Learn how to work with the vibrant Python Notebooks.
This repository houses a set of Python 3.8 Chapters written in the beautifully simple IDE Jupyter Notebook. You may either use the Jupyter Notebook interface or Google Colab to view, edit and execute these notebooks. As Notebooks are rendered in the browser as web applications, they can support a wide range of frontend elements, including cells (code and markdown), graphics, animation and interactivity. This makes the Python Notebook a much more interesting platform to learn python than the other IDEs.
Jupyter initializes a local server running python at the backend (on your own computer), and renders the frontend through a Notebook environment at localhost:8888. Keep the backend server (generally runs on your terminal for Linux/Mac or Anaconda Prompt for Windows) running so that the frontend notebook interface can access Python.
In terms of this course, the easiest way to install Python 3.8, Jupyter, and all relevant packages for computation is to install the entire Anaconda platform for Python 3.8. Once you have installed Anaconda, you will find Jupyter installed within the platform.
- In Linux/Mac, open
terminaland typejupyter notebookto access the platform. - In Windows, you may simply search-and-click Jupyter Notebook in your standard applications, or open the Anaconda Prompt and type
jupyter notebookto begin.
In case installing Anaconda is too heavy for you, and you already know how to manage
condaenvironments and packages, you may also download and install MiniConda (https://docs.conda.io/en/latest/miniconda.html). It's much lighter in footprint, but you will have to install all required data science packages individually, throughconda.
You may download the Notebooks (Chapters) posted in this repository (or git clone the entire repository), store them in a dedicated folder on your computer, access the folder through Jupyter Notebook navigation tree, and open the Notebooks individually. Get yourself familiar with the Jupyter Notebook environment, and the basic Python syntax in Chapter 0. Then, feel free to explore the rest of the Chapters in sequence.
Chapter 0 : Python in Jupyter Notebook
Chapter 1 : Basic Data Types in Python
Chapter 2 : Data Structures in Python
Chapter 3 : Conditions and Loops in Python
Chapter 4 : Functions and Chapters in Python
Chapter 5 : Computing with Python
Chapter 6 : Data Handling with Python
Chapter 7 : Basic Data Science in Python
Chapter 8 : Classes and Objects in Python
Chapter X : Miscellaneous Topics in Python
Google Colab offers a wonderful cloud platform to host, edit, run and share your Python Notebooks. The look and feel is similar to Jupyter Notebooks, and you can use the same Notebooks on both the platforms. In case you do not want to install Anaconda on your computer, but still want to access all data science packages, Google Colab comes with almost all of them pre-installed. In addition, it offers you free (but limited) access to GPUs for high-performance computing requirements. You must check it out!
Visit the Google Colaboratory website, and start exploring on your own.
License Declaration : Following the lead from the inspirations for this material, and the spirit of Python education and development, all Chapters of this work are licensed under the Creative Commons Attribution 3.0 Unported License.