Horizontal bar chart with plt.barh in Matplotlib

Introduction

A horizontal bar chart helps you compare values across categories using bars that go from left to right. It is easy to read when category names are long or when you want to show data horizontally.

Comparing sales numbers for different products with long names.

Showing survey results where categories have long labels.

Visualizing time spent on tasks where horizontal layout fits better.

Displaying rankings or scores in a clear horizontal format.

Syntax

Matplotlib

plt.barh(y, width, height=0.8, left=None, *, align='center', **kwargs)

y is the position of bars on the vertical axis (usually categories).

width is the length of each bar (the data values).

Examples

Basic horizontal bar chart with categories on the y-axis and values as bar lengths.

Matplotlib

import matplotlib.pyplot as plt

categories = ['Apples', 'Bananas', 'Cherries']
values = [10, 15, 7]
plt.barh(categories, values)
plt.show()

Horizontal bars with custom color and thinner bars using height.

Matplotlib

plt.barh(['A', 'B', 'C'], [5, 8, 3], color='skyblue', height=0.5)
plt.show()

Using numeric positions for bars and setting labels with tick_label.

Matplotlib

positions = [0, 1, 2]
values = [4, 9, 6]
labels = ['X', 'Y', 'Z']
plt.barh(positions, values, tick_label=labels)
plt.show()

Sample Program

This program creates a horizontal bar chart showing quantities of different fruits. It adds a title and axis labels for clarity.

Matplotlib

import matplotlib.pyplot as plt

# Data
fruits = ['Apples', 'Bananas', 'Cherries', 'Dates']
quantities = [25, 40, 15, 10]

# Create horizontal bar chart
plt.barh(fruits, quantities, color='green')

# Add title and labels
plt.title('Fruit Quantities')
plt.xlabel('Quantity')
plt.ylabel('Fruit')

# Show the plot
plt.show()

Important Notes

You can customize colors, bar height, and labels easily with parameters.

Horizontal bars are good when category names are long or many.

Use plt.bar for vertical bars and plt.barh for horizontal bars.

Summary

Horizontal bar charts show data with bars going left to right.

Use plt.barh with categories on y-axis and values as bar lengths.

Customize appearance with colors, bar height, and labels.

Practice

1. What does the plt.barh() function in matplotlib do?

easy

A. Generates a scatter plot with dots

B. Creates a vertical bar chart with bars extending bottom to top

C. Creates a horizontal bar chart with bars extending left to right

D. Plots a line graph connecting data points

Solution

1. Step 1: Understand the function purpose

   plt.barh() is designed to create horizontal bar charts, where bars extend from left to right.

2. Step 2: Compare with other plot types

   Vertical bar charts use plt.bar(), line graphs use plt.plot(), and scatter plots use plt.scatter().

3. Final Answer:

   Creates a horizontal bar chart with bars extending left to right -> Option C

4. Quick Check:

   Horizontal bar chart = plt.barh() [OK]

Hint: Remember 'h' in barh means horizontal bars [OK]

Common Mistakes:

• Confusing plt.barh() with plt.bar()
• Thinking plt.barh() creates vertical bars
• Mixing bar charts with line or scatter plots

2. Which of the following is the correct syntax to create a horizontal bar chart with categories ['A', 'B', 'C'] and values [5, 7, 3] using matplotlib?

easy

A. plt.barh(['A', 'B', 'C'], [5, 7, 3])

B. plt.bar([5, 7, 3], ['A', 'B', 'C'])

C. plt.barh([5, 7, 3], ['A', 'B', 'C'])

D. plt.plot(['A', 'B', 'C'], [5, 7, 3])

Solution

1. Step 1: Identify correct function and argument order

   plt.barh() takes categories as the first argument (y-axis labels) and values as the second argument (bar lengths).

2. Step 2: Check each option

   plt.barh(['A', 'B', 'C'], [5, 7, 3]) correctly uses plt.barh(['A', 'B', 'C'], [5, 7, 3]). plt.bar([5, 7, 3], ['A', 'B', 'C']) uses vertical bars and reverses arguments. plt.barh([5, 7, 3], ['A', 'B', 'C']) reverses arguments for horizontal bars. plt.plot(['A', 'B', 'C'], [5, 7, 3]) plots a line graph.

3. Final Answer:

   plt.barh(['A', 'B', 'C'], [5, 7, 3]) -> Option A

4. Quick Check:

   Categories first, values second in plt.barh() [OK]

Hint: In plt.barh(), categories come first, then values [OK]

Common Mistakes:

• Swapping categories and values in arguments
• Using plt.bar() instead of plt.barh() for horizontal bars
• Using plt.plot() instead of bar chart functions

3. What will be the output of the following code?

import matplotlib.pyplot as plt
categories = ['X', 'Y', 'Z']
values = [10, 5, 8]
plt.barh(categories, values, color='green')
plt.show()

medium

A. A vertical bar chart with green bars for categories X, Y, Z

B. A line plot connecting points (X,10), (Y,5), (Z,8)

C. An error because color must be a list

D. A horizontal bar chart with green bars for categories X, Y, Z

Solution

1. Step 1: Analyze the function and parameters

   The code uses plt.barh() with categories on y-axis and values as bar lengths, colored green.

2. Step 2: Understand the plot output

   This produces a horizontal bar chart with green bars labeled X, Y, Z with lengths 10, 5, and 8 respectively.

3. Final Answer:

   A horizontal bar chart with green bars for categories X, Y, Z -> Option D

4. Quick Check:

   plt.barh() + color='green' = horizontal green bars [OK]

Hint: plt.barh() always creates horizontal bars, color accepts string [OK]

Common Mistakes:

• Thinking plt.barh() creates vertical bars
• Assuming color must be a list
• Confusing bar chart with line plot

4. Identify the error in this code snippet that tries to create a horizontal bar chart:

import matplotlib.pyplot as plt
labels = ['A', 'B', 'C']
values = [4, 6, 8]
plt.barh(values, labels)
plt.show()

medium

A. plt.barh() does not accept lists as arguments

B. Arguments are reversed; categories should be first, values second

C. Missing plt.show() call

D. Color argument is required for plt.barh()

Solution

1. Step 1: Check argument order in plt.barh()

   The first argument should be categories (labels), the second argument should be values (bar lengths).

2. Step 2: Identify the mistake in the code

   The code passes values first and labels second, which reverses the expected order and causes incorrect plotting.

3. Final Answer:

   Arguments are reversed; categories should be first, values second -> Option B

4. Quick Check:

   plt.barh(categories, values) order matters [OK]

Hint: Categories first, values second in plt.barh() [OK]

Common Mistakes:

• Swapping the order of categories and values
• Forgetting plt.show() (though present here)
• Thinking color argument is mandatory

5. You have a dataset with categories ['Jan', 'Feb', 'Mar'] and sales [200, 150, 300]. You want to create a horizontal bar chart with red bars, bar height of 0.5, and display the sales values on the bars. Which code snippet correctly achieves this?

hard

A. plt.barh(['Jan', 'Feb', 'Mar'], [200, 150, 300], color='red', height=0.5)
for i, v in enumerate([200, 150, 300]):
plt.text(v + 5, i, str(v))
plt.show()

B. plt.barh([200, 150, 300], ['Jan', 'Feb', 'Mar'], color='red', height=0.5)
plt.show()

C. plt.bar(['Jan', 'Feb', 'Mar'], [200, 150, 300], color='red', height=0.5)
plt.show()

D. plt.barh(['Jan', 'Feb', 'Mar'], [200, 150, 300], color='red', width=0.5)
plt.show()

Solution

1. Step 1: Use plt.barh() with correct arguments

   Categories come first, then values. Set color='red' and height=0.5 to adjust bar thickness.

2. Step 2: Add sales values as text labels on bars

   Use a for loop with plt.text() to place sales numbers slightly right of each bar end.

3. Step 3: Verify other options

   plt.barh([200, 150, 300], ['Jan', 'Feb', 'Mar'], color='red', height=0.5) plt.show() reverses arguments, plt.bar(['Jan', 'Feb', 'Mar'], [200, 150, 300], color='red', height=0.5) plt.show() uses vertical bars, plt.barh(['Jan', 'Feb', 'Mar'], [200, 150, 300], color='red', width=0.5) plt.show() uses 'width' instead of 'height' which is invalid for barh.

4. Final Answer:

   plt.barh(['Jan', 'Feb', 'Mar'], [200, 150, 300], color='red', height=0.5) for i, v in enumerate([200, 150, 300]): plt.text(v + 5, i, str(v)) plt.show() -> Option A

5. Quick Check:

   plt.barh(categories, values, height=0.5) + plt.text() labels [OK]

Hint: Use height for bar thickness in plt.barh(), add labels with plt.text() [OK]

Common Mistakes:

• Swapping categories and values
• Using width instead of height for bar thickness
• Not adding text labels or placing them incorrectly