I have been working with Python and data visualization for more than a decade. I have often needed to create quick bar charts directly from Pandas DataFrames.
At first, I used to export my data to Excel and create charts there. But once I realized how powerful Matplotlib is, I switched to plotting directly in Python. This saved me a lot of time and gave me more control over formatting.
In this tutorial, I will show you step by step how to plot a bar chart from a DataFrame using Python Matplotlib. I will cover multiple methods so you can choose whichever feels most comfortable.
What is a Bar Chart in Python Matplotlib?
A bar chart is one of the most common ways to visualize categorical data. It uses rectangular bars to represent values.
In Python, Matplotlib makes it very easy to create bar charts. You can either plot them directly from a Pandas DataFrame or by passing lists/arrays.
Since most of us already work with DataFrames in Pandas, I’ll focus on plotting bar charts directly from them.
Dataset Example – U.S. State Sales Data
To make this tutorial practical, I created a small dataset of sales numbers for different U.S. states.
Here is the dataset in a Pandas DataFrame:
import pandas as pd
# Sample sales data for U.S. states
data = {
"State": ["California", "Texas", "New York", "Florida", "Illinois"],
"Sales": [250000, 180000, 200000, 150000, 120000]
}
df = pd.DataFrame(data)
print(df)When you run this code, you will see:
State Sales
0 California 250000
1 Texas 180000
2 New York 200000
3 Florida 150000
4 Illinois 120000This dataset will be the foundation for our bar chart examples.
Method 1 – Use Pandas .plot() in Python
The easiest way to plot a bar chart from a DataFrame is by using Pandas’ built‑in .plot() method. This internally uses Matplotlib.
import matplotlib.pyplot as plt
# Plot bar chart using Pandas
df.plot(x="State", y="Sales", kind="bar", legend=False)
plt.title("Sales by State (USA)")
plt.xlabel("State")
plt.ylabel("Sales in USD")
plt.show()You can refer ot the screenshot below to see the output.
This method is quick and requires very little code. The .plot(kind=”bar”) option tells Pandas to create a bar chart.
Method 2 – Plot Bar Chart Using Matplotlib plt.bar()
Sometimes, I need more control over the chart. In that case, I use Matplotlib’s plt.bar() function directly.
# Plot bar chart using Matplotlib directly
plt.bar(df["State"], df["Sales"], color="skyblue")
plt.title("Sales by State (USA)")
plt.xlabel("State")
plt.ylabel("Sales in USD")
plt.xticks(rotation=45)
plt.show()You can refer ot the screenshot below to see the output.
This method gives me full control over colors, labels, and formatting. For example, I rotated the x‑axis labels so they don’t overlap.
Method 3 – Horizontal Bar Chart
In some cases, horizontal bar charts are easier to read, especially when state names are long.
# Horizontal bar chart
plt.barh(df["State"], df["Sales"], color="orange")
plt.title("Sales by State (USA)")
plt.xlabel("Sales in USD")
plt.ylabel("State")
plt.show()You can refer ot the screenshot below to see the output.
I prefer horizontal bar charts when dealing with long labels or when I want to emphasize ranking.
Method 4 – Grouped Bar Chart from DataFrame
Let’s say we have sales data for two different years. We can plot them side by side using a grouped bar chart.
# Sample dataset with two years
data = {
"State": ["California", "Texas", "New York", "Florida", "Illinois"],
"2023": [250000, 180000, 200000, 150000, 120000],
"2024": [270000, 190000, 210000, 160000, 130000]
}
df2 = pd.DataFrame(data)
# Plot grouped bar chart
x = range(len(df2["State"]))
plt.bar([p - 0.2 for p in x], df2["2023"], width=0.4, label="2023")
plt.bar([p + 0.2 for p in x], df2["2024"], width=0.4, label="2024")
plt.xticks(x, df2["State"], rotation=45)
plt.title("Sales by State (USA) – 2023 vs 2024")
plt.xlabel("State")
plt.ylabel("Sales in USD")
plt.legend()
plt.show()You can refer ot the screenshot below to see the output.
This method is very useful when I want to compare multiple categories side by side.
Method 5 – Stacked Bar Chart
Another way to compare categories is by stacking bars on top of each other.
# Stacked bar chart
df2.set_index("State")[["2023", "2024"]].plot(kind="bar", stacked=True)
plt.title("Stacked Sales by State (USA)")
plt.xlabel("State")
plt.ylabel("Sales in USD")
plt.show()Stacked bar charts are helpful when I want to show the total as well as the contribution of each category.
Add Labels to Bars in Matplotlib
Sometimes, I want to display the exact sales numbers on top of each bar. This makes the chart more informative.
# Bar chart with labels
bars = plt.bar(df["State"], df["Sales"], color="green")
plt.title("Sales by State (USA)")
plt.xlabel("State")
plt.ylabel("Sales in USD")
# Add labels
for bar in bars:
plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() - 10000,
f'{bar.get_height():,}', ha='center', va='bottom', color="white", fontweight="bold")
plt.show()Adding labels makes it easier for readers to understand the chart at a glance without guessing the values.
Style the Bar Chart in Python Matplotlib
Matplotlib allows me to style the chart in many ways:
- Change bar colors
- Add gridlines
- Customize font sizes
- Use different themes
plt.style.use("ggplot") # Apply ggplot style
plt.bar(df["State"], df["Sales"], color="purple")
plt.title("Styled Sales by State (USA)")
plt.xlabel("State")
plt.ylabel("Sales in USD")
plt.grid(True, axis="y", linestyle="--", alpha=0.7)
plt.show()I often use styles like “ggplot” or “seaborn” to make the chart look modern and professional.
When to Use Each Method?
- Pandas .plot() → Quick and simple bar chart
- Matplotlib plt.bar() → Full customization
- Horizontal bar chart → Best for long labels
- Grouped bar chart → Compare categories across years
- Stacked bar chart → Show totals and contributions
Choosing the right method depends on the story you want to tell with your data.
Creating bar charts from a DataFrame in Python Matplotlib is straightforward once you know the different methods.
I personally start with Pandas’ .plot() when I want something quick. But when I need more control, I switch to Matplotlib’s plt.bar() or grouped/stacked charts.
You can now confidently plot bar charts directly from your DataFrame and customize them for your needs. With these techniques, you’ll save time and create professional‑looking charts for your reports or dashboards.
You may also read:
- Matplotlib Horizontal Line with Text in Python
- Remove a Horizontal Line in Matplotlib using Python
- Matplotlib Bar Chart with Different Colors in Python
- Plot a Bar Chart from a Dictionary in Python Matplotlib
I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.
