Why is Python used so much in AI? It is the glue between models, data and experiments

Open almost any AI tutorial and you will eventually see Python.

That can make beginners think, “Do I need to become a professional programmer before I can work with AI?”

No.

You need enough Python to express data, call functions, install libraries and read errors. You can learn deeper software engineering as your projects grow.

Why Python became popular in AI

Python is not the fastest language at every low-level operation.

Its strength is that it is easy to read and has a huge ecosystem of libraries that connect to fast code written underneath in C, C++, CUDA and other systems.

You can use Python as the workbench while optimized libraries perform the heavy numerical work.

The first concepts you actually need

A beginner should be comfortable with:

• variables,
• strings and numbers,
• lists and dictionaries,
• if statements,
• for loops,
• functions,
• importing packages,
• reading and writing files,
• basic exceptions.

That is enough to understand a surprising amount of AI example code.

Libraries are a major reason Python is useful

Common categories include:

• NumPy for numerical arrays,
• pandas for tabular data,
• PyTorch for tensor computation and deep learning,
• model libraries such as Transformers,
• provider SDKs for calling APIs,
• Jupyter for interactive notebooks,
• plotting and data-processing tools.

You are not expected to implement matrix multiplication from scratch every time.

What is a virtual environment?

Different projects may need different package versions.

A virtual environment gives one project an isolated Python package environment so that installing a library for Project A does not unnecessarily break Project B.

Tools such as venv, conda, uv or other environment managers solve this problem in different ways.

Jupyter notebooks are useful for experiments

A notebook lets you run code in cells, inspect variables and place explanation beside code.

That makes it convenient for data exploration and model experiments.

But notebooks can hide execution order and environment assumptions, so production code often moves into scripts or packages once the experiment stabilizes.

Python often talks to things outside Python

A Python program may:

• call an AI API,
• load a local model,
• read CSV or JSON,
• query a database,
• launch GPU kernels through a library,
• automate files,
• build a small web service.

That “glue” role is why it appears so often in AI work.

Learn by modifying small working programs

Do not wait until you have memorized the whole language.

Take a 20-line script, change the input, print intermediate values, deliberately cause an error and understand the traceback.

That loop builds practical fluency quickly.

One thing to remember

Python is popular in AI because it gives humans a readable interface to a huge ecosystem of data, model and automation tools.

Lesson 027 introduces three things you will meet as soon as projects leave a notebook: the command line, Git and GitHub.