Custom tick formatters in Matplotlib - Step-by-Step Execution

Concept Flow - Custom tick formatters

Start plotting

↓

Set axis ticks

↓

Define custom formatter function

↓

Apply formatter to axis ticks

↓

Render plot with formatted ticks

↓

End

This flow shows how to create a plot, define a custom function to format tick labels, apply it, and then display the plot.

Execution Sample

Matplotlib

import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter

fig, ax = plt.subplots()
ax.plot([0, 1, 2], [10, 20, 30])
formatter = FuncFormatter(lambda x, _: f'{int(x*10)}%')
ax.xaxis.set_major_formatter(formatter)
plt.show()

This code plots points and formats x-axis ticks to show values multiplied by 10 with a percent sign.

Execution Table

Step	Action	Input Tick Value	Formatter Output	Effect on Plot
1	Plot points	[0,1,2]	-	Line plot with x=0,1,2 and y=10,20,30
2	Define formatter	Function lambda x,_: f'{x*10}%'	-	Custom function ready to format ticks
3	Apply formatter to x-axis	Ticks: 0,1,2	0%, 10%, 20%	X-axis tick labels changed accordingly
4	Render plot	-	-	Plot shows x-axis ticks as 0%, 10%, 20%
5	End	-	-	Plot displayed with custom tick labels

💡 All ticks formatted and plot rendered with custom labels

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
fig	None	Figure object created	Same	Same
ax	None	Axes object created	Same	Same
x ticks	None	[0,1,2]	[0,1,2]	[0,1,2]
formatter	None	lambda function wrapped in FuncFormatter	Applied to ticks	Applied
formatted labels	None	None	['0%', '10%', '20%']	Displayed

Key Moments - 3 Insights

Why do we use a function to format ticks instead of just setting labels directly?

Using a function lets matplotlib format any tick value dynamically, as shown in execution_table step 3, so it works even if ticks change.

What does the underscore '_' mean in the lambda function parameters?

The underscore is a placeholder for the tick position argument that we don't use; the function only needs the tick value 'x' as in execution_table step 2.

Why do the formatted labels show '0%', '10%', '20%' instead of original tick values?

Because the formatter multiplies each tick by 10 and adds '%', changing the label text as seen in execution_table step 3.

Visual Quiz - 3 Questions

Look at the execution_table step 3, what is the formatted label for the tick value 1?

A 20%

B 1%

C 10%

D 100%

💡 Hint

Check the 'Formatter Output' column for tick value 1 in step 3.

At which step is the custom formatter function applied to the x-axis ticks?

A Step 1

B Step 3

C Step 2

D Step 4

💡 Hint

Look for the step where 'Apply formatter to x-axis' happens in the execution_table.

If we changed the formatter to multiply ticks by 5 instead of 10, what would be the label for tick 2?

A 10%

B 20%

C 5%

D 100%

💡 Hint

Use the formula in the formatter: tick value times multiplier plus '%'

Concept Snapshot

Custom tick formatters in matplotlib:
- Use ax.xaxis.set_major_formatter() with a function
- Function takes tick value and returns string label
- Allows dynamic, flexible tick labels
- Useful for units, percentages, or special formats
- Keeps plot ticks updated automatically

Full Transcript

This visual execution shows how to use custom tick formatters in matplotlib. First, we create a plot with points. Then, we define a lambda function that formats tick values by multiplying by 10 and adding a percent sign. We apply this function to the x-axis ticks. The execution table traces each step, showing how the formatter changes tick labels from numbers to strings like '0%', '10%', and '20%'. The variable tracker follows key variables like the figure, axes, ticks, and formatted labels. Key moments clarify why a function is used, the meaning of the underscore parameter, and how labels change. The quiz tests understanding of the formatted labels and application steps. The snapshot summarizes how to use custom tick formatters for flexible axis labels.