How to Create Bar Chart with Line Using Seaborn Matplotlib

Farook MohammadAugust 7, 2025

0 3 3 minutes read

🧠 Introduction

Ever looked at a chart and thought, “Wow, that’s both beautiful and insightful”? That’s usually when a bar chart and line chart come together! In this guide, you’ll learn how to create a bar chart with a line using Seaborn and Matplotlib in Python. This combo is perfect for visualizing quantities (bars) and trends (lines) on the same graph.

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🔧 Prerequisites

Before we dive in, make sure you have these libraries installed:

pip install matplotlib seaborn pandas

Now import them:

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

📊 Understanding Bar and Line Charts

What is a Bar Chart?

Bar charts display quantitative data with rectangular bars. They’re perfect for comparisons.

What is a Line Chart?

Line charts use a line to display patterns over time.Great for time-series data or performance tracking.

Why Combine Both?

When you mix bars (values) with lines (trends), your story becomes clearer. For example, showing monthly sales (bars) and cumulative growth (line) in one view gives context.

📁 Dataset Preparation

Using a Sample Dataset

Let’s use a basic monthly dataset:

data = {
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'],
    'Sales': [100, 120, 150, 170, 160, 180],
    'Growth': [10, 15, 20, 18, 17, 22]
}

df = pd.DataFrame(data)

Creating a Custom Dataset with Pandas

You can also read data from a CSV:

df = pd.read_csv('your_data.csv')

📈 Introduction to Seaborn and Matplotlib

Key Differences

Seaborn = High-level, beautiful default themes
Matplotlib = Low-level, complete control

When to Use Which

Use Seaborn for the bar chart and Matplotlib for the line overlay.

🛠️ Step-by-Step Guide to Creating the Chart

Creating the Base Bar Chart

plt.figure(figsize=(10, 6))

sns.barplot(x='Month', y='Sales', data=df, color='skyblue')

Adding the Line Chart

plt.plot(df['Month'], df['Growth'], color='red', marker='o', label='Growth')

Optional: Adding Secondary Axis

fig, ax1 = plt.subplots(figsize=(10, 6))
sns.barplot(x='Month', y='Sales', data=df, ax=ax1, color='skyblue')
ax2 = ax1.twinx()
ax2.plot(df['Month'], df['Growth'], color='red', marker='o', label='Growth')
ax1.set_ylabel('Sales')
ax2.set_ylabel('Growth (%)')

🎨 Customizing Your Charts

Titles and Labels

plt.title('Monthly Sales and Growth')
plt.xlabel('Month')
plt.ylabel('Sales')

Styling and Themes

sns.set_style("whitegrid")

Adding Data Labels

for index, value in enumerate(df['Sales']):
    plt.text(index, value + 5, str(value), ha='center')

📊 Handling Multiple Series

Grouped Bar + Line

If you have multiple categories:

sns.barplot(x='Month', y='Sales', hue='Category', data=df)

Then add line:

for cat in df['Category'].unique():
    subset = df[df['Category'] == cat]
    plt.plot(subset['Month'], subset['Growth'])

Legends and Labels

plt.legend(loc='upper left')

💾 Saving the Plot

Export as Image

plt.savefig('bar_line_chart.png', dpi=300, bbox_inches='tight')

Adjusting Figure Size

Set figure size before plotting:

plt.figure(figsize=(12, 7))

🐞 Common Errors and Fixes

Mismatched Axis Scale

Always normalize or scale your data if one range is too wide compared to another.

Overlapping Plots

Use plt.tight_layout() to fix layout issues.

✅ Best Practices

Chart Readability

Keep it clean.
Avoid too many colors.
Use readable fonts.

Color Combinations

Use:

Cool color for bars
Warm color for line
Match your brand/theme

🌍 Real-World Use Cases

Sales vs. Target

Bars: Actual sales
Line: Target goal

Website Traffic Analysis

Bars: Daily visits
Line: Conversion rate

🧾 Conclusion

Combining bar and line charts in Python using Seaborn and Matplotlib is a powerful technique for clear, meaningful visualizations. Whether you’re tracking KPIs, analyzing trends, or impressing your boss with data visuals, mastering this combo is a must. Try it out, tweak it, and make your charts speak louder than words!

❓ FAQs

1. How to plot two different datasets on the same chart?

Use twinx() for dual y-axes and make sure both datasets have the same x-axis scale.

2. Can I use Seaborn for both bar and line?

Not always. Seaborn is great for bars, but Matplotlib gives more control for lines.

3. How to add tooltips to charts?

Use mplcursors or plotly for interactive tooltips.

4. How can values be displayed above bars?

Use plt.text() to manually place value labels on bars.

5. What’s the best way to choose colors?

Stick to color palettes from seaborn.color_palette() or use colorblind-friendly palettes for accessibility.