Scatter plots

Matplotlib Basics

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Published Oct 5 2025

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A scatter plot displays data as points, each representing a pair of x–y coordinates. It’s used to visualise correlations, patterns, and outliers.


Syntax:

plt.scatter(x, y, s=None, c=None, marker=None, cmap=None, alpha=None, edgecolors=None, linewidths=None)

Parameters:

  • x, y = coordinates of points
  • s = size of each marker (scalar or list)
  • c = colour(s) of each marker (scalar, list, or colormap)
  • marker = shape of the point
  • cmap = colour map (used when c is numeric)
  • alpha = transparency (0–1)
  • edgecolors = outline colour of markers
  • linewidths = thickness of marker edges



Basic example

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]

plt.scatter(x, y)
plt.title("Basic Scatter Plot")
plt.xlabel("X Axis")
plt.ylabel("Y Axis")
plt.show()

matplotlib scatter chart basic example




Marker style and colour

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]

plt.scatter(x, y, color='red', marker='^', s=100)
plt.title("Custom Marker and Colour")
plt.show()

matplotlib scatter chart custom marker example

Marker options:

Marker

Symbol

Description

'o'

○

Circle

's'

■

Square

'^'

▲

Triangle up

'v'

▼

Triangle down

'*'

✱

Star

'D'

◆

Diamond

'x'

✕

Cross





Custom edges

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]

plt.scatter(x, y, color='lightgreen', edgecolor='green', linewidths=1)
plt.title("Scatter Plot with Custom Edges")
plt.show()

matplotlib scatter chart custom edges example




Variable marker sizes

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]
sizes = [50, 100, 200, 300, 400]

plt.scatter(x, y, s=sizes, color='skyblue')
plt.title("Variable Marker Sizes")
plt.show()

matplotlib scatter chart variable marker sizes example




Variable Marker Colours (Colour Encoding)

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]
colours = [10, 20, 30, 40, 50]

plt.scatter(x, y, c=colours, cmap='viridis', s=150)
plt.colorbar(label='Value')
plt.title("Colour Encoded Scatter Plot")
plt.show()

matplotlib scatter chart colour encoded example




Multiple scatter groups (categorical comparison)

import matplotlib.pyplot as plt

x1 = [1, 2, 3, 4, 5]
y1 = [2, 4, 6, 8, 10]
x2 = [1, 2, 3, 4, 5]
y2 = [1, 3, 2, 5, 3]

plt.scatter(x1, y1, color='blue', label='Group A', marker='o')
plt.scatter(x2, y2, color='red', label='Group B', marker='^')
plt.legend()
plt.title("Multiple Groups in One Scatter Plot")
plt.show()

matplotlib scatter chart multiple groups example




Using alpha and colormap for dense data

When you have many points, use transparency to make patterns visible:

import matplotlib.pyplot as plt
import numpy as np

x = np.random.rand(500)
y = np.random.rand(500)

plt.scatter(x, y, alpha=0.3, color='purple')
plt.title("Dense Scatter with Transparency")
plt.show()

matplotlib scatter chart dense transparency example

Or add colour coding based on a third variable:

import matplotlib.pyplot as plt
import numpy as np

x = np.random.rand(500)
y = np.random.rand(500)
z = np.random.rand(500)

plt.scatter(x, y, c=z, cmap='plasma', alpha=0.7)
plt.colorbar(label='Intensity')
plt.title("Scatter Plot with Colour Dimension")
plt.show()

matplotlib scatter chart dense colour example




Scatter with line

You can combine scatter with other chart types or add reference lines.:

import matplotlib.pyplot as plt
import numpy as np

x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 8, 7]

plt.scatter(x, y, color='green', label='Data Points')
plt.plot(sorted(x), sorted(y), 'r--', label='Trend Line')
plt.legend()
plt.title("Scatter with Trend Line")
plt.show()

matplotlib scatter chart scatter with line example
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