1/16/2024 0 Comments Matplotlib subplotI 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. If you want to create multiple sub plots in a single figure to show different aspects of a data, then the subplots() function should be used.Sometimes its helpful to get back to basics. Let us understand the code of the live example which is given below in which we have plotted two sub plots. Let us cover a live example to understand this function in more detail. The output for the above code is as follows: With the given below code snippet, we will create a figure having 2 rows and 2 columns of subplots. Let us understand this method with the help of a few examples: Example 1: It can be an Axes object or an array of Axes objects. The values returned by these function are as follows:įig: This method is used to return the figure layout.Īx: This method is mainly used to return the axes. Matplotlib subplots() Function Returned Values This parameter is used to indicate the dict with keywords passed to the GridSpec constructor that is used to create the grid on which the subplots are placed on. This parameter is used to indicate the dict with keywords that are passed to the add_subplot call which is used to create each subplot. This optional parameter usually contains boolean values with the default is True. To control the sharing of properties among x (sharex) or among y (sharey) axis these parameters are used. The parameter nrows is used to indicate the number of rows and the parameter ncols is used to indicate the number of columns of the subplot grid. Let us discuss the parameters used by this function: The basic syntax to use this function is as follows: (nrows, ncols, sharex, sharey, squeeze, subplot_kw, gridspec_kw, **fig_kw) Matplotlib subplots() Function Parameters Various kind of subplots supported by matplotlib is 2x1 vertical, 2x1 horizontal or a 2x2 grid. The main objective of this function is to create a figure with a set of subplots. This function helps in creating common layouts of subplots and it also includes the enclosing figure object, in a single call. The subplots() function in the Matplotlib acts as a utility wrapper. In this tutorial, we will cover the subplots() function in the state-based interface Pyplot in the Matplotlib Library.
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