Let us modify the minimum and maximum limit on each axis, by calling the set_xlim, set_ylim, and set_zlim methods. We have plotted data of 3 variables, namely, height, weight and age on the 3 axes.Īs you can see, the limits on the X, Y, and Z axes have been assigned automatically based on the input data. Heights = np.random.randint(130, 195, 35)Īx.scatter(xs = heights, ys = weights, zs = ages)Īx.set_title('Age-wise body weight-height distribution') Let us create another scatter plot representing a new set of data points, and then modify its axes range and interval.Īges = np.random.randint(low = 8, high = 30, size=35)

We can however alter them to our desired values. The range and interval of values on the axes are set by default based on the input values. We have used the parameters marker and c to change the style and color of the individual points Modifying the axes limits and ticks Let us begin by changing the color and style of the marker ax.scatter(xs,ys,zs, marker='x', c='red') We can alter the appearance of the markers to make them more expressive.

ax.set_xlabel('Atomic mass (dalton)')Īx.set_zlabel('Atomic velocity (x10⁶ m/s)')Īs we have seen in our previous examples, the marker for each point, by default, is a filled blue circle of constant size. 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. 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 panel.background = element_rect( fill = '#fbf9f4', color = '#fbf9f4'), plot.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. = element_blank(), = element_blank(), = element_line( color = '#b4aea9', linetype = 'dashed'), # Use a light color for the background of the plot and the panel. axis.text = element_text( size = 10, color = 'black'), axis.title = element_text( size = 12), axis.ticks = element_blank(), # Axis lines are now lighter than default axis.line = element_line( colour = 'grey50'), # Only keep y-axis major grid lines, with a grey color and dashed type. legend.text = element_text( size = 9, family = 'Roboto'), legend.title = element_text( face = 'bold', size= 12, family = 'Roboto'), legend.position = c( 1, 0), legend.justification = c( 1, 0), legend.background = element_blank(), # This one removes the background behind each key in the legend legend.key = element_blank(), # Customize title and subtitle font/size/color plot.title = element_text( family = 'Lobster Two', size = 20, face = 'bold', color = '#2a475e' ), plot.subtitle = element_text( family = 'Lobster Two', size = 15, face = 'bold', color = '#1b2838' ), = 'plot', # Adjust axis parameters such as size and color.

Plt <- plt + # Add axes labels, title, and subtitle labs( title = 'Palmer Penguins Data Visualization', subtitle = 'Scatter plot of flipper lenth vs bill length', x = 'flip length (mm)', y = 'bill length (mm)' ) + theme( # The default font when not explicitly specified text = element_text( family = 'Lobster Two', size = 8, color = 'black'), # Customize legend text, position, and background.

Label points scatter plot matplotlib