1/18/2024 0 Comments Pyplot enlarge subplot size![]() ![]() This can be very valuable when you want to generate multiple plots and need them all to be the same size. This allows you to produce multiple visualizations and only modify the size a single time. We can change the figure size in Matplotlib globally using the rcParams available. Changing Plot Size in Matplotlib Using rcParams In the next section, you’ll learn how to use the rcParams in Matplotlib to change plot sizes. We then declared a subplot axes and added our plot.We then used the set_figwidth() and set_figheight() functions to control the figure size.This returns the following image: Using fig_setheight and fig_setwidth to change matplotlib size Let’s see how we can use these functions to modify the size of our plot in Matplotlib: # Using Figure Functions to Change Matplotlib Chart Size While the previous section showed you how to change the size in one argument, these functions give you the control to modify these parameters individually. Matplotlib also provides ways to more explicitly define the height and width individually, using the set_fightheight() and set_figwidth() functions. Changing Plot Size in Matplotlib Using set_figheight and set_figwidth In the next section, you’ll learn how to use Matplotlib to set width and height individually. This returns the following image: Changing the DPI of a Matplotlib Figure Let’s change our DPI to 200 pixels per inch: # Changing the DPI of a Matplotlib Chart In order to mofidy this, you can use the dpi= parameter on the figure object. ![]() By default, Matplotlib uses a DPI of 100 pixels per inch. In order to finely tune your printable reports, you can also control the DPI of the charts your produce. Matplotlib allows you to prepare print-friendly reports. This can be done by multiplying a value in inches by 2.54 to calculate it in centimeters. If you’re more accustomed to working with centimeters, you first need to convert the measurement to centimeters. We passed in a tuple containing the width and the length in inches. In the code above, we assigned the figsize= parameter in order to specify the size of the figure we want to use. In the code above, we accessed the Figure that was created by default. This returns the following image: Using figsize to Change Matplotlib Figure Size Let’s take a look at how we can do this: # Changing the figure size using figsize= Because of this, we first need to instantiate a figure in which to host our plot. As the name of the argument indicates, this is applied to a Matplotlib figure. One of the simplest and most expressive ways of changing the plot size in Matplotlib is to use the figsize= argument. show() method to display the visualizationĬhanging Plot Size in Matplotlib Using figsize We then passed these variables into the plt.plot() function.We then used a Python list comprehension to define the y values as the square of each x value.We defined x values as the values from 1 through 10.This returns the following image: Loading a Sample Matplotlib Plot We’ll be using a simple plot for this tutorial, in order to really focus on how to change the figure size of your plot in Matplotlib. To follow along with this tutorial line by line, copy and paste the code below into your favourite code editor. Changing Plot Size in Matplotlib Using rcParams.Changing Plot Size in Matplotlib Using set_figheight and set_figwidth.Changing Plot Size in Matplotlib Using figsize.In Python, this looks like: t = np.arange (0,50,0.1) Sine waves are always fun, so let’s start by create a time array, $t$, and then a function $y$ that is a function of time and related to $t$ by $y = sin(t)$. First, I will import some packages: import numpy as np Let’s see how this works with an example. No I am trying to use plt.subplots_adjust to make my subplots look great. I used to use tight_layout, but that was never predictable and I didn’t really understand how it worked. The tricky part is getting all of the figure and plot parts spaced out in a readable manner. I often make figures with multiple plots, which is straightforward with the plt.subplots command. To avoid that in the future, I am going to use these “Today I Learned (TIL)” posts as notes for future Alejandro, so he doesn’t have to spend so much time re-discoverying how to make Matplotlib plots. This happens a lot when I try advanced plotting techniques in Python/Matplotlib. When it comes to programming, there are a number of tasks that I spend time learning by searching the web and then subsequently forgetting by the next time I need that skill, so that I have to search the web all over again.
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