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When I create a single plot using the figure()-function of PyPlot, I can set the name of the appearing window using a string as argument: import matplotlib.pyplot as plt figure = plt.figure('MyName') The function plt.subplots() doesn´t accept strings as first argument. For example: plt.subplots(2,2) # for creating a window with 4 subplots
As of November 2020, in order to show one figure at a time, the following works: import matplotlib.pyplot as plt f1, ax1 = plt.subplots() ax1.plot(range(0,10)) f1.show() input("Close the figure and press a key to continue") f2, ax2 = plt.subplots() ax2.plot(range(10,20)) f2.show() input("Close the figure and press a key to continue")
Using one-liners to generate basic plots in matplotlib is fairly simple, but skillfully commanding the remaining 98% of the library can be daunting. This article is a beginner-to-intermediate-level walkthrough on matplotlib that mixes theory with examples.
Viewing the underlying data structure for any plotly.graph_objects.Figure object, including those returned by Plotly Express, can be done via print(fig) or, in JupyterLab, with the special fig.show("json") renderer.
We will discuss how to create Figures in more detail below, but first it is helpful to understand how to view a Figure. This varies based on how you are using Matplotlib, and what Backend you are using. Notebooks and IDEs# Screenshot of a Jupyter Notebook, with a figure generated via the default inline backend. #
This figure shows the name of several matplotlib elements composing a figure.
Throughout this book, we’ll commonly use the variable name fig to refer to a figure instance, and ax to refer to an axes instance or group of axes instances. Once we have created an axes, we can use the ax.plot function to plot some data.
