Customising Plots

Matplotlib provides many options for customising the appearance of your plots. You can control line styles, colours, markers, and more to create clear, professional-looking figures.

Line Styles and Markers

By default, plt.plot() draws a solid line connecting your data points. You can change this behaviour using format strings.

import numpy as np
import matplotlib.pyplot as plt
%config InlineBackend.figure_format='retina'

# Temperature measurements
time = np.array([0, 2, 4, 6, 8, 10])
temperature = np.array([298, 310, 323, 335, 348, 360])

plt.plot(time, temperature, 'o')  # Plot as points only
plt.xlabel('Time / min')
plt.ylabel('Temperature / K')
plt.show()

The format string 'o' tells matplotlib to plot circular markers without connecting lines. Common format strings include:

• '-' : solid line (default)

• '--' : dashed line

• ':' : dotted line

• 'o' : circular markers

• 's' : square markers

• 'o-' : circular markers connected by lines

plt.plot(time, temperature, 'o-')  # Points connected by lines
plt.xlabel('Time / min')
plt.ylabel('Temperature / K')
plt.show()

Colours

You can specify the colour of your plot using the color keyword argument.

plt.plot(time, temperature, 'o-', color='red')
plt.xlabel('Time / min')
plt.ylabel('Temperature / K')
plt.show()

Matplotlib recognises many named colours, including 'red', 'blue', 'green', 'orange', 'purple', 'black', and 'gray'.

You can also specify colours using RGB tuples (values between 0 and 1) or hexadecimal strings:

# Using an RGB tuple: (red, green, blue)
plt.plot(time, temperature, 'o-', color=(0.2, 0.4, 0.8))
plt.xlabel('Time / min')
plt.ylabel('Temperature / K')
plt.show()

# Using a hexadecimal colour code
plt.plot(time, temperature, 'o-', color='#27bbc7')
plt.xlabel('Time / min')
plt.ylabel('Temperature / K')
plt.show()

Hexadecimal colour codes specify RGB values in base 16, from #000000 (black) to #FFFFFF (white). Online colour pickers can help you find hex codes for specific colours.

Marker Size and Line Width

You can control the size of markers and the width of lines to emphasise particular features of your data.

import numpy as np
import matplotlib.pyplot as plt
%config InlineBackend.figure_format='retina'

time = np.array([0, 2, 4, 6, 8, 10])
temperature = np.array([298, 310, 323, 335, 348, 360])

plt.plot(time, temperature, 'o-', markersize=10, linewidth=2)
plt.xlabel('Time / min')
plt.ylabel('Temperature / K')
plt.show()

• markersize controls the size of the markers (default is 6)

• linewidth controls the thickness of the line (default is 1.5)

Axis Limits

By default, matplotlib automatically scales the axes to fit your data. You can override this to zoom in on a specific region or ensure consistent scales across multiple plots

time = np.linspace(0, 60, 100)
concentration = 1.0 * np.exp(-0.05 * time)

plt.plot(time, concentration)
plt.xlabel('Time / min')
plt.ylabel('Concentration / mol L$^{-1}$')
plt.xlim(0, 40)  # Show only 0-40 minutes
plt.ylim(0, 1.0)  # Set y-axis from 0 to 1
plt.show()

• plt.xlim(min, max) sets the x-axis limits

• plt.ylim(min, max) sets the y-axis limits

Logarithmic Scales

Some data is better visualised on a logarithmic scale, such as when plotting concentration over many orders of magnitude or creating an Arrhenius plot.

# First-order decay over a long time period
time = np.linspace(0, 100, 200)
concentration = 1.0 * np.exp(-0.1 * time)

# Linear scale
plt.plot(time, concentration)
plt.xlabel('Time / min')
plt.ylabel('Concentration / mol L$^{-1}$')
plt.title('Linear scale')
plt.show()

plt.plot(time, concentration)
plt.xlabel('Time / min')
plt.ylabel('Concentration / mol L$^{-1}$')
plt.yscale('log')
plt.title('Logarithmic scale')
plt.show()

Use plt.yscale('log') for a logarithmic y-axis or plt.xscale('log') for a logarithmic x-axis. Logarithmic scales make it easier to see exponential relationships and data spanning multiple orders of magnitude.

Exercise

You have measured the concentration of a reactant over time in two different solvents:

Water:

• Time (s): 0, 10, 20, 30, 40, 50, 60

• Concentration (M): 1.00, 0.61, 0.37, 0.22, 0.14, 0.08, 0.05

Ethanol:

• Time (s): 0, 10, 20, 30, 40, 50, 60

• Concentration (M): 1.00, 0.82, 0.67, 0.55, 0.45, 0.37, 0.30

Create a plot that:

1. Shows both datasets with different colours

2. Uses circular markers with a line connecting them

3. Includes a legend

4. Has a logarithmic y-axis

5. Limits the x-axis to 0-60 seconds

Show solution

time = np.array([0, 10, 20, 30, 40, 50, 60])
conc_water = np.array([1.00, 0.61, 0.37, 0.22, 0.14, 0.08, 0.05])
conc_ethanol = np.array([1.00, 0.82, 0.67, 0.55, 0.45, 0.37, 0.30])

plt.plot(time, conc_water, 'o-', color='blue', label='Water')
plt.plot(time, conc_ethanol, 'o-', color='red', label='Ethanol')
plt.xlabel('Time / s')
plt.ylabel('Concentration / M')
plt.yscale('log')
plt.xlim(0, 60)
plt.legend()
plt.show()

Summary

You have learned how to:

• Control line styles and markers using format strings ('-', '--', 'o', etc.)

• Specify colours using named colours, RGB tuples, or hexadecimal codes

• Adjust marker size with markersize and line thickness with linewidth

• Set axis limits using plt.xlim() and plt.ylim()

• Use logarithmic scales with plt.xscale('log') and plt.yscale('log')