How to Visualize Data with Matplotlib and Seaborn
- IOTA ACADEMY
- Apr 28
- 4 min read
An essential component of machine learning and data analysis is data visualization. It facilitates the comprehension of trends, correlations, and patterns in the data. Matplotlib and Seaborn are two of the most popular Python visualization libraries. While Seaborn expands upon Matplotlib and delivers more sophisticated, visually appealing statistical visuals, Matplotlib gives basic plotting capabilities.
In this blog, we'll look at how to use Seaborn and Matplotlib to make different kinds of plots, alter them, and make them easier to read for deeper insights.
Why Use Matplotlib and Seaborn?
Matplotlib is a low-level visualization library that provides full control over plot customization. It is frequently used to produce interactive, animated, and static visualizations. Contrarily, Seaborn is based on Matplotlib and comes with pre-installed themes and statistical visuals that make creating intricate visualizations simpler.
Key Benefits:
Matplotlib: Incredibly adaptable, ideal for both simple and intricate plots.
Seaborn: Makes it easier to create visually stunning statistics charts.
When combined, they provide strong instruments for producing perceptive and eye-catching data visualizations.
Installing and Importing Libraries
To use Matplotlib and Seaborn, you first need to install them. If you haven't already, install them using:
pip install matplotlib pip install seaborn |
Then, import the necessary libraries:
import matplotlib.pyplot as plt import seaborn as sns import numpy as np import pandas as pd |
Basic Plotting with Matplotlib
Matplotlib follows an object-oriented approach, allowing you to create and customize figures and axes. The most commonly used function is plt.plot(), which creates a simple line plot.
x = np.linspace(0, 10, 100) y = np.sin(x) plt.plot(x, y) plt.title("Simple Line Plot") plt.xlabel("X-axis") plt.ylabel("Y-axis") plt.show() |
This code generates a basic sine wave plot as shown below.
Common Matplotlib Visualizations
1. Line Plot
A line plot is useful for showing trends over time.
x = np.arange(1, 11) y = x ** 2 plt.plot(x, y, marker='o', linestyle='--', color='r') plt.title("Line Plot Example") plt.xlabel("X values") plt.ylabel("Y values") plt.grid(True) plt.show() |
This code generates a plot as shown below.
2. Bar Chart
A bar chart is used to compare categorical data.
This code generates a plot as shown below.
3. Scatter Plot
A scatter plot helps visualize relationships between two numerical variables.
x = np.random.rand(50) y = np.random.rand(50) plt.scatter(x, y, color='green', marker='o') plt.title("Scatter Plot Example") plt.xlabel("X values") plt.ylabel("Y values") plt.show() |
This code generates a plot as shown below.
4. Histogram
A histogram shows the distribution of a dataset.
data = np.random.randn(1000) plt.hist(data, bins=30, color='purple', alpha=0.7) plt.title("Histogram Example") plt.xlabel("Value") plt.ylabel("Frequency") plt.show() |
This code generates a plot as shown below.
Advanced Visualizations with Seaborn
Seaborn simplifies the process of creating visually appealing statistical plots. It provides various built-in themes and functions for advanced plotting.
1. Line Plot with Seaborn
Seaborn's lineplot() function is useful for visualizing trends.
sns.set_theme(style="darkgrid") x = np.linspace(0, 10, 100) y = np.sin(x) sns.lineplot(x=x, y=y) plt.title("Seaborn Line Plot") plt.show() |
This code generates a plot as shown below.
2. Bar Chart
Seaborn makes it easy to visualize categorical data with the barplot() function.
data = pd.DataFrame({'Category': ['A', 'B', 'C', 'D'], 'Values': [10, 15, 7, 12]}) sns.barplot(x='Category', y='Values', data=data, palette="Blues") plt.title("Seaborn Bar Chart") plt.show() |
This code generates a plot as shown below.
3. Scatter Plot with Regression Line
Seaborn provides regplot() to add regression lines to scatter plots.
tips = sns.load_dataset("tips") sns.regplot(x="total_bill", y="tip", data=tips) plt.title("Scatter Plot with Regression Line") plt.show() |
This code generates a plot as shown below.
4. Pair Plot
A pair plot is used to visualize relationships between multiple numerical variables in a dataset.
iris = sns.load_dataset("iris") sns.pairplot(iris, hue="species", palette="coolwarm") plt.title("Pair Plot Example") plt.show() |
This code generates a plot as shown below.
5. Heatmap
A heatmap is used to visualize correlations between numerical features.
import seaborn as sns import matplotlib.pyplot as plt # Load dataset tips = sns.load_dataset("tips") # Select only numeric columns correlation = tips.select_dtypes(include=['number']).corr() # Create heatmap sns.heatmap(correlation, annot=True, cmap="coolwarm", linewidths=0.5) plt.title("Heatmap Example") plt.show() |
This code generates a plot as shown below.
Customizing Plots in Matplotlib and Seaborn
Both libraries allow extensive customization to enhance the readability of visualizations.
Customizing Titles, Labels, and Legends
plt.plot(x, y, label="Sine Wave", color="red") plt.xlabel("Time") plt.ylabel("Amplitude") plt.title("Customized Plot Example") plt.legend() plt.grid(True) plt.show() |
This code generates a plot as shown below.
Changing Seaborn Themes
Seaborn provides built-in themes for different aesthetics:
sns.set_style("whitegrid") # Options: darkgrid, whitegrid, dark, white, ticks sns.lineplot(x=x, y=y) plt.title("Seaborn Theme Example") plt.show() |
This code generates a plot as shown below.
Choosing Between Matplotlib and Seaborn
Feature | Matplotlib | Seaborn |
Customization | High | Moderate |
Ease of Use | Requires more code | Simpler syntax |
Plot Aesthetics | Basic | Advanced |
Statistical Features | Limited | Extensive |
If you need full control and customization, Matplotlib is the better choice. If you prefer statistical plots with minimal coding, Seaborn is more convenient.
Conclusion
Two excellent Python utilities for data visualization are Matplotlib and Seaborn. While Seaborn simplifies statistical visualizations with stunning aesthetics, Matplotlib offers total control over plots. You can find hidden insights in your data by combining the two libraries to create visually stunning and educational visualizations.
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