Using an ESP8266 and some DS18B20 one-wire temperature sensors, I have been automatically recording temperature data from various parts of my pond, to see how it fluctuated with air temperature, depth and filter configuration.
Despite the help I was receiving from the feline fish monitor, I was getting a bit irked at the quality of the graphs I was getting using matplotlib.
Matplotlib has been around since 2003, more than 20 years now. It’s arguably the defacto method of producing graphs in python and it’s not going away. However, it’s also a pain to use and by default produces some quite ugly plots unless you put in the mileage. In fact, when attempting to quickly explore data, Michael L. Waskom’s frustrations with matplotlib were directly related to the production of the seaborn library. “By producing complete graphics from a single function call with minimal
arguments, seaborn facilitates rapid prototyping and exploratory data analysis.”
Seaborn makes use of matplotlib and integrates tightly with pandas provide a neat wrapper for matplotlib functions, allowing you to avoid a lot of the data herding needed to view a graph.
You may think “OK, so seaborn finally tames matplotlib, why should I use anything else?” In short, interactivity. Seaborn and Matplotlib may produce graphs, but a graph alone doesn’t really let you explore the data. If you look at a graph you’re limited to the scale the author thought made sense, you can’t zoom in or out and if one line is behind another, you’re kind of stuck.
Where plotly really shines is with just two lines you can generate your figure and then either save it as the image below, or as an interactive HTML graph such as this.
