Python Tutorials · Python Matplotlib

Matplotlib Scatter

Learn all about Matplotlib Scatter in this comprehensive tutorial.

5 min read intermediate

•With Pyplot, you can use the scatter() function to draw a scatter plot.
•In the example above, there seems to be a relationship between speed and age, but what if we plot the observations from another day as well?
•You can set your own color for each scatter plot with the color or the c argument:
•You can even set a specific color for each dot by using an array of colors as value for the c argument:
•The Matplotlib module has a number of available colormaps.
•You can change the size of the dots with the s argument.
•You can adjust the transparency of the dots with the alpha argument.
•You can combine a colormap with different sizes of the dots.

Creating Scatter Plots

With Pyplot, you can use the scatter() function to draw a scatter plot.

The scatter() function plots one dot for each observation. It needs two arrays of the same length, one for the values of the x-axis, and one for values on the y-axis:

python

import matplotlib.pyplot as pltimport numpy as npx = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])val_2 = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])plt.scatter(val_1, val_2)plt.show()

The observation in the example above is the result of 13 cars passing by.

The X-axis shows how old the car is.

The Y-axis shows the speed of the car when it passes.

Are there any relationships between the observations?

It seems that the newer the car, the faster it drives, but that could be a coincidence, after all we only registered 13 cars.

Compare Plots

In the example above, there seems to be a relationship between speed and age, but what if we plot the observations from another day as well? Will the scatter plot tell us something else?

python

import matplotlib.pyplot as pltimport numpy as np#day one, the age 
  and speed of 13 cars:val_1 = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
  val_2 = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])plt.scatter(val_1, 
  val_2)#day two, the age and speed of 15 cars:val_1 = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])val_2 = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
  plt.scatter(val_1, val_2)plt.show()

Note: Note: The two plots are plotted with two different colors, by default blue and orange, you will learn how to change colors later in this chapter.

By comparing the two plots, I think it is safe to say that they both gives us the same conclusion: the newer the car, the faster it drives.

Colors

You can set your own color for each scatter plot with the color or the c argument:

python

import matplotlib.pyplot as pltimport numpy as npx = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
  val_2 = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])plt.scatter(val_1, 
  val_2, color = 'hotpink')val_1 = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])val_2 = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
  plt.scatter(val_1, val_2, color = '#88c999')plt.show()

Color Each Dot

You can even set a specific color for each dot by using an array of colors as value for the c argument:

Note: Note: You cannot use the color argument for this, only the c argument.

python

import matplotlib.pyplot as pltimport numpy as npx = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
  val_2 = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])colors = np.array(["red","green","blue","yellow","pink","black","orange","purple","beige","brown","gray","cyan","magenta"])
plt.scatter(val_1, val_2, c=colors)plt.show()

ColorMap

The Matplotlib module has a number of available colormaps.

A colormap is like a list of colors, where each color has a value that ranges from 0 to 100.

Here is an example of a colormap:

This colormap is called 'viridis' and as you can see it ranges from 0, which is a purple color, up to 100, which is a yellow color.

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.

In addition you have to create an array with values (from 0 to 100), one value for each point in the scatter plot:

python

import matplotlib.pyplot as pltimport numpy as npx = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
  val_2 = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])colors = np.array([0, 
  10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])
plt.scatter(val_1, val_2, c=colors, cmap='viridis')plt.show()

You can include the colormap in the drawing by including the plt.colorbar() statement:

python

import matplotlib.pyplot as pltimport numpy as npx = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
  val_2 = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])colors = np.array([0, 
  10, 20, 30, 40, 45, 50, 55, 60, 70, 80, 90, 100])
plt.scatter(val_1, val_2, c=colors, cmap='viridis')plt.colorbar()plt.show()

You can choose any of the built-in colormaps:

Size

You can change the size of the dots with the s argument.

Just like colors, make sure the array for sizes has the same length as the arrays for the x- and y-axis:

python

import matplotlib.pyplot as pltimport numpy as npx = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
  val_2 = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])sizes = 
  np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])plt.scatter(val_1, 
  val_2, s=sizes)plt.show()

Alpha

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:

python

import matplotlib.pyplot as pltimport numpy as npx = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
  val_2 = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])sizes = 
  np.array([20,50,100,200,500,1000,60,90,10,300,600,800,75])plt.scatter(val_1, 
  val_2, s=sizes, alpha=0.5)plt.show()

Combine Color Size and Alpha

You can combine a colormap with different sizes of the dots. This is best visualized if the dots are transparent:

python

import matplotlib.pyplot as pltimport numpy as npx = 
  np.random.randint(100, size=(100))val_2 = np.random.randint(100, size=(100))
  colors = np.random.randint(100, size=(100))sizes = 10 * np.random.randint(100, 
  size=(100))plt.scatter(val_1, val_2, c=colors, s=sizes, alpha=0.5, cmap='nipy_spectral')
plt.colorbar()plt.show()

Module quiz

2 questions

Which of the following is true about Matplotlib Scatter?

It is strictly typedIt is evaluated at runtimeIt requires explicit memory managementIt is deprecated

What is the most common pitfall when working with Matplotlib Scatter?

SyntaxErrorIndentationErrorNameErrorTypeError

Answer all questions to submit.

Matplotlib Subplot

Matplotlib Bars

Matplotlib Scatter

AI Summary

Creating Scatter Plots

Compare Plots

Colors

Color Each Dot

ColorMap

Size

Alpha

Combine Color Size and Alpha

Module quiz