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.

Practice

1. What is the main purpose of using a FuncFormatter in matplotlib?

easy

A. To change the color of the plot lines

B. To customize how tick labels are displayed on an axis

C. To add grid lines to the plot

D. To set the plot title

Solution

1. Step 1: Understand what tick labels are

   Tick labels are the numbers or text shown on the axis to indicate scale or categories.

2. Step 2: Role of FuncFormatter

   FuncFormatter lets you define a function that changes how these labels appear, for example formatting numbers or adding units.

3. Final Answer:

   To customize how tick labels are displayed on an axis -> Option B

4. Quick Check:

   FuncFormatter changes tick labels [OK]

Hint: FuncFormatter changes axis label text format fast [OK]

Common Mistakes:

• Confusing FuncFormatter with plot styling functions
• Thinking it changes plot colors
• Assuming it adds grid lines

2. Which of the following is the correct way to import FuncFormatter from matplotlib?

easy

A. from matplotlib.ticker import FuncFormatter

B. import matplotlib.pyplot as FuncFormatter

C. from matplotlib import FuncFormatter

D. import FuncFormatter from matplotlib.ticker

Solution

1. Step 1: Identify the module for FuncFormatter

   FuncFormatter is part of the ticker module in matplotlib, so it must be imported from matplotlib.ticker.

2. Step 2: Check correct Python import syntax

   The correct syntax is 'from matplotlib.ticker import FuncFormatter'. Other options either use wrong syntax or wrong module.

3. Final Answer:

   from matplotlib.ticker import FuncFormatter -> Option A

4. Quick Check:

   Correct import = from matplotlib.ticker import FuncFormatter [OK]

Hint: FuncFormatter is in matplotlib.ticker module [OK]

Common Mistakes:

• Using wrong import syntax
• Importing from matplotlib.pyplot
• Trying to import directly from matplotlib

3. What will be the output tick labels on the x-axis after running this code?

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

def format_func(value, tick_number):
    return f"${value:.1f}k"

fig, ax = plt.subplots()
ax.plot([1000, 2000, 3000])
ax.xaxis.set_major_formatter(FuncFormatter(format_func))
plt.show()

medium

A. Tick labels like '1000', '2000', '3000'

B. Tick labels like '0', '1', '2'

C. Tick labels like '$0.0k', '$1.0k', '$2.0k'

D. Tick labels like 'k0.0$', 'k1.0$', 'k2.0$'

Solution

1. Step 1: Understand the formatter function

   The function formats the tick value as a string with a dollar sign, one decimal place, and 'k' suffix, e.g. '$0.0k'.

2. Step 2: Check the x-axis tick values

   Since the plot has 3 points, the default ticks are at positions 0, 1, 2. The formatter converts these to '$0.0k', '$1.0k', '$2.0k'.

3. Final Answer:

   Tick labels like '$0.0k', '$1.0k', '$2.0k' -> Option C

4. Quick Check:

   Formatter adds $ and k to tick numbers [OK]

Hint: Formatter changes tick numbers to strings with $ and k [OK]

Common Mistakes:

• Assuming formatter changes data points instead of ticks
• Expecting original data values as labels
• Confusing tick positions with data values

4. Identify the error in this code snippet that tries to format y-axis ticks with percentages:

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

def percent(x, pos):
    return f"{x}%"

fig, ax = plt.subplots()
ax.plot([0.1, 0.2, 0.3])
ax.yaxis.set_major_formatter(FuncFormatter(percent))
plt.show()

medium

A. The percent function should multiply by 100 and add % sign as string

B. The percent function should return a string with % sign, but it returns a float

C. The percent function is missing the pos parameter

D. The FuncFormatter is applied to xaxis instead of yaxis

Solution

1. Step 1: Examine the percent function

   The function returns f"{x}%", e.g., for x=0.1 it returns "0.1%", which does not properly represent a percentage.

2. Step 2: Identify the required change

   For percentage formatting, the function should multiply x by 100 before appending the % sign, e.g., "10%" for 0.1.

3. Final Answer:

   The percent function should multiply by 100 and add % sign as string -> Option A

4. Quick Check:

   Percent formatters multiply by 100 [OK]

Hint: Formatter functions must return strings, not numbers [OK]

Common Mistakes:

• Returning numbers instead of strings from formatter
• Forgetting to multiply by 100 for percentages
• Missing required function parameters

5. You want to display x-axis ticks as dates in 'Month-Day' format using a custom formatter. Which approach correctly applies this using FuncFormatter?

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

dates = [datetime.date(2024, 6, d) for d in range(1, 6)]
values = [10, 20, 15, 25, 30]

fig, ax = plt.subplots()
ax.plot(dates, values)

# Which formatter function and application is correct?

hard

A. def format_date(x, pos):
return dates[int(x)].strftime('%m-%d')
ax.xaxis.set_major_formatter(FuncFormatter(format_date))

B. def format_date(x, pos):
return str(x)
ax.xaxis.set_major_formatter(FuncFormatter(format_date))

C. def format_date(x, pos):
return x.strftime('%m-%d')
ax.xaxis.set_major_formatter(FuncFormatter(format_date))

D. def format_date(x, pos):
return datetime.datetime.fromordinal(int(x)).strftime('%m-%d')
ax.xaxis.set_major_formatter(FuncFormatter(format_date))

Solution

1. Step 1: Understand x values in date plots

   Matplotlib converts dates to float numbers representing days since a fixed date (ordinal). So x is a float ordinal number.

2. Step 2: Convert ordinal to date string

   Use datetime.datetime.fromordinal(int(x)) to convert float x to a date object, then format with strftime('%m-%d').

3. Step 3: Check options

   def format_date(x, pos): return datetime.datetime.fromordinal(int(x)).strftime('%m-%d') ax.xaxis.set_major_formatter(FuncFormatter(format_date)) correctly converts x to date and formats it. def format_date(x, pos): return dates[int(x)].strftime('%m-%d') ax.xaxis.set_major_formatter(FuncFormatter(format_date)) incorrectly indexes dates list by int(x) which may cause index errors. def format_date(x, pos): return x.strftime('%m-%d') ax.xaxis.set_major_formatter(FuncFormatter(format_date)) tries to call strftime on float x, causing error. def format_date(x, pos): return str(x) ax.xaxis.set_major_formatter(FuncFormatter(format_date)) returns string of float, not formatted date.

4. Final Answer:

   def format_date(x, pos): return datetime.datetime.fromordinal(int(x)).strftime('%m-%d') ax.xaxis.set_major_formatter(FuncFormatter(format_date)) -> Option D

5. Quick Check:

   Date ticks need ordinal to date conversion [OK]

Hint: Convert float ticks to dates with fromordinal before formatting [OK]

Common Mistakes:

• Using tick value as list index causing errors
• Calling date methods on float values
• Returning raw float strings instead of formatted dates