Scatter plots

Matplotlib Basics

1 min read

Published Oct 5 2025

ChartsGraphsMatplotlibNumPyPandasPythonVisualisation

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()

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

Scatter plots

Matplotlib Basics

1 min read

Published Oct 5 2025

Guide Sections

Guide Comments

Basic example

Marker style and colour

Custom edges

Variable marker sizes

Variable Marker Colours (Colour Encoding)

Multiple scatter groups (categorical comparison)

Using alpha and colormap for dense data

Scatter with line

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