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Scatter() plot in matplotlib
- A scatter plot is a type of data visualization that uses dots to show values for two variables, with one variable on the x-axis and the other on the y-axis. It's useful for identifying relationships, trends, and correlations, as well as spotting clusters and outliers.
- The dots on the plot shows how the variables are related. A scatter plot is made with the matplotlib library's
scatter() method.
Syntax
Here's how to write code for the scatter() method:
matplotlib.pyplot.scatter (x_axis_value, y_axis_value, s = None, c = None, vmin = None, vmax = None, marker = None, cmap = None, alpha = None, linewidths = None, edgecolors = None)
Prerequisites
Scatter plots can be created in Python with Matplotlib's pyplot library. To build a Scatter plot, first import matplotlib. It is a standard convention to import Matplotlib's pyplot library as plt.
import matplotlib.pyplot as plt
Creating a simple Scatter Plot
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:
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()
When executed, this will show the following Scatter plot:
Compare Plots
In a scatter plot, comparing plots involves examining multiple sets of points to identify differences or similarities in patterns, trends, or correlations between the data sets.
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()
When executed, this will show the following Compare Scatter plot:
Colors in Scatter plot
You can set your own color for each scatter plot with the color or the c argument:
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()
When executed, this will show the following Colors Scatter plot:
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.
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()
When executed, this will show the following Color Each Dot:
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:
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()
When executed, this will show the following Scatter ColorMap:
You can include the colormap in the drawing by including the plt.colorbar() statement:
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()
When executed, this will show the following Scatter ColorMap using plt.colorbar():
