We can set a label for each axis in a 3D plot by calling the methods set_xlabel, set_ylabel and set_zlabel on the axes object. Let us now add labels to each axis on the plot. NOTE that I have not added the preceding code (to create the figure and add scatter plot) here, but you should do it. ax.set_title("Atom velocity distribution") We will call the set_title method of the axes object to add a title to the plot. Let us now add a title to this plot Adding a title We will use NumPy random seed so you can generate the same random number as the tutorial. Let us plot a scatter plot in 3D space and look at how we can customize its appearance in different ways based on our preferences. The z coordinate is simply the sum of the squares of the corresponding x and y coordinates. The x and y coordinates are generated using np.linspace to generate 50 uniformly distributed points between -4π and +4π. We are generating x, y, and z coordinates for 50 points. We will use the plot() method and pass 3 arrays, one each for the x, y, and z coordinates of the points on the line. Now that we know how to plot a single point in 3D, we can similarly plot a continuous line passing through a list of 3D coordinates. fig = plt.figure(figsize=(4,4))Īx.scatter(2,3,4) # plot the point (2,3,4) on the figureĪs you can see, a single point has been plotted (in blue) at (2,3,4). To plot a single point, we will use the scatter()method, and pass the three coordinates of the point. Step 3: Plot the pointĪfter we create the axes object, we can use it to create any type of plot we want in the 3D space. Note that these two steps will be common in most of the 3D plotting you do in Python using Matplotlib. We will use this axis object ‘ax’ to add any plot to the figure. We then create a 3-D axis object by calling the add_subplot method and specifying the value ‘3d’ to the projection parameter. Here we are first creating a figure of size 4 inches X 4 inches. Step 2: Create figure and axes fig = plt.figure(figsize=(4,4))Īx = fig.add_subplot(111, projection='3d') For versions 3.2.0 and higher, you can plot 3D plots without importing mpl_3D.
Note that the second import is required for Matplotlib versions before 3.2.0.
It is, otherwise, not used anywhere else. The second import of the Axes3D class is required for enabling 3D projections. The first one is a standard import statement for plotting using matplotlib, which you would see for 2D plotting as well. Step 1: Import the libraries import matplotlib.pyplot as plt
Let us begin by going through every step necessary to create a 3D plot in Python, with an example of plotting a point in 3D space. 3.4 Modifying the axes limits and ticks.
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