This is best visualized if the dots are transparent.Įxample 10: Create random arrays with 200 values for x-points, y-points, colors and sizes.Ĭolors = np.random.randint(200, size=(200)) You can combine a colormap with different sizes on the dots. Just like colors, make sure the array for sizes has the same length as the arrays forĮxample 9: Set your own size for the markers. You can adjust the transparency of the dots with the alpha argument. Just like colors, make sure the array for sizes has the same length as the arrays for the x- and y-axis.Įxample 8: Set your own size for the markers. You can change the size of the dots with the s argument. You can choose any of the built-in colormaps.Ĭlick Here to See the Options Available For ColorMaps Tthe Colormap can be includedin the drawing by including the plt.colorbar() statement. How to include the colormap in the drawing? Plt.scatter(x, y, c=colors, cmap='viridis') In addition you have to create an array with values (from 0 to 100), one value for each of the point in the scatter plot.Įxample 6: Create a color array, and specify a colormap in the scatter plot.Ĭolors = np.array() You can specify the colormap with the keyword argument cmap with the value of the colormap, in this case 'viridis' which is one of the built-in colormaps available in Matplotlib. This colormap is called 'viridis' and as you can see it ranges from 0, which is a purple color, and up to 100, which is a yellow color. There are a number of options available for colormaps in Matplotlib module.Ī colormap is like a list of colors, where each color has a value that ranges from 0 to 100. Note: You cannot use the color argument for this, only the c argument.Įxample 4: Set your own color of the markers.Ĭolors = np.array(["magenta","green","blue", You can even set a specific color for each dot by using an array of colors as value for the c argument. You can set your own color for each scatter plot with the color or the c argument.Įxample 3: Set your own color of the markers. The newer the Bike, the faster it drives. Note: The two plots are plotted with two different colors, by default blue and orange.īy comparing the 2 plots we can safely say that they both gives us the same conclusion. In the example above, there seems to be a relationship between speed and age, but what if we plot the observationsįrom another day as well? Will the scatter plot tell us something else?Įxample 2: Draw two plots on the same figure Given the fact is that only 13 Bikes are registered. It seems that the newer the Bikes, the faster it drives, but that could be a coincidence, The Y-axis shows the speed of the Bikes when it passes.Īre there any relationships between the observations? The observation in the example above is the result of 13 Bikes passing by. It needs two arrays of the same length, one for the values of the x-axis, and one for values on the y-axis. The scatter() function plots one dot for each observation. Scatter() function is used with Pyplot in order to draw a scatter plot. Python Data Science Matplotlib Scatter Plot the scatter() function How to Create Scatter Plots? Allah, Your Lord There Is No Deity Except Him.
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