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6 Reasons Why Python Is Best for Apps Using AI, ML and Data Analytics

By: Dan M
  |  March 15, 2023
6 Reasons Why Python Is Best for Apps Using AI, ML and Data Analytics

There are a variety of technology stacks for Artificial intelligence (AI), Machine learning (ML) and data analytics applications. However, the ideal programming language for AI must be powerful, scalable and readable. All three conditions are met by the Python programming language.

With outstanding libraries, tools and frameworks for AI, ML and data analytics, Python has proven success leveraging all three technologies. Python caters to complex AI and ML modules, making it ideal for ML and AI-driven applications. And because of its simplicity and flexibility, Python enables programmers to increase their confidence and productivity, while leveraging these technologies.

These elements overall contribute to the popularity of Python and make it the most successful AI programming language.

Why Python Is So Successful

Let’s take a closer look into the reasons that have made Python so successful with these three technologies.

1. Rapid Development and Readability

Python aids rapid development because developers can readily comprehend what’s going on in the application, even when they join in the middle of a project. Developers have little trouble comprehending, changing, copying or pasting peer code with Python, since it is simple to read and understand. 

When using Python, there are no ambiguities, mistakes, or inconsistencies in paradigms. This makes it easier for AI, ML specialists, and data analysts to share algorithms, tools and ideas. Python code can also easily be optimized. Other capabilities, including testing, debugging, and tab completion, are available in tools like IPython to help you streamline your process.

2. Flexibility

Python is a flexible programming language and allows developers to choose between utilizing object-oriented programming (OOP) and scripting. Developers can also use Python with other languages to get the desired outcome, which reduces the probability of mistakes. Python further eliminates the need to recompile source code, making results easily visible.

Because of its adaptability, Python gives programmers the most freedom when creating related applications. For example, when creating an ML model, Python provides simple frameworks like Keras to train the ML model. ML models can then be deployed and monitored with MLOps platforms to ensure continuous model monitoring and management of ML models.

3. Community Support

Like many open-source programming languages, Python has a broad and active developer community. Programmers can use forums to express their problems and engage with other programmers to find solutions. The global footprint of Python communities means programmers can usually get assistance 24/7. Therefore, expert help is never far away when fixing bugs in AI, ML, and data analytics application development.

Among the most helpful and active Python communities and organizations are, GitHub and Stack Overflow.

4. AI, ML and Data Analytics Libraries

Python is great for AI, ML & data analytics apps

The list of AI, ML and data analytics libraries available for Python is extensive. Here are some of the most popular Python libraries for AI, ML, and data analytics:

  • Scikit-learn handles basic ML algorithms such as clustering, regression and classification.
  • Pandas is a programming language that may be used to do complex data analysis and organizing. It enables you to mix and filter data, as well as obtain information from other sources (such as Excel).
  • Keras is a framework for deep learning. It makes use of both the GPU and the CPU of the computer, allowing for rapid computations and prototyping. 
  • TensorFlow is a tool for developing, training and employing artificial neural networks with enormous data sets.
  • NumPy is a Python programming language specifically for data science computations. It’s a fantastic multi-dimensional array object library written in Python that enables you to perform scientific calculations. NumPy and Python work together to reduce the computational complexity of the software.
  • Matplotlib is a Python library for plotting data. It comes with data visualization and exploration tools that help you easily create charts, histograms, scatter plots and more. Matplotlib facilitates the quick manipulation of data to customize Python AI applications by creating visual representations in a short time.
  • Additional Python libraries like NLTK, PyBrain, Caffe and others aid the smooth operation of AI applications.

5. Python is Simple to Learn

Python is a straightforward programming language, generating code that is short, easy to read and understand. In fact, many developers consider Python to be the most intuitive of all programming languages.

The language’s simplicity makes it easy to construct trustworthy AI and ML models and systems. Rather than worrying about the language’s technical details, developers can focus entirely on solving ML, AI or data analytics challenges.

6. Visualization Capabilities

As noted earlier, Python has a lot of libraries, including several with great visualization tools. When building data science applications, for example, programmers can leverage tools like Matplotlib to better comprehend, present and visualize data. Data is the most important aspect of ML, AI, and Deep Learning algorithms, and visualizing data helps to find patterns, tracking data online and on social media sites.

Histograms, charts and graphs help developers see how data will interact and collaborate. APIs allow you to build extensive data reports, which makes the visualization process easier. In addition, the visualization process is aided by a variety of application programming interfaces, which make it easier to create concise reports.


Python is a programming language that has been around for a long time and continues to demonstrate staying power. With the processing power and scalability to deal with massive amounts of data requests, Python is a highly effective language for AI, ML and data analytics applications, while still being highly flexible and easy to learn.

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