Scatter plots

Matplotlib Basics

1 min read

Published Oct 5 2025, updated Aug 17 2026

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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 pltx = [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()

Marker style and colour

import matplotlib.pyplot as pltx = [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 pltx = [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()

Variable marker sizes

import matplotlib.pyplot as pltx = [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()

Variable Marker Colours (Colour Encoding)

import matplotlib.pyplot as pltx = [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 pltx1 = [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 pltimport numpy as npx = 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 pltimport numpy as npx = 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 pltimport numpy as npx = [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