How to Install Python on PC or Web Host

To install Python on your system, follow the instructions for your specific operating system:


  1. Download the latest Python installer for Windows (either the 32-bit or 64-bit version, depending on your system).
  2. Run the installer.
  3. In the installer, check the box next to “Add Python to PATH” to automatically update your system’s PATH variable.
  4. Choose the “Customize installation” option if you wish to change any installation settings, or simply click on “Install Now” to proceed with the default settings.
  5. Wait for the installation to complete.


  1. Download the latest Python installer for macOS.
  2. Run the installer and follow the on-screen instructions.
  3. Wait for the installation to complete.


Most Linux distributions come with Python pre-installed. However, you might want to install a different version or update your current version. The following instructions cover installing Python on popular Linux distributions:

Debian/Ubuntu-based systems:

  1. Open a terminal.
  2. Update the package list: sudo apt update
  3. Install Python 3: sudo apt install python3
  4. (Optional) Install Python 2: sudo apt install python


  1. Open a terminal.
  2. Update the package list: sudo dnf update
  3. Install Python 3: sudo dnf install python3
  4. (Optional) Install Python 2: sudo dnf install python


  1. Open a terminal.
  2. Update the package list: sudo yum update
  3. Install Python 3: sudo yum install python3
  4. (Optional) Install Python 2: sudo yum install python

After installing Python, you can check the installed version by running python --version or python3 --version in your terminal or command prompt. If you need help then contact python hosting providers.



Python system requirement

Operating System:

Python is compatible with a wide range of operating systems, including:

  1. Windows: Windows 7, 8, and 10, Windows Server 2008 R2 and later
  2. macOS: macOS 10.9 (Mavericks) and later
  3. Linux: Most modern Linux distributions, such as Ubuntu, Debian, Fedora, CentOS, and openSUSE
  4. Other Unix-based systems: FreeBSD, OpenBSD, AIX, Solaris, etc.

Hardware Requirements:

Python itself has very low hardware requirements. However, the actual requirements depend on the applications you’re developing or running with Python. Here are some general guidelines:

  1. CPU: A modern x86 or x64 processor
  2. RAM: At least 512 MB (1 GB or more recommended for development and running larger applications)
  3. Disk Space: At least 100 MB for the Python installation (more space required for additional libraries and your own code)

Software Requirements:

Python has some additional software requirements, depending on your operating system:

  1. Windows: Visual C++ redistributable packages may be required for some Python extensions.
  2. Linux: Most Linux distributions come with Python pre-installed, but you might need to install some additional dependencies (e.g., build-essential on Ubuntu/Debian-based systems) to compile and install certain Python libraries.
  3. macOS: The latest version of Xcode and the associated command-line tools may be required for compiling and installing some Python libraries.


  1. NumPy: A library for numerical computing, providing support for arrays, matrices, and mathematical functions.
  2. Pandas: A data manipulation library that provides data structures like DataFrames for handling and analyzing structured data.
  3. Matplotlib: A plotting library for creating static, interactive, and animated visualizations in Python.
  4. SciPy: A library for scientific computing that builds on NumPy and provides additional functionality, such as optimization, linear algebra, and signal processing.
  5. Scikit-learn: A machine learning library that includes various algorithms for classification, regression, clustering, and dimensionality reduction.
  6. TensorFlow: An open-source machine learning framework developed by Google, used for training and deploying deep learning models.
  7. Keras: A high-level neural networks API that can run on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit (CNTK), making it easier to design, train, and evaluate deep learning models.
  8. Flask: A lightweight web framework for building web applications in Python.
  9. Django: A high-level web framework for developing robust, scalable web applications in Python, following the model-view-controller (MVC) architectural pattern.
  10. Requests: A library for making HTTP requests, providing a simple and user-friendly API for interacting with web services.
  11. Beautiful Soup: A library for web scraping, allowing you to parse HTML and XML documents and extract data from websites.
  12. Selenium: A browser automation library, commonly used for web scraping and testing web applications.
  13. PyTorch: An open-source machine learning library developed by Facebook, providing tensor computation and deep neural networks.
  14. NLTK (Natural Language Toolkit): A library for working with human language data (text), providing tools for text processing, tokenization, stemming, and other natural language processing tasks.
  15. spaCy: Another library for natural language processing, designed for large-scale information extraction tasks and focused on performance.
  16. SQLAlchemy: A SQL toolkit and Object-Relational Mapping (ORM) library for Python, allowing you to work with relational databases in a Pythonic way.
  17. Redis: A Python client for the Redis key-value store, enabling interaction with Redis databases from Python code.
  18. Pillow: A library for image processing in Python, providing support for various image file formats and operations like cropping, resizing, and filtering.



  1. What is Python? Python is a high-level, interpreted programming language that emphasizes code readability and simplicity. It is used for a wide range of applications, including web development, data analysis, artificial intelligence, scientific computing, and more.
  2. Who created Python? Python was created by Guido van Rossum in the late 1980s, and its development has been guided by the Python Software Foundation (PSF) since 2001.
  3. What are the advantages of using Python? Python’s main advantages include its simplicity, readability, versatility, and large ecosystem of libraries and tools. It is also easy to learn and has a strong community of developers.
  4. What is the difference between Python 2 and Python 3? Python 2 and Python 3 are two major versions of the language that are not fully compatible. Python 3 is the newer version and has several improvements over Python 2, such as better Unicode support, improved syntax, and more consistent handling of errors. However, some older libraries and tools still require Python 2.
  5. How do I install Python? Python can be installed on various operating systems. To install Python, download the appropriate installer for your operating system from the official Python website, run the installer, and follow the instructions.
  6. What are Python modules and packages? Python modules are individual files that contain Python code and can be imported into other Python scripts to reuse their functionality. Packages are collections of related modules that are organized into a directory structure and can be installed and managed using tools like pip.
  7. What are Python virtual environments? Python virtual environments allow you to create isolated environments with their own dependencies and settings, separate from the system’s Python installation. This makes it easier to manage and test different versions of packages and dependencies.
  8. What are Python decorators? Python decorators are a syntax feature that allow you to modify or extend the behavior of functions or classes without changing their source code. They are a powerful tool for adding functionality to existing code without cluttering it with additional logic.
  9. What are Python generators? Python generators are a type of iterable, similar to lists or tuples, but with the ability to generate values on the fly, without storing them all in memory at once. This makes generators useful for working with large datasets or infinite sequences.
  10. What is the difference between a list and a tuple in Python? Lists and tuples are both types of sequences in Python, but lists are mutable (can be modified) while tuples are immutable (cannot be modified). Tuples are typically used for fixed data that will not change, while lists are more flexible and can be modified as needed.
  11. What is a Python lambda function? A lambda function is a small, anonymous function in Python that can take any number of arguments, but can only have one expression. Lambda functions are commonly used for short, one-time operations that don’t require a full function definition.
  12. What is the Python Global Interpreter Lock (GIL)? The GIL is a mechanism in Python that ensures only one thread can execute Python bytecode at a time, even on multi-core systems. This means that Python’s threading model is not ideal for CPU-bound tasks, but is still useful for I/O-bound tasks.
  13. What are Python iterators? Python iterators are objects that implement the iterator protocol, allowing them to be used in for loops and other iteration contexts. Iterators are commonly used for processing large datasets or infinite sequences.
  14. What is the Python Standard Library? The Python Standard Library is a collection of modules that come with Python and provide a wide range of functionality, including file.




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