Custom tick formatters in Matplotlib - Mini Project: Build & Apply

Custom Tick Formatters with Matplotlib

📖 Scenario: You are creating a simple line chart to show daily temperatures over a week. The x-axis shows days as numbers (1 to 7), but you want to display the day names instead (like Mon, Tue, Wed, etc.).

🎯 Goal: Build a matplotlib plot with custom tick labels on the x-axis using a custom tick formatter function.

📋 What You'll Learn

Create a list of temperatures for 7 days.

Create a list of day names for the x-axis labels.

Write a custom tick formatter function to convert day numbers to day names.

Apply the custom tick formatter to the x-axis.

Display the plot with the custom labels.

💡 Why This Matters

🌍 Real World

Custom tick formatters are used in data visualization to make charts clearer and more user-friendly by showing labels that people understand easily.

💼 Career

Data scientists and analysts often customize plots to communicate insights effectively to non-technical audiences.

1

Create temperature data and day numbers

Create a list called temperatures with these exact values: [22, 21, 23, 24, 20, 19, 18]. Also create a list called days with numbers from 1 to 7.

Matplotlib

# Create the list temperatures and the list days
# Your code here

Hint

Use square brackets to create lists. The days list should have numbers 1 through 7.

2

Create day names list

Create a list called day_names with these exact strings: ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"].

Matplotlib

temperatures = [22, 21, 23, 24, 20, 19, 18]
days = [1, 2, 3, 4, 5, 6, 7]
# Create the list day_names
# Your code here

Hint

Use square brackets and quotes to create a list of strings.

3

Write custom tick formatter function

Import matplotlib.pyplot as plt and matplotlib.ticker as mticker. Then write a function called day_formatter that takes a parameter x and returns the day name from day_names at index int(x) - 1. Use mticker.FuncFormatter to create a formatter called formatter using day_formatter.

Matplotlib

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

# Write the day_formatter function and create formatter
# Your code here

Hint

The formatter function must accept two parameters: x and pos (pos can be optional). Use int(x) - 1 to get the correct index.

4

Plot data and apply custom tick formatter

Create a plot using plt.plot(days, temperatures). Then set the x-axis major formatter to formatter using plt.gca().xaxis.set_major_formatter(formatter). Finally, display the plot with plt.show().

Matplotlib

import matplotlib.pyplot as plt
import matplotlib.ticker as mticker

temperatures = [22, 21, 23, 24, 20, 19, 18]
days = [1, 2, 3, 4, 5, 6, 7]
day_names = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]

def day_formatter(x, pos=None):
    return day_names[int(x) - 1]

formatter = mticker.FuncFormatter(day_formatter)

# Plot the data and apply the custom formatter
# Your code here

Hint

Use plt.plot() to draw the line. Use plt.gca().xaxis.set_major_formatter(formatter) to apply the custom labels. Then call plt.show() to display.

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