Matplotlib | Scatter

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:

Example

A simple scatter plot:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])

plt.scatter(x, y)
plt.show()

Result:

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?

Example

Draw two plots on the same figure:

import matplotlib.pyplot as plt
import numpy as np

#day one, the age and speed of 13 cars:
x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
plt.scatter(x, y)

#day two, the age and speed of 15 cars:
x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
plt.scatter(x, y)

plt.show()

Result:

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:

Example

Set your own color of the markers:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = np.array([99,86,87,88,111,86,103,87,94,78,77,85,86])
plt.scatter(x, y, color = ‘hotpink’)

x = np.array([2,2,8,1,15,8,12,9,7,3,11,4,7,14,12])
y = np.array([100,105,84,105,90,99,90,95,94,100,79,112,91,80,85])
plt.scatter(x, y, color = ‘#88c999’)

plt.show()

Result:

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

Example

Set your own color of the markers:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = 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(x, y, c=colors)

plt.show()

Result:

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, and up to 100, which is a yellow color.

How to Use the ColorMap

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 of the point in the scatter plot:

Example

Create a color array, and specify a colormap in the scatter plot:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = 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(x, y, c=colors, cmap=’viridis’)

plt.show()

Result:

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

Example

Include the actual colormap:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = 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(x, y, c=colors, cmap=’viridis’)

plt.colorbar()

plt.show()

Result:

Available ColorMaps

You can choose any of the built-in colormaps:

Name Reverse
Accent Accent_r
Blues Blues_r
BrBG BrBG_r
BuGn BuGn_r
BuPu BuPu_r
CMRmap CMRmap_r
Dark2 Dark2_r
GnBu GnBu_r
Greens Greens_r
Greys Greys_r
OrRd OrRd_r
Oranges Oranges_r
PRGn PRGn_r
Paired Paired_r
Pastel1 Pastel1_r
Pastel2 Pastel2_r
PiYG PiYG_r
PuBu PuBu_r
PuBuGn PuBuGn_r
PuOr PuOr_r
PuRd PuRd_r
Purples Purples_r
RdBu RdBu_r
RdGy RdGy_r
RdPu RdPu_r
RdYlBu RdYlBu_r
RdYlGn RdYlGn_r
Reds Reds_r
Set1 Set1_r
Set2 Set2_r
Set3 Set3_r
Spectral Spectral_r
Wistia Wistia_r
YlGn YlGn_r
YlGnBu YlGnBu_r
YlOrBr YlOrBr_r
YlOrRd YlOrRd_r
afmhot afmhot_r
autumn autumn_r
binary binary_r
bone bone_r
brg brg_r
bwr bwr_r
cividis cividis_r
cool cool_r
coolwarm coolwarm_r
copper copper_r
cubehelix cubehelix_r
flag flag_r
gist_earth gist_earth_r
gist_gray gist_gray_r
gist_heat gist_heat_r
gist_ncar gist_ncar_r
gist_rainbow gist_rainbow_r
gist_stern gist_stern_r
gist_yarg gist_yarg_r
gnuplot gnuplot_r
gnuplot2 gnuplot2_r
gray gray_r
hot hot_r
hsv hsv_r
inferno inferno_r
jet jet_r
magma magma_r
nipy_spectral nipy_spectral_r
ocean ocean_r
pink pink_r
plasma plasma_r
prism prism_r
rainbow rainbow_r
seismic seismic_r
spring spring_r
summer summer_r
tab10 tab10_r
tab20 tab20_r
tab20b tab20b_r
tab20c tab20c_r
terrain terrain_r
twilight twilight_r
twilight_shifted twilight_shifted_r
viridis viridis_r
winter winter_r

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:

Example

Set your own size for the markers:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = 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(x, y, s=sizes)

plt.show()

Result:

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:

Example

Set your own size for the markers:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([5,7,8,7,2,17,2,9,4,11,12,9,6])
y = 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(x, y, s=sizes, alpha=0.5)

plt.show()

Result:

Combine Color Size and Alpha

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

Example

Create random arrays with 100 values for x-points, y-points, colors and sizes:

import matplotlib.pyplot as plt
import numpy as np

x = np.random.randint(100, size=(100))
y = np.random.randint(100, size=(100))
colors = np.random.randint(100, size=(100))
sizes = 10 * np.random.randint(100, size=(100))

plt.scatter(x, y, c=colors, s=sizes, alpha=0.5, cmap=’nipy_spectral’)

plt.colorbar()

plt.show()

Result: