2. Jupyter Notebook¶

The Jupyter Notebook is an ideal tool for exploratory data analysis, by using which you can finish data processing, computation, plotting and even writing (using Markdown) in one stop. Also, it supports more programming languages than just Python: Julia, R, etc.. In this course, we will only use Python for its well developed ecosystem in data science and scientific computing.

Below we only cover several key points for using Jupyter Notebooks A more complete tutorial can be found here.

2.1. Jupyter Notebook Extensions¶

In addition to the Jupyter Notebook itself, we recommend Jupyter Notebook Extensions as well, which can further enhance your productivity in the Jupyter Notebook environment. A good guide can be found here.

Among a variety of extensions, the following are considered essential and particularly useful for this course, and many other data-analysis-oriented ones:

Table of Contents: this extension helps building well-structured notebooks and allows easier navigation.

Collapsible Headings: this tool further enhances the power of the above one by allowing collasible sections, particularly useful for large notebooks.

ExecuteTime: this gadget reports the executation time of each cell so you can always have a measure of the code efficiency.

Code prettify: this tool quickly formats your Python code with a pretty and tidy layout.

2.2. Best Practices¶

A good guide can be found here.

And we emphasise that structuring your notebook is the key. This post helps you set up your Notebook with a good structure.