Day 0 — Python Notebooks Unveiled 📚🐍

Welcome, coding explorers! Let’s dive into the world of Python and data science with Day 0 — Notebooks for Python 📓🐍

Agenda:

• Introduction to Notebooks
• Meet Jupyter Notebook
• Why Choose Jupyter?
• Installing Jupyter on Windows
• Discover Google Colab
• Colab’s Benefits for Coders
• Getting Started with Colab

1. The Notebook Magic: Notebooks are your canvas for ideas and code. 📝

2. Jupyter Notebook Magic Jupyter Notebook: Your code, equations, and insights in one place. 🌐

3. Why Jupyter Rocks: Interactive, versatile, and perfect for teaching. 🌟

4. Jupyter on Windows: Easy steps to set up JupyterLab on Windows. 🛠️

5. Google Colab Wonders: Explore Google Colab: Coding in the cloud. ☁️

6. Colab’s Offerings Colab gifts: Python, math, teamwork, datasets, and more. 🎁

7. Let’s Colab! Discover Colab through our quick video. 🚀

A notebook is a place to collect thoughts and ideas, a place to record memories, a place to take risks, and a place to play.

We use notebooks while learning, and studying to keep records and to note down important points. To learn Python and data science concepts we can also use the notebooks developed to execute the Python code.

What is Jupyter Notebook?

Born out of IPython in 2014, Jupyter Notebook has seen an enthusiastic adoption among the data science community, to the extent that it has become a default environment for research.

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. Uses include data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more.

— description from Project Jupyter

Project Jupyter’s name is a reference to the three core programming languages supported by Jupyter, which are Julia, Python, and R, and also a homage to Galileo’s notebooks recording the discovery of the moons of Jupiter.

One analysis of the code-sharing site GitHub counted more than 2.5 million public Jupyter notebooks in September 2018, up from 200,000 or so in 2015.

Why use Jupyter Notebook?

• Jupyter supports over 40 programming languages, including Python, R, Julia, and Scala.
• Notebooks can be shared with others using email, Dropbox, GitHub, and the Jupyter Notebook Viewer.
• Your code can produce rich, interactive output: HTML, images, videos, LaTeX, and custom MIME types.
• Leverage big data tools, such as Apache Spark, from Python, R, and Scala. Explore that same data with pandas, sci-kit-learn, ggplot2, and TensorFlow.
• Jupyter is well-suited to demonstrations of programming concepts and training.

How to install Jupyter Notebook on Windows?

JupyterLab can be installed using conda or pip. For more detailed instructions, consult the installation guide.

1. Conda: If you use conda you can install a jupyter notebook using the below command.

conda install -c conda-forge jupyterlab

2. pip: If you are using a pip installer, you can use the command as.

pip install jupyterlab

What is an alternative to the Jupyter Notebook?

Google has created a cloud-based Jupyter notebook environment called Colab. To be precise, Colab is a free Jupyter Notebook environment that runs entirely in the cloud.

Most importantly, it does not require setup and the notebooks that you create can be simultaneously edited by your team members — just the way you edit documents in Google Docs. Colab supports many popular machine learning libraries which can be easily loaded in your notebook.

Get started writing the Python code with Colab, with zero installation.

What colab offers to the programmer?

As a programmer or data scientist or researcher, you can perform the below activities on colab:

1. Write and Execute code in Python.
2. You can use Text cells to document your code and add and write different mathematical equations or problem statements.
3. You can upload, create, save, and share notebooks with team members and others.
4. You can upload these notebooks to google drive and GitHub.
5. Import external datasets from a site like Kaggle or import local data files.
6. Integrate modules like Pytorch, TensorFlow, Keras, etc.

How to use Colab?

Please check the below small introductory video for colab.

We will be using Google Colab extensively for our development.

Summary

We looked into the development environment and different notebooks for Python. Practice some operations with colab so that you have some knowledge and hands-on experience with the colab environment.

We will start writing small codes to perform small tasks from the next articles.