Plot a Bar Chart from a Dictionary in Python Matplotlib

I have been working with Python for many years, and one of the most common tasks I face is creating quick visualizations from data.

Many times, the data I receive is in the form of a Python dictionary. Instead of converting it into a DataFrame or CSV, I often directly plot it using Matplotlib bar charts.

In this tutorial, I will show you step by step how to plot a bar chart from a dictionary in Python Matplotlib. I’ll cover multiple methods so you can pick the one that fits your workflow best.

Methods to Use a Python Dictionary for Bar Charts

I like using dictionaries because they are easy to create and manage. For example, if I want to compare the average salaries of different professions in the USA, I can store them in a dictionary like this:

salaries = {
    "Software Engineer": 120000,
    "Data Scientist": 115000,
    "Teacher": 60000,
    "Nurse": 75000,
    "Accountant": 70000
}

This makes it very convenient to directly pass the keys and values into a Matplotlib bar chart.

Method 1 – Use plt.bar() in Python

The simplest way to plot a bar chart from a Python dictionary is by using the plt.bar() function from Matplotlib.

Here is the complete code:

import matplotlib.pyplot as plt

# Sample dictionary with average salaries in the USA
salaries = {
    "Software Engineer": 120000,
    "Data Scientist": 115000,
    "Teacher": 60000,
    "Nurse": 75000,
    "Accountant": 70000
}

# Extract keys and values
professions = list(salaries.keys())
income = list(salaries.values())

# Create bar chart
plt.bar(professions, income, color="skyblue")

# Add labels and title
plt.xlabel("Profession")
plt.ylabel("Average Salary (USD)")
plt.title("Average Salaries in the USA by Profession")

# Rotate x-axis labels for better readability
plt.xticks(rotation=30)

# Show chart
plt.show()

I have executed the above example code and added the screenshot below.

This method is my go-to when I want a quick visualization without much customization. The plt.bar() function directly takes the keys as labels and values as heights.

Method 2 – Horizontal Bar Chart from Python Dictionary

Sometimes, when the dictionary has long keys, a horizontal bar chart looks much better.

Here is the code:

import matplotlib.pyplot as plt

# Sample dictionary with population of US states
population = {
    "California": 39500000,
    "Texas": 29000000,
    "Florida": 21500000,
    "New York": 19500000,
    "Illinois": 12500000
}

# Extract keys and values
states = list(population.keys())
pop_values = list(population.values())

# Create horizontal bar chart
plt.barh(states, pop_values, color="lightgreen")

# Add labels and title
plt.xlabel("Population (in millions)")
plt.ylabel("States")
plt.title("Top 5 US States by Population")

# Show chart
plt.show()

I have executed the above example code and added the screenshot below.

I often use this method when the labels are long or when I want to emphasize the comparative values more clearly.

Method 3 – Sort Python Dictionary Before Plotting

Sometimes, the dictionary data is unordered, and I want to plot the bars in ascending or descending order.

Here’s how I do it in Python:

import matplotlib.pyplot as plt

# Sample dictionary with US car sales
car_sales = {
    "Ford": 1800000,
    "Toyota": 2200000,
    "Chevrolet": 1750000,
    "Honda": 1400000,
    "Nissan": 1200000
}

# Sort dictionary by values (descending order)
sorted_sales = dict(sorted(car_sales.items(), key=lambda item: item[1], reverse=True))

brands = list(sorted_sales.keys())
sales = list(sorted_sales.values())

# Create bar chart
plt.bar(brands, sales, color="orange")

# Add labels and title
plt.xlabel("Car Brand")
plt.ylabel("Units Sold")
plt.title("Car Sales in the USA (Sorted by Sales)")

# Show chart
plt.show()

I have executed the above example code and added the screenshot below.

Sorting makes the visualization much more meaningful, especially when comparing performance across categories.

Method 4 – Add Values on Top of Bars

When presenting data, I often want to display the actual values on top of each bar.

Here’s how I add them:

import matplotlib.pyplot as plt

# Sample dictionary with monthly expenses
expenses = {
    "Rent": 1500,
    "Groceries": 600,
    "Utilities": 200,
    "Transport": 300,
    "Entertainment": 250
}

categories = list(expenses.keys())
amounts = list(expenses.values())

# Create bar chart
plt.bar(categories, amounts, color="purple")

# Add labels and title
plt.xlabel("Expense Category")
plt.ylabel("Amount (USD)")
plt.title("Monthly Expenses Breakdown")

# Add values on top of bars
for i, value in enumerate(amounts):
    plt.text(i, value + 20, str(value), ha="center")

plt.show()

I have executed the above example code and added the screenshot below.

This makes the chart more professional and easier to interpret at a glance.

Method 5 – Use pandas with Matplotlib in Python

Although dictionaries are simple, sometimes I convert them into a pandas DataFrame for more flexibility.

Here’s how I do it:

import matplotlib.pyplot as plt
import pandas as pd

# Sample dictionary with average temperatures in US cities
temperatures = {
    "New York": 55,
    "Los Angeles": 65,
    "Chicago": 50,
    "Houston": 70,
    "Phoenix": 75
}

# Convert dictionary to DataFrame
df = pd.DataFrame(list(temperatures.items()), columns=["City", "Temperature"])

# Plot bar chart using pandas
df.plot(kind="bar", x="City", y="Temperature", color="teal", legend=False)

# Add title and labels
plt.title("Average Annual Temperatures in Major US Cities")
plt.ylabel("Temperature (°F)")

plt.show()

I use this method when I want to combine pandas with Matplotlib for more advanced analysis.

Bonus Tip – Customize Colors

Sometimes I want each bar to have a different color. Here’s a quick example:

import matplotlib.pyplot as plt

# Sample dictionary with fruit sales
fruit_sales = {
    "Apples": 500,
    "Bananas": 400,
    "Oranges": 300,
    "Grapes": 200,
    "Mangoes": 100
}

fruits = list(fruit_sales.keys())
sales = list(fruit_sales.values())

colors = ["red", "yellow", "orange", "purple", "green"]

plt.bar(fruits, sales, color=colors)

plt.title("Fruit Sales in a Local Market")
plt.xlabel("Fruit")
plt.ylabel("Units Sold")

plt.show()

This method is excellent when I want to make the chart visually engaging.

Creating a bar chart from a Python dictionary in Matplotlib is simple and powerful.

I showed you different methods:

• Directly using plt.bar()
• Horizontal bar charts with plt.barh()
• Sorting dictionary values before plotting
• Adding values on top of bars
• Using pandas for flexibility
• Customizing colors for better visuals

I personally use these techniques almost daily in my Python projects, whether for quick analysis or professional presentations.

You may also like to read:

• Add Horizontal Grid Lines in Matplotlib
• Matplotlib Horizontal Line with Text in Python
• Remove a Horizontal Line in Matplotlib using Python
• Matplotlib Bar Chart with Different Colors in Python

Bijay Kumar

Bijay Kumar is an experienced Python and AI professional who enjoys helping developers learn modern technologies through practical tutorials and examples. His expertise includes Python development, Machine Learning, Artificial Intelligence, automation, and data analysis using libraries like Pandas, NumPy, TensorFlow, Matplotlib, SciPy, and Scikit-Learn. At PythonGuides.com, he shares in-depth guides designed for both beginners and experienced developers. More about us.

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