with requests, and scraping web pages with BeautifulSoup
• System and cloud tasks: server scripts, scheduled jobs, and AWS automation with boto3

This script renames every .txt report in a folder to a consistent lowercase format:

from pathlib import Path

for f in Path(“reports”).iterdir():
    if f.suffix.lower() == “.txt”:
        f.rename(f.with_name(f.stem.lower().replace(” “, “_”) + “.txt”))
# “Sales Report Jan.TXT” -> “sales_report_jan.txt”

Python for software testing

Python test automation uses three main tools:

• pytest for unit and API tests
• Selenium, the long-established browser automation library with the widest browser and language support
• Playwright, a newer browser automation framework from Microsoft that waits for elements automatically and runs in Chromium, Firefox and WebKit

In the TestGuild 2025 survey (about 200 respondents), 45.1% of testers used Playwright and 22.1% used Selenium. Both have official Python libraries.

A pytest unit test:

def total(prices, discount=0):
    return round(sum(prices) * (1 – discount / 100), 2)

def test_total_with_discount():
    assert total([100, 250], discount=10) == 315.0

A Playwright browser test in Python (with pytest-playwright):

from playwright.sync_api import Page, expect

def test_login_shows_dashboard(page: Page):
    page.goto(“https://example.com/login”)
    page.fill(“#user”, “asha”)
    page.fill(“#pass”, “secret”)
    page.click(“text=Log in”)
    expect(page.get_by_role(“heading”)).to_have_text(“Dashboard”)

Python automation testing skills lead to QA Automation Engineer and SDET roles. Learn more about the tools in Playwright automation and the software testing course in Pune.

Python for Data Analytics and AI

Python is the leading language for data analytics and artificial intelligence. It covers the whole workflow: loading and cleaning data, analysing it, visualising results, training machine learning models, and building AI applications with large language models (LLMs). Stack Overflow credits Python’s 2025 usage jump to exactly these AI and data uses.

Data analytics is the process of examining data to find trends and answer business questions. AI is the broader field of building systems that learn from data or generate content. Python serves both, with the same core libraries.

Python for data analytics

A data analyst’s Python workflow typically follows four steps:

1. Load: read CSV, Excel or SQL data into a pandas DataFrame
2. Clean: handle missing values and duplicates, and fix data types
3. Analyse: group, filter and aggregate
4. Visualise: build charts with Matplotlib, Seaborn or Plotly, or feed the data into Power BI

The example below totals revenue by city from a sales file in three lines:

import pandas as pd

df = pd.read_csv(“sales.csv”)  # columns: city, product, revenue
print(df.groupby(“city”)[“revenue”].sum().sort_values(ascending=False))
# Pune      91000
# Mumbai    85000

Analysts usually combine Python with SQL and Excel. This guide to Excel vs SQL vs Python for data analysts explains when to use each.

Python for AI and machine learning

GitHub’s Octoverse 2025 report describes Python as dominant for AI and data science workloads. It also found that repositories using Jupyter Notebooks, the interactive Python environment used for analysis and AI experiments, grew 75% year on year to 2.4 million.

For a deeper look, read the role of Python in artificial intelligence, or explore the Data Analytics and Machine Learning and Generative AI courses.

Learn Python at Cyber Success, Pune

The Python Full Stack course in Pune at Cyber Success teaches Python for the uses employers hire for most:

• web back ends with Django and FastAPI
• SQL and data handling
• automation
• interview preparation

Learners build projects reviewed by trainers and receive placement assistance at the Deccan and Karve Nagar centres. Learners aiming at data or AI roles can continue with the Data Science course.

Call +91 9168665644 or email hello@cybersuccess.biz to book a free demo session.

Related reading

• Role of Python in Artificial Intelligence
• How to Learn Python for Free
• How to Practice Python: 15 Exercises With Solutions
• Python Tips and Tricks for Beginners
• Python Future Scope