Creating custom style sheets in Matplotlib

Introduction

Custom style sheets help you make your charts look the same every time without repeating code. They save time and keep your visuals consistent.

You want all your charts in a report to have the same colors and fonts.

You need to share your chart style with teammates so everyone uses the same look.

You want to quickly switch between different chart looks for presentations.

You create many charts and want to avoid writing style code again and again.

Syntax

Matplotlib

[style_name]
key1: value1
key2: value2
...

Style sheets are simple text files with sections named by [style_name].

Each line sets a style property like color or font using key: value format.

Examples

This style sets line width to 2, line color to red, and title size to 14 points.

Matplotlib

[my_style]
lines.linewidth: 2
lines.color: red
axes.titlesize: 14

This style makes the figure background light gray, shows grid lines, and sets font size to 12.

Matplotlib

[cool_style]
figure.facecolor: lightgray
axes.grid: True
font.size: 12

Sample Program

This code creates a custom style sheet file with green thick lines and bigger titles. Then it uses that style to draw a simple line chart. Finally, it prints a confirmation message.

Matplotlib

import matplotlib.pyplot as plt
import matplotlib as mpl

# Create a custom style sheet file
style_content = '''
[custom_style]
lines.linewidth: 3
lines.color: green
axes.titlesize: 16
axes.labelsize: 14
figure.facecolor: #f0f0f0
'''

# Save the style sheet to a file
with open('custom_style.mplstyle', 'w') as f:
    f.write(style_content)

# Use the custom style
with mpl.style.context('custom_style.mplstyle'):
    plt.plot([1, 2, 3], [4, 5, 6])
    plt.title('Chart with Custom Style')
    plt.xlabel('X axis')
    plt.ylabel('Y axis')
    plt.show()

# Print confirmation
print('Custom style applied and plot shown.')

Important Notes

You can create many style sheets and switch between them easily.

Style sheets can control colors, fonts, line widths, grid, and more.

Save style files with .mplstyle extension and load them with mpl.style.context.

Summary

Custom style sheets keep your charts consistent and save time.

They are simple text files with style settings in key: value format.

You can create, save, and apply styles easily using matplotlib.

Practice

1. What is the main purpose of creating a custom style sheet in matplotlib?

easy

A. To make charts look consistent and save time when plotting

B. To write Python code faster

C. To change the data in the plot

D. To export plots as images

Solution

1. Step 1: Understand the role of style sheets

   Style sheets control the appearance of plots, such as colors and fonts.

2. Step 2: Identify the benefit of custom style sheets

   Custom style sheets help keep charts consistent and save time by reusing styles.

3. Final Answer:

   To make charts look consistent and save time when plotting -> Option A

4. Quick Check:

   Custom style sheets = consistent charts + time saving [OK]

Hint: Custom styles = consistent look + less work [OK]

Common Mistakes:

• Thinking style sheets change data
• Confusing style sheets with exporting images
• Believing style sheets speed up coding

2. Which of the following is the correct way to apply a custom style sheet named my_style.mplstyle in matplotlib?

easy

A. plt.style('my_style')

B. plt.use_style('my_style')

C. plt.style.use('my_style.mplstyle')

D. plt.style.apply('my_style.mplstyle')

Solution

1. Step 1: Recall the correct function to apply styles

   The function to apply a style sheet is plt.style.use().

2. Step 2: Check the correct argument format

   The style file name should be passed as a string, including the extension if needed.

3. Final Answer:

   plt.style.use('my_style.mplstyle') -> Option C

4. Quick Check:

   Use plt.style.use() with filename [OK]

Hint: Use plt.style.use('filename') to apply styles [OK]

Common Mistakes:

• Using incorrect function names like plt.use_style
• Omitting .mplstyle extension when needed
• Using plt.style() instead of plt.style.use()

3. Given the following code, what will be the color of the line in the plot?

import matplotlib.pyplot as plt
plt.style.use('custom_style.mplstyle')
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()

Assuming custom_style.mplstyle contains:

lines.color: red

medium

A. Black

B. Blue

C. Green

D. Red

Solution

1. Step 1: Understand style sheet effect

   The style sheet sets lines.color to red, which changes line color.

2. Step 2: Apply style to plot

   Since plt.style.use('custom_style.mplstyle') is called, the line color becomes red.

3. Final Answer:

   Red -> Option D

4. Quick Check:

   Style sets lines.color = red, so line is red [OK]

Hint: Style file sets line color, so plot uses that color [OK]

Common Mistakes:

• Ignoring style file and expecting default color
• Confusing color names
• Not applying style before plotting

4. What is wrong with this code snippet if the custom style sheet my_style.mplstyle is not applied?

import matplotlib.pyplot as plt
plt.style.use(my_style.mplstyle)
plt.plot([1, 2], [3, 4])
plt.show()

medium

A. The style file name should be a string in quotes

B. plt.plot() is missing required arguments

C. plt.show() should be called before plt.plot()

D. The style file must be imported as a module

Solution

1. Step 1: Check how style file is passed

   The style file name must be a string, so it needs quotes around it.

2. Step 2: Identify the error in code

   Without quotes, Python treats my_style.mplstyle as a variable, causing an error.

3. Final Answer:

   The style file name should be a string in quotes -> Option A

4. Quick Check:

   File name must be string in plt.style.use() [OK]

Hint: Put style file name in quotes inside plt.style.use() [OK]

Common Mistakes:

• Forgetting quotes around style file name
• Calling plt.show() too early
• Trying to import style file as Python module

5. You want to create a custom style sheet that sets the plot background to light gray and the grid lines to dashed blue. Which of the following .mplstyle file contents will achieve this?

hard

A. figure.facecolor: #d3d3d3
grid.color: blue
grid.linestyle: '--'

B. axes.facecolor: lightgray
grid.color: blue
grid.linestyle: '--'

C. background.color: lightgray
grid.color: blue
grid.line: dashed

D. axes.background: lightgray
grid.style: dashed
grid.color: blue

Solution

1. Step 1: Identify correct keys for background and grid

   The correct key for plot background is axes.facecolor. Grid color uses grid.color and line style uses grid.linestyle.

2. Step 2: Match values to keys

   Light gray is 'lightgray', grid color is 'blue', and dashed lines use '--'. axes.facecolor: lightgray grid.color: blue grid.linestyle: '--' matches all correctly.

3. Final Answer:

   axes.facecolor: lightgray grid.color: blue grid.linestyle: '--' -> Option B

4. Quick Check:

   Use axes.facecolor and grid.linestyle for background and grid [OK]

Hint: Use axes.facecolor and grid.linestyle keys in style file [OK]

Common Mistakes:

• Using wrong keys like axes.background or grid.style
• Wrong color names or formats
• Missing quotes or incorrect line style syntax