Seaborn

Seaborn is a powerful Python library built on top of Matplotlib that is specifically designed for statistical data visualization. It integrates seamlessly with Pandas DataFrames and allows you to create elegant, informative, and modern charts with single-line commands.

1. Introduction and Styling

Importing Seaborn

By convention, Seaborn is imported as sns:

import seaborn as sns
import matplotlib.pyplot as plt

Themes and Palettes

Seaborn allows you to change the global appearance of plots with one command.

Styles: whitegrid, darkgrid, ticks, white, dark.
Contexts: paper, notebook, talk, poster (scales fonts and sizes).

# Apply a modern dark grid style and a pastel color palette
sns.set_theme(style="darkgrid", palette="muted")

2. Analyzing Numerical Distributions

Distribution plots help you understand the range, skewness, and density of numerical features. You can plot histograms combined with Kernel Density Estimation (KDE) to see a smoothed probability curve over the bars.

tips = sns.load_dataset("tips")

# Plot distribution of bill totals with a density curve
sns.histplot(data=tips, x="total_bill", kde=True, color="teal")
plt.title("Distribution of Total Bills")
plt.show()

Joint plots draw a bivariate relationship (like a scatter plot) along with univariate distributions (histograms) on the margins.

# Scatter plot with marginal histograms showing bills vs. tips
sns.jointplot(data=tips, x="total_bill", y="tip", kind="scatter", color="purple")
plt.show()

Pair plots create a grid of scatter plots and histograms comparing every numerical column in a DataFrame against every other numerical column. This is one of the most common first steps in Machine Learning data exploration.

# Create pairwise relationships across the entire dataset, color-coded by gender
sns.pairplot(data=tips, hue="sex", palette="coolwarm")
plt.show()

3. Categorical Visualizations

Categorical plots are used to inspect statistical aggregates across distinct categories. Seaborn’s barplot automatically aggregates data (calculating the mean by default) and draws error bars representing confidence intervals.

# Compare average tips across days of the week
sns.barplot(data=tips, x="day", y="tip", hue="time", palette="Set2")
plt.title("Average Tip by Day and Time")
plt.show()

A count plot counts the number of records (rows) in each category (similar to a bar plot of frequencies).

# Count the number of smokers vs non-smokers in the dataset
sns.countplot(data=tips, x="smoker", palette="Blues")
plt.title("Frequency of Customers by Smoking Status")
plt.show()

Violin Plots vs. Box Plots

sns.boxplot: Shows the quartiles and outliers.
sns.violinplot: Combines a box plot with a KDE density estimation, showing the shape of the data distribution.

fig, axes = plt.subplots(1, 2, figsize=(12, 5))

# 1. Box plot
sns.boxplot(data=tips, x="day", y="total_bill", ax=axes[0], palette="pastel")
axes[0].set_title("Total Bill Range per Day (Box Plot)")

# 2. Violin plot
sns.violinplot(data=tips, x="day", y="total_bill", ax=axes[1], palette="muted")
axes[1].set_title("Total Bill Distribution density per Day (Violin Plot)")

plt.tight_layout()
plt.show()

4. Relationship and Regressions

Scatter Plots and Line Plots

sns.scatterplot: Plots data points with color (hue) and size mapping.
sns.lineplot: Plots trends, grouping, and error bands.

# Scatter plot with size and color styling
sns.scatterplot(data=tips, x="total_bill", y="tip", hue="smoker", size="size", sizes=(20, 200))
plt.title("Bill vs Tip sized by Table Size")
plt.show()

Draws a scatter plot along with a fitted linear regression line and confidence bands.

# Plot linear relationship between bill and tip, grouped by smoking status
sns.lmplot(data=tips, x="total_bill", y="tip", hue="smoker", height=5)
plt.show()

5. Matrix Plots (Correlation Heatmaps)

Heatmaps are highly useful for visualizing correlation tables between numerical variables. To plot correlation matrices:

Select only the numerical columns from the DataFrame.
Compute the Pearson correlation using .corr().
Draw the heatmap with values printed inside the boxes.

# Select only numerical features
numerical_df = tips.select_dtypes(include=["number"])

# Calculate correlation matrix
corr_matrix = numerical_df.corr()

# Draw Heatmap
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", vmin=-1, vmax=1, linewidths=0.5)
plt.title("Tips Dataset Correlation Matrix")
plt.show()

Practice and Next Steps

Before moving to the next section, make sure to practice your Seaborn skills using the interactive notebook:

Seaborn Practice Exercise

Practice your skills using the interactive notebook.💻 VS Code | 🚀 Colab | 📥 Download

Next Step: Data Visualization Guide

See a comprehensive guide comparing univariate, bivariate, and multivariate analysis on student data.

SQL & Databases

Libraries

1. Introduction and Styling

Importing Seaborn

Themes and Palettes

2. Analyzing Numerical Distributions

3. Categorical Visualizations

Violin Plots vs. Box Plots

4. Relationship and Regressions

Scatter Plots and Line Plots

5. Matrix Plots (Correlation Heatmaps)

Practice and Next Steps

Seaborn Practice Exercise

Next Step: Data Visualization Guide

​1. Introduction and Styling

​Importing Seaborn

​Themes and Palettes

​2. Analyzing Numerical Distributions

​Histograms and KDE (sns.histplot)

​Joint Plots (sns.jointplot)

​Pair Plots (sns.pairplot)

​3. Categorical Visualizations

​Bar Plots (sns.barplot)

​Count Plots (sns.countplot)

​Violin Plots vs. Box Plots

​4. Relationship and Regressions

​Scatter Plots and Line Plots

​Regression Plots (sns.lmplot)

​5. Matrix Plots (Correlation Heatmaps)

​Heatmaps (sns.heatmap)

​Practice and Next Steps

Seaborn Practice Exercise

Next Step: Data Visualization Guide

1. Introduction and Styling

Importing Seaborn

Themes and Palettes

2. Analyzing Numerical Distributions

Histograms and KDE (`sns.histplot`)

Joint Plots (`sns.jointplot`)

Pair Plots (`sns.pairplot`)

3. Categorical Visualizations

Bar Plots (`sns.barplot`)

Count Plots (`sns.countplot`)

Violin Plots vs. Box Plots

4. Relationship and Regressions

Scatter Plots and Line Plots

Regression Plots (`sns.lmplot`)

5. Matrix Plots (Correlation Heatmaps)

Heatmaps (`sns.heatmap`)

Practice and Next Steps