Creating Data Visualizations

Step 1: Importing tools and data

Before we can begin creating data visualizations in python, we should first import the tools we need, as well as read in the dataset. To do so, we first import pandas, a tool that allows for data analysis and manipulation. Then, we import the data that we would like to create visualizations with and read it with read_csv, which is part of pandas. We will also import numpy and matplotlib, which are needed for creating graphs.

This is a test. Some exciting text in my text file.

I’m an awesome PIC16B student and I am running Jekyll!

In this post, we’ll get set up with Jekyll. Jekyll is a static site converter, which you can use to turn plaintext documents into attractive webpages.

Fortunately, it’s pretty easy to embed interactive HTML figures produced via Plotly on your blog. Just use plotly.io.write_html() to save your figure. Then, copy the resulting HTML file to the _includes directory of your blog. Finally, place the code

It is possible to construct, maintain, and update your blog fully from GitHub. In this case, it is not necessary to download your blog’s files or modify them on your computer. However, when constructing complex posts involving code and figures, local editing can be more comfortable. Additionally, since GitHub Pages usually takes a few minutes to publish all your changes, modifying your blog locally allows you to more quickly see the results of your changes, including errors when they arise. In this post, I’ll show how to manage your blog locally.

In this post, I’ll show how to create a helpful histogram of some synthetic data.

In this post, we’ll see some examples of how to create technical posts that include Python code, explanatory text, and notes about your learnings. We’ll go over the primary methods that you’ll use to embed content in your posts.