![]() When we work with Subplots, we work with multiple Axes on one Figure. The pyplot module implicitly works on one Figure and one Axes at a time. In your code, you should make a distinction between each – you plot on a singular Axes but will store all the Axes in a Numpy array.Īn Axis refers to the XAxis or YAxis – the part that gets ticks and labels. In speech and writing use the same word for the singular and plural form. However, you can have multiple Axes ( Subplots) on a Figure. The word Axes refers to the area you plot on and is synonymous with Subplot. The typical variable names for each object are: However, it is worth mentioning here to explain where the term Axes comes from. In this tutorial, we’ll mostly control ticks, tick labels, and data limits through other mechanisms, so we won’t touch the individual Axis part of things all that much. These contain the ticks, tick locations, labels, etc. Usually we’ll set up an Axes with a call to subplots (which places Axes on a regular grid), so in most cases, Axes and Subplot are synonymous.Įach Axes has an XAxis and a YAxis. The axes is effectively the area that we plot data on and any ticks/labels/etc associated with it. You can have multiple independent figures and Figures can contain multiple Axes. It is the overall window/page that everything is drawn on. The Figure is the top-level container in this hierarchy. Let’s look at an image that explains the main classes from the AnatomyOfMatplotlib tutorial: But to draw multiple plots on one Figure, you need to learn the underlying classes in matplotlib. These work nicely when you draw one plot at a time. Up until now, you have probably made all your plots with the functions in matplotlib.pyplot i.e. # Access third Subplot and plot cube numbers # Access second Subplot and plot square numbers # Access first Subplot and plot linear numbers # Generate Figure object and Axes object with shape 3x1įig, axes = plt.subplots(nrows=3, ncols=1) # Import necessary modules and (optionally) set Seaborn style Finally, call plt.show() to display your plot. Once all Subplots have been plotted, call plt.tight_layout() to ensure no parts of the plots overlap. Access each Subplot using Numpy slice notation and call the plot() method to plot a line graph. The Numpy array axes has shape (nrows, ncols) the same shape as the grid, in this case (3,) (it’s a 1D array since one of nrows or ncols is 1). This creates a Figure and Subplots in a 3×1 grid. fig, axes = plt.subplots(nrows=3, ncols=1) Specify the number of rows and columns you want with the nrows and ncols arguments. The plt.subplots() function creates a Figure and a Numpy array of Subplot/ Axes objects which you store in fig and axes respectively. Let’s start with the short answer on how to use it-you’ll learn all the details later!
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