Matplotlib Scatter Plot

We can use the scatter() method from pyplot to create scatter plots.

The syntax for the scatter() method is as follows:

matplotlib.pyplot.scatter(x, y, s=None, c=None, marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, *, edgecolors=None, plotnonfinite=False, data=None, **kwargs)

Parameter Descriptions:

x, y: Arrays of the same length, representing the data points to be plotted, input data.

s: Size of the points, default is 20. It can also be an array where each element corresponds to the size of a point.

c: Color of the points, default is blue 'b'. It can also be an RGB or RGBA two-dimensional row array.

marker: Style of the points, default is a small circle 'o'.

cmap: Colormap, default is None. It can be a scalar or the name of a colormap. It is used only when c is an array of floating-point numbers. If not specified, it defaults to image.cmap.

norm: Normalize, default is None. It scales data to the range 0-1, used only when c is an array of floating-point numbers.

vmin, vmax: Brightness settings, ignored if norm is present.

alpha: Transparency setting, between 0 and 1, default is None, meaning opaque.

linewidths: Width of the marker lines.

edgecolors: Color or sequence of colors, default is 'face'. Possible values are 'face', 'none', or None.

plotnonfinite: Boolean, sets whether to plot points with non-finite c (inf, -inf, or nan).

**kwargs: Other parameters.

The following example demonstrates the scatter() function receiving arrays of the same length, one for the x-axis values and another for the y-axis values:

Example

import matplotlib.pyplot as plt
import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y = np.array([1, 4, 9, 16, 7, 11, 23, 18])

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

The result is displayed as follows:

To set the icon size:

Example

import matplotlib.pyplot as plt
import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y = np.array([1, 4, 9, 16, 7, 11, 23, 18])
sizes = np.array([20, 50, 100, 200, 500, 1000, 60, 90])
plt.scatter(x, y, s=sizes)
plt.show()

The result is displayed as follows:

To customize the color of the points:

Example

import matplotlib.pyplot as plt
import numpy as np

x = np.array([1, 2, 3, 4, 5, 6, 7, 8])
y = np.array([1, 4, 9, 16, 7, 11, 23, 18])
colors = np.array(["red", "green", "black", "orange", "purple", "beige", "cyan", "magenta"])

plt.scatter(x, y, c=colors)
plt.show()

The result is displayed as follows:

To set two sets of scatter plots:

Example

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()

The result is displayed as follows:

To use random numbers to set the scatter plot:

Example

import numpy as np
import matplotlib.pyplot as plt

# Random number generator seed
np.random.seed(19680801)

N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = (30 * np.random.rand(N))**2  # 0 to 15 point radii

plt.scatter(x, y, s=area, c=colors, alpha=0.5)  # Set color and transparency

plt.title("tutorialpro Scatter Test")  # Set title

plt.show()

The result is displayed as follows:

Colorbar Colormap

The Matplotlib module provides many available colormaps.

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

Here is an example of a colormap:

To set the colormap, use the cmap parameter, with the default value being 'viridis', and then set the color values to an array from 0 to 100.

Example

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()

The result is displayed as follows:

To display the colorbar, use the plt.colorbar() method:

Example

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()

The result is displayed as follows:

To change the colormap parameter, set cmap to 'afmhot_r':

Example

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='afmhot_r')
plt.colorbar()
plt.show()

The result is displayed as follows:

The colormap parameter values can be the following: