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:

Try it Yourself »

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:

Try it Yourself »

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:

Try it Yourself »

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:

Try it Yourself »

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.

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 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:

Try it Yourself »

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:

Try it Yourself »

Available ColorMaps

You can choose any of the built-in colormaps:

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

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:

Try it Yourself »

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:

Result:

Try it Yourself »

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:

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:

Try it Yourself »

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