Custom tick formatters in Matplotlib - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct module for custom tick formatting in matplotlib.

Matplotlib

from matplotlib.ticker import [1]

Drag options to blanks, or click blank then click option'

AFuncFormatter

BLinearLocator

CMaxNLocator

DMultipleLocator

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a locator class instead of a formatter.

Using a class that controls tick positions, not formatting.

The FuncFormatter class is used to create custom tick formatters in matplotlib.

2fill in blank

medium

Complete the code to define a function that formats ticks by adding a dollar sign before the value.

Matplotlib

def currency_format(x, pos):
    return [1]

Drag options to blanks, or click blank then click option'

A"$" + x

B"$" + str

C"$" + str(x)

D"$" + f"{x}"

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to concatenate a string with a number directly.

Using incorrect string formatting syntax.

The function must convert the number x to a string and add a dollar sign before it.

3fill in blank

hard

Fix the error in the code to apply the custom formatter to the x-axis.

Matplotlib

ax.xaxis.set_major_formatter([1](currency_format))

Drag options to blanks, or click blank then click option'

AFormatStrFormatter

BFixedFormatter

CScalarFormatter

DFuncFormatter

Attempts:

3 left

💡 Hint

Common Mistakes

Using a formatter class that expects a format string instead of a function.

Not wrapping the function with a formatter class.

The FuncFormatter class wraps the custom function to format ticks.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that formats numbers as percentages if greater than 0.5.

Matplotlib

percent_dict = {num: f"[1]%" for num in numbers if num [2] 0.5}

Drag options to blanks, or click blank then click option'

Aint(num * 100)

B>

C<

Dround(num * 100)

Attempts:

3 left

💡 Hint

Common Mistakes

Using the less than operator instead of greater than.

Not converting the number to an integer before formatting.

The dictionary comprehension converts numbers greater than 0.5 to percentages by multiplying by 100 and converting to int.

5fill in blank

hard

Fill all three blanks to create a custom formatter that shows values with 2 decimals and a percent sign only if value is less than 1.

Matplotlib

def custom_percent(x, pos):
    if x [1] 1:
        return f"{x * 100:[2][3]"
    else:
        return str(x)

Drag options to blanks, or click blank then click option'

A<

B.2f

C%

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using the greater than operator instead of less than.

Forgetting to multiply by 100 before formatting.

Incorrect string formatting syntax.

The function checks if x is less than 1, then formats it as a percentage with 2 decimals using .2f and adds a percent sign.

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