![]() This is how our input and output will look like in python: Plt.plot(c,d, label = "cos function", c = 'red') # Now the 3rd number will define the position of the 2 plots.Ĭode: plt.plot(a,b, label = "sin function") ![]() So, in our example, the first 2 numbers are telling that we will have a grid of 1 row and 2 columns. The first 2 numbers passed in the argument define the dimensions of the grid in which we want our plots to be displayed. (Let us understand what exactly the function subplot(1, 2, 1) is doing. We are now ready to create our subplots: plt.subplot(1, 2, 1) Let us take 2 functions, sine and cosine for this example. Let us now understand how to create subplots in python using Matplotlib: _subfigureĪdd a SubFigure to the figure as part of a subplot arrangement.How to Create Matplotlib Subplots in Python? We can define subfigures in matplotlib by specifying the number of rows and columns or by using NumPy slice syntax to specify the location of subfigures in GridSpec. There are two functions add_subfigure() and subfigures in matplotlib to add subfigures in matplotlib. How to Insert Subfigures in Matplotlibįirst, we have to create a figure to add the subfigures. We can also instantiate a subfigure inside a subfigure. Subfigures in Matplotlibįigure inside the figure (subfigures) can be instantiated using Figure.add_subfigure() or SubFigure.add_subfigure(), or SubFigure.subfigures(). Also, we can make nested gridspec or nested subfigures to place the subplots in groups. We can place the subfigures in the grid using the gridspec() method. Here comes the concept of subfigures, where we can place subfigures inside the figures and subplots inside the subfigures where we can change the properties of the certain group of the subplot of any subfigures. We don't have any individual control over each subplot. We can place the subplot on a figure, but if we change some properties of the figure, it will affect the whole plot. All the plots and properties are shown in the figure in matplotlib. The figure is the top-level container of all the axes and properties of a plot, or we can say that it is a canvas that holds the drawings (graphs or plots) on it. Subfigures are an important component in the matplotlib where we can place the subplots in a different layout. If we can plot the figures, why do we need subfigures? Need of Subfigures in Matplotlib We can also create a figure using the function () and change its visual properties by adding some parameters. In matplotlib, a default figure is automatically generated in the backend, where we can place our plots. Introductionįigures or subfigures are the canvas for the plots and subplots, and we can't plot without figures. Subplots and plot is the place where we can plot the data in matplotlib. Figures, Subfigures, and GridSpec only allot space on the canvas for the plots and subplots. We can also implement nested GridSpec or nested subfigures in matplotlib. ![]() GridSpec divides the area on the figure in terms of grids where we can place subfigures or subplots according to our needs. There are two methods to add subfigures to the figure or gridspec in matplotlib Figure.add_subfigure() and Figure.subfigures(). Matplotlib provides the libraries and function to add subfigures on a figure where we can place the subplots.
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