# the second subplot will span one row and one columnĪx1.text(0.5, 0.5, 'This is axes object 1, gspec coordinates ', ha='center') # the first subplot will span one row and two columnsĪx0.text(0.5, 0.5, 'This is axes object 0, gspec coordinates ', ha='center') Gspec = fig.add_gridspec(nrows=2, ncols=3) The units of this padding are in percentages of the default font size. The argument “pad” adds padding between subplots and the figure boarders, while “h_pad” and “w_pad” add height and width padding between subplots. If we want to add some extra padding between subplots, we can add some arguments to override the default tight_layout parameters. This will create the following set of plots: Finally, we’ll use the Axes object to generate the plot:įig, ,] = plt.subplots(nrows=2, ncols=2)Īx0.text(0.5, 0.5, 'This is axes object 0', ha='center')Īx1.text(0.5, 0.5, 'This is axes object 1', ha='center')Īx2.text(0.5, 0.5, 'This is axes object 2', ha='center')Īx3.text(0.5, 0.5, 'This is axes object 3', ha='center') Next we access this Axes object using the Figure object’s “gca” method, which stands for “get current axes”. First, we’ll generate a Figure object using Pyplot which will automatically create an axes object behind the scenes. To create the plot above using Matplotlib’s object oriented syntax we need to make a few small modifications to the original code. Figure objects can contain one or multiple Axes objects, each representing an individual chart (this can be a big source of confusion when learning Matplotlib, the term “axes object” is inherited from Matlab and refers to an entire chart rather than a x or y axis). I think of this as the blank canvas that the plots will be generated on. Figure objects represent the containers that hold a visualization. By directly accessing and working with this object oriented structure we can create highly customized visualizations.įor the purposes of this blog, there are two Matplotlib objects that are important: Figure objects and Axes objects. Luckily, the Matlab style syntax is simply hiding an object oriented code structure that is at the core of Matplotlib. While the Matlab style syntax is easy to use, it is actually quite limiting in its ability to create custom visualizations. For example, to create a simple line plot we can use the following code: As such, one way we can create visualizations using Matplotlib is through “Matlab style” syntax which is contained within Matplotlib’s flagship module, Pyplot. As it’s name suggests, Matplotlib was originally created as a means of replicating Matlab style plotting functionality in Python. A brief introduction to Matplotlib’s object oriented syntax

Since Python is an open source language, there are multiple ways of creating and working with subplots, so in this post I’ll outline a few ways that work for me, and provide some context about how things work behind the scenes in Matplotlib.

I’ve chosen to devote this post to making subplots, a task often necessary for scientific visualizations that can be surprisingly frustrating if you don’t understand Matplotlib’s structure. With this in mind, I’m writing this post to serve as a guide for those new to plotting with Matplotlib, and to help fill in some gaps for those who are already experienced Matplotlib users. While constructing the summer course, I realized how much time I could have saved if I had learned how Matplotlib worked earlier in my PhD. I don’t think my experience is uncommon, creating basic visualizations in Python is fairly straight forward, and a quick Google search will yield tutorials on how to make most common plot types.

Over the course of my graduate career I’ve taken several courses on programming and Python, and each time visualization or plotting was viewed as an afterthought. I recently created a summer course on data visualization with Python, and the experience made me realize that the workings of Python’s main visualization library, Matplotlib, are often left of out formal Python courses. Sometimes its helpful to get back to basics.

Matplotlib subplot smaller