When you see informative charts and infographics, what is your first reaction? I wonder how you decide which chart type to use.

In this issue, I will share some of the best resources I found.

The Extreme Presentation Method by Dr. Andrew V. Abela is probably the most popular Chart decision tree around. The first step is to focus on your purpose. What do you want to show: Comparison, Distribution, Composition, Relationship. And then pick a chart type depending upon the number of variables, and whether one of them is time.

This works quite well in picking a reasonably good chart, though there has been some critique too.

Here is another popular visualization decision tree. It too starts with the purpose. It appears a bit more complex than the previous method, but is still quite simple and straightforward.

Another way to start with the type of the data: whether you have discrete categories, ordinal data, or continuous data. Though this method to choose a visualization appears to be a bit more complex, it is mostly due to the details.

Here is another (and simpler table) for how to choose your visuals:

From Data to Viz decision tree guide that “leads you to the most appropriate graph for your data.” It has quite a rich set of charts. The decision tree is a bit of a hybrid, it uses data type as well as broad application (map, network, time-series) to select the right visualization.

Datacamp Data Viz Cheatsheet uses the purpose as the first decision point and is more descriptive.

Apart from these decision trees, I do look at the following when doing data visualization using Python:

Over a period of time, you most likely will have your favorite charts at your figure tip.

Sometimes, it is better to have an interactive data app instead of a. static chart. Following are the most popular tools for that purpose: