> ## Documentation Index > Fetch the complete documentation index at: https://genai.codewithsiva.dev/llms.txt > Use this file to discover all available pages before exploring further. # Seaborn > Statistical data visualization built on top of Matplotlib 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`: ```python theme={null} 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). ```python theme={null} # 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. ### Histograms and KDE (`sns.histplot`) You can plot histograms combined with **Kernel Density Estimation (KDE)** to see a smoothed probability curve over the bars. ```python theme={null} 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 (`sns.jointplot`) Joint plots draw a bivariate relationship (like a scatter plot) along with univariate distributions (histograms) on the margins. ```python theme={null} # 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 (`sns.pairplot`) 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. ```python theme={null} # 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. ### Bar Plots (`sns.barplot`) Seaborn's `barplot` automatically aggregates data (calculating the mean by default) and draws error bars representing confidence intervals. ```python theme={null} # 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() ``` ### Count Plots (`sns.countplot`) A count plot counts the number of records (rows) in each category (similar to a bar plot of frequencies). ```python theme={null} # 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. ```python theme={null} 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. ```python theme={null} # 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() ``` ### Regression Plots (`sns.lmplot`) Draws a scatter plot along with a fitted linear regression line and confidence bands. ```python theme={null} # 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. ### Heatmaps (`sns.heatmap`) To plot correlation matrices: 1. Select only the numerical columns from the DataFrame. 2. Compute the Pearson correlation using `.corr()`. 3. Draw the heatmap with values printed inside the boxes. ```python theme={null} # 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: Practice your skills using the interactive notebook. [💻 VS Code](vscode://file/Users/sivaprasad/Downloads/python%20material/python4ai/public/notebooks/seaborn_student_exercise.ipynb) | [🚀 Colab](https://colab.research.google.com/github/prasad230776/python4ai/blob/master/public/notebooks/seaborn_student_exercise.ipynb) | 📥 Download See a comprehensive guide comparing univariate, bivariate, and multivariate analysis on student data.