Python Virtual Environment.pptx

Editor's Notes

• #2 When developing software with Python, a basic approach is to install Python on your machine, install all your required libraries via the terminal and write all of your source code. This works fine for simple Python scripting projects.
• #3 But to implement complex software development projects, is required a Python interpreter, Python library, an API, or software development kit, often you will be working with multiple files, multiple packages, dependencies and as well as your source code. As a result, you will need to isolate your Python development environment for that particular project. This isolated environment is known as Virtual Environment. In this tutorial, I would like to present a clear explanation about Virtual Environment and how can you implement it on your python project.
• #4 For example, in your Local Machine you have installed Python 3.5.9. This Python version 3.5.9 is a Global Version for your computer.
• #5 As you know that python has thousands of “Third Party Library” such as Numpy 1.18.1, SciPy 1.9.1, Scikit-learn 1.1.2, TensorFlow 2.9.0, Pandas 1.4.4 and so on. You have installed anyone library as you want. I would like to called it “Global Third-Party Library”.
• #6 Now, you have created a python project “Application-A” by the Python version 3.5.9. And imagine, in your Local Machine you have installed Python 3.5.9. To implement this “Application-A”, you need to import some “Third Party Library” such Numpy version 1.18.1, SciPy version 1.9.1. You can import those “Third-Party Library” Numpy and SciPy from the “Globally installed Third-Party Library”. But problem is that with the over time being Python, Numpy and SciPy version can be changed. So “Application-A” source code can be interrupted if you update the Python, Numpy and SciPy.
• #7 Now, you have created a python project “Application-A” by the Python version 3.5.9. And imagine, in your Local Machine you have installed Python 3.5.9. To implement this “Application-A”, you need to import some “Third Party Library” such Numpy version 1.18.1, SciPy version 1.9.1. You can import those “Third-Party Library” Numpy and SciPy from the “Globally installed Third-Party Library”. But problem is that with the over time being Python, Numpy and SciPy version can be changed. So “Application-A” source code can be interrupted if you update the Python, Numpy and SciPy.
• #8 Now, you have created a python project “Application-A” by the Python version 3.5.9. And imagine, in your Local Machine you have installed Python 3.5.9. To implement this “Application-A”, you need to import some “Third Party Library” such Numpy version 1.18.1, SciPy version 1.9.1. You can import those “Third-Party Library” Numpy and SciPy from the “Globally installed Third-Party Library”. But problem is that with the over time being Python, Numpy and SciPy version can be changed. So “Application-A” source code can be interrupted if you update the Python, Numpy and SciPy.
• #9 Now, you have created a python project “Application-A” by the Python version 3.5.9. And imagine, in your Local Machine you have installed Python 3.5.9. To implement this “Application-A”, you need to import some “Third Party Library” such Numpy version 1.18.1, SciPy version 1.9.1. You can import those “Third-Party Library” Numpy and SciPy from the “Globally installed Third-Party Library”. But problem is that with the over time being Python, Numpy and SciPy version can be changed. So “Application-A” source code can be interrupted if you update the Python, Numpy and SciPy.
• #10 Now, you have created a python project “Application-A” by the Python version 3.5.9. And imagine, in your Local Machine you have installed Python 3.5.9. To implement this “Application-A”, you need to import some “Third Party Library” such Numpy version 1.18.1, SciPy version 1.9.1. You can import those “Third-Party Library” Numpy and SciPy from the “Globally installed Third-Party Library”. But problem is that with the over time being Python, Numpy and SciPy version can be changed. So “Application-A” source code can be interrupted if you update the Python, Numpy and SciPy.
• #11 Now, you have created a python project “Application-A” by the Python version 3.5.9. And imagine, in your Local Machine you have installed Python 3.5.9. To implement this “Application-A”, you need to import some “Third Party Library” such Numpy version 1.18.1, SciPy version 1.9.1. You can import those “Third-Party Library” Numpy and SciPy from the “Globally installed Third-Party Library”. But problem is that with the over time being Python, Numpy and SciPy version can be changed. So “Application-A” source code can be interrupted if you update the Python, Numpy and SciPy.
• #12 Consider another scenario, you have created another Python project “Application-B” by the Python version 3.7.10. In your Local Machine installed Python version is 3.5.9. And need to import “Third Party Library” such Numpy version 1.23.2, TensorFlow version 2.9.0. Here you can’t import globally installed Numpy third party library because of version mismatch. But can be import globally installed TensorFlow third party library as both version is same.
• #14 Python virtual environments give you the ability to isolate your Python development projects from your system installed Python and other Python environments. This gives you full control of your project and makes it easily reproducible.
• #15 Now time to implement the virtual environment for your python project. Here I would like to know you that virtual environment manager: either venv or virtualenv for Python. Each module is the preferred way to create and manage isolated virtual environments. venv is included in the Python standard library and requires no additional installation. Whereas virtualenv is includes in the Python standard library and some others additional library installation.