Python installation

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Revision as of 22:01, 2 November 2025 by Root (talk | contribs) (Created page with "= Installing python = == Installation on linux == Most modern Linux distributions include Python by default. To verify your version: <syntaxhighlight lang="bash"> python3 --version </syntaxhighlight> If it is not installed, you can install it using your package manager: <syntaxhighlight lang="bash"> sudo apt update sudo apt install python3 python3-pip </syntaxhighlight> == Installation on windows == Go to the official Python website: https://www.python.org/downloads...")
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Installing python

Installation on linux

Most modern Linux distributions include Python by default. To verify your version:

 python3 --version

If it is not installed, you can install it using your package manager:

sudo apt update
sudo apt install python3 python3-pip

Installation on windows

Go to the official Python website: https://www.python.org/downloads Download the latest Windows installer.

Run the installer and make sure to check the option Add Python to PATH.

After installation, verify it by opening a terminal (cmd or PowerShell):

 python --version

You can also install packages using pip:

 pip install package_name

Installing Jupyter Notebook

What is Jupyter

Jupyter Notebook is an interactive environment that allows you to combine live code, visualizations, and text in a single document. It is especially useful for data analysis, machine learning, and education.

Installation

Once Python and pip are installed, you can install Jupyter using:

 pip install jupyterlab

To start it:

 jupyter lab

This will open a web interface in your browser where you can write and execute Python code interactively.

When to use command line or Jupyter

Using the command line

  • Running scripts directly (python script.py)
  • Automating tasks and system operations
  • Working with projects that require performance or command-based workflows
  • Integrating with development tools like Git or Docker
  • Faster execution for scripts
  • Easier integration with automation
  • Lower resource usage

Using Jupyter

  • Data exploration and visualization
  • Prototyping and experimentation
  • Teaching and sharing results interactively
  • Interactive code execution
  • Easy visualization of data
  • Supports text, math, and graphics in one notebook