Advanced Data Visualization with Seaborn

Mastering Advanced Data Visualization with Seaborn: Real-World Projects and Best Practices

Unlock the full potential of your data with advanced data visualization techniques using Seaborn. This comprehensive guide covers everything from the basics to advanced plotting, providing detailed explanations, best practices, and hands-on examples with real-world datasets. Whether you’re visualizing health metrics, analyzing technology usage trends, or tracking data science job salaries, this guide will equip you with the skills to create insightful and visually appealing charts. Learn how to use Seaborn’s powerful tools to enhance your data storytelling, customize plots for clarity, and leverage Kaggle for data exploration and sharing. Perfect for data enthusiasts looking to elevate their data visualization expertise to the next level.

Table of Contents

1. Introduction to Seaborn
   • Overview
   • Seaborn Basics
   • Working with DataFrames
   • Basic Plotting Functions
2. Advanced Plotting Techniques
   • Pair Plot
     • Example Code
     • Best Practices
   • FacetGrid
     • Example Code
     • Best Practices
   • Heatmap
     • Example Code
     • Best Practices
3. Customizing Plots
   • Adding Titles and Labels
   • Styling
4. Real-Life Unique Projects
   • Project 1: Customer Segmentation Analysis
     • Dataset
     • Visualization Goals
     • Example
   • Project 2: Sales Performance Dashboard
     • Dataset
     • Visualization Goals
     • Example
   • Project 3: Health Metrics Analysis
     • Dataset
     • Visualization Goals
     • Example
   • Project 4: Technology Usage Trends
     • Dataset
     • Visualization Goals
     • Example
   • Project 5: Data Science Job Salaries Analysis
     • Dataset
     • Visualization Goals
     • Example
5. Seaborn Cheat Sheet
6. Using Kaggle
   • Create a Kaggle Account
   • Start a New Kernel
   • Upload Your Data
   • Write Your Code
   • Save and Publish
   • Example Kernel
7. Best Practices
8. Conclusion

1. Introduction to Seaborn

Overview

Seaborn is a powerful Python library built on top of Matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics. It is particularly useful for creating complex visualizations with minimal code and is well-suited for exploratory data analysis.

Seaborn Basics

Before diving into advanced topics, it’s essential to have a basic understanding of Seaborn. Here are some fundamental concepts:

• Importing Seaborn
• Loading datasets
• Basic plots

Working with DataFrames

Seaborn works well with Pandas DataFrames, which makes it easy to visualize data directly from DataFrames.

Basic Plotting Functions

Seaborn provides various basic plotting functions such as sns.scatterplot, sns.lineplot, sns.histplot, etc.

2. Advanced Plotting Techniques

Pair Plot

A pair plot allows you to visualize relationships between multiple pairs of variables. It is useful for exploring multidimensional data.

Example Code

# Import seaborn
import seaborn as sns

# Apply the default theme
sns.set_theme()

# Load an example dataset
tips = sns.load_dataset("tips")

# Create a visualization
sns.relplot(
    data=tips,
    x="total_bill", y="tip", col="time",
    hue="smoker", style="smoker", size="size",
)

Best Practices

• Hue: Use the hue parameter to distinguish between different categories.
• Palette: Choose a color palette that is easy to distinguish.

FacetGrid

FacetGrid is used to plot multiple charts based on the conditions of different categories in the dataset.

Example Code

# Load the tips dataset
tips = sns.load_dataset('tips')

# Create a FacetGrid
g = sns.FacetGrid(tips, col="time", row="smoker")
g.map(sns.histplot, "total_bill")
plt.show()

Best Practices

• Column and Row: Use meaningful variables for col and row to compare different subsets.
• Mapping: Ensure the mapped function is appropriate for the data distribution.

Heatmap

Heatmaps are great for visualizing matrix-like data, such as correlation matrices.

Example Code

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

# Generate example data
np.random.seed(0)
data = np.random.rand(10, 12)
data_frame = pd.DataFrame(data, columns=[f'Col{i}' for i in range(1, 13)], 
                          index=[f'Row{i}' for i in range(1, 11)])

# Create a heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(data_frame, annot=True, fmt=".2f", cmap='viridis', linewidths=.5)
plt.title('Seaborn Heatmap Example')
plt.xlabel('Columns')
plt.ylabel('Rows')
plt.show()

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

# Fetch real-time data from the disease.sh COVID-19 API
url = "https://disease.sh/v3/covid-19/countries"
response = requests.get(url)
data = response.json()

# Process the data to create a DataFrame
df = pd.DataFrame(data)

# Select relevant columns
df = df[['country', 'cases', 'deaths', 'recovered']]

# Filter for countries with significant case numbers
df = df[df['cases'] > 100000]

# Set the country as the index
df.set_index('country', inplace=True)

# Normalize the data for better heatmap visualization
df_normalized = df.div(df.max(axis=1), axis=0)

# Create a heatmap
plt.figure(figsize=(15, 10))
sns.heatmap(df_normalized, annot=True, fmt=".2f", cmap='coolwarm', linewidths=.5)
plt.title('COVID-19 Statistics Heatmap by Country')
plt.xlabel('Metrics')
plt.ylabel('Country')
plt.show()

Best Practices

• Annotations: Use annot=True to display the correlation values.
• Color Map: Choose a color map that enhances the readability of the data.

3. Customizing Plots

Adding Titles and Labels

Adding titles and labels to your plots makes them more informative.

# Scatter plot with customization
sns.scatterplot(data=iris, x='sepal_length', y='sepal_width', hue='species')
plt.title('Sepal Length vs Sepal Width')
plt.xlabel('Sepal Length (cm)')
plt.ylabel('Sepal Width (cm)')
plt.legend(title='Species')
plt.show()

Styling

Seaborn offers various themes and styles to improve the aesthetics of your plots.

# Set the style
sns.set_style("whitegrid")

# Plot with the new style
sns.boxplot(data=tips, x='day', y='total_bill', palette='Set2')
plt.show()

4. Real-Life Unique Projects

Project 1: Customer Segmentation Analysis

Dataset

Customer data including purchase history, demographics, and behavior metrics. Download a suitable dataset from Kaggle, such as the Mall Customer Segmentation Data.

Visualization Goals

• Identify distinct customer segments.
• Visualize purchasing patterns across segments.

Example

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

# Load the customer dataset
df_customers = pd.read_csv('Mall_Customers.csv')

# Plotting pair plot
sns.pairplot(df_customers[['Age', 'Annual Income (k$)', 'Spending Score (1-100)']])
plt.show()

Output:

Project 2: Sales Performance Dashboard

Dataset

Sales data including regions, sales reps, products, and monthly sales figures. Download a suitable dataset from Kaggle, such as the Superstore Dataset.

Visualization Goals

• Compare sales performance across regions.
• Track sales trends over time.

Example

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load sales dataset
sales_data = pd.read_csv('Superstore.csv')

# Create a FacetGrid for sales performance by region
g = sns.FacetGrid(sales_data, col="Region", hue="Category", height=4, aspect=1)
g.map(sns.lineplot, "Order Date", "Sales")
g.add_legend()
plt.show()

Output:

Project 3: Health Metrics Analysis

Dataset

Health metrics data including patient records, treatments, outcomes, and demographics. Download a suitable dataset from Kaggle, such as the Heart Disease Dataset.

Visualization Goals

• Analyze the distribution of various health metrics.
• Identify correlations between different health parameters.
• Visualize patient outcomes across different demographic groups.

Example

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

# Load the health metrics dataset
health_data = pd.read_csv('heart.csv')

# Pair plot for health metrics
sns.pairplot(health_data[['age', 'trestbps', 'chol', 'thalach', 'target']], hue='target', palette='coolwarm')
plt.title('Health Metrics Analysis')
plt.show()

# Heatmap for correlation between health metrics
corr = health_data.corr()
sns.heatmap(corr, annot=True, cmap='viridis')
plt.title('Correlation Heatmap of Health Metrics')
plt.show()

# Box plot for patient outcomes by age group
health_data['age_group'] = pd.cut(health_data['age'], bins=[20, 40, 60, 80], labels=['20-40', '40-60', '60-80'])
sns.boxplot(data=health_data, x='age_group', y='thalach', hue='target')
plt.title('Patient Outcomes by Age Group')
plt.xlabel('Age Group')
plt.ylabel('Max Heart Rate Achieved')
plt.legend(title='Disease Presence')
plt.show()

Output:

Project 4: Technology Usage Trends

Dataset

Technology usage data including app usage, screen time, and demographic information. Download a suitable dataset from Kaggle, such as the Technology Adoption Dataset. OR use this data

Visualization Goals

• Analyze trends in technology usage over time.
• Compare usage patterns across different demographics.
• Visualize the impact of technology on productivity.

Example

Project 5: Data Science Job Salaries Analysis

Dataset

Data on job salaries for data science positions across different years. A suitable dataset is the Data Science Job Salaries Dataset from Kaggle.

Visualization Goals

• Analyze trends in data science salaries over different years.
• Compare salary distributions across various job titles.
• Visualize the impact of experience and location on salaries.

Example

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

# Load the Data Science job salaries dataset
salary_data = pd.read_csv('data_science_salaries.csv')

# Convert the 'year' column to datetime for better handling
salary_data['year'] = pd.to_datetime(salary_data['year'], format='%Y')

# Line plot for salaries over the years
plt.figure(figsize=(12, 6))
sns.lineplot(data=salary_data, x='year', y='salary', hue='job_title', palette='Set1')
plt.title('Data Science Job Salaries Over the Years')
plt.xlabel('Year')
plt.ylabel('Average Salary (USD)')
plt.legend(title='Job Title')
plt.grid(True)
plt.show()

# Box plot for salary distribution by job title
plt.figure(figsize=(12, 6))
sns.boxplot(data=salary_data, x='job_title', y='salary', palette='Set2')
plt.title('Salary Distribution by Job Title')
plt.xlabel('Job Title')
plt.ylabel('Salary (USD)')
plt.xticks(rotation=45)
plt.show()

# FacetGrid for salary trends by experience level
salary_data['experience_level'] = pd.Categorical(salary_data['experience_level'], 
                                                  categories=['Junior', 'Mid-level', 'Senior', 'Lead'], 
                                                  ordered=True)
g = sns.FacetGrid(salary_data, col="experience_level", hue="job_title", height=4, aspect=1)
g.map(sns.lineplot, "year", "salary")
g.add_legend()
plt.title('Salary Trends by Experience Level')
plt.xlabel('Year')
plt.ylabel('Salary (USD)')
plt.show()

# seaborn_functions

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

# Load example datasets
tips = sns.load_dataset('tips')
iris = sns.load_dataset('iris')
flights = sns.load_dataset('flights')
diamonds = sns.load_dataset('diamonds')

# Set the aesthetic style of the plots
sns.set(style="whitegrid")

# 1. Scatter Plot
# Used to plot the relationship between two variables with a dot for each data point.
sns.scatterplot(data=iris, x='sepal_length', y='sepal_width', hue='species')
plt.title('Scatter Plot of Sepal Length vs Sepal Width')
plt.show()

# 2. Line Plot
# Used to plot continuous data points as a line.
sns.lineplot(data=flights, x='year', y='passengers')
plt.title('Line Plot of Passengers Over Years')
plt.show()

# 3. Histogram
# Used to plot the distribution of a single variable.
sns.histplot(data=tips, x='total_bill', bins=30, kde=True)
plt.title('Histogram of Total Bill')
plt.show()

# 4. Box Plot
# Used to show the distribution of a quantitative variable and to compare between levels of a categorical variable.
sns.boxplot(data=tips, x='day', y='total_bill')
plt.title('Box Plot of Total Bill by Day')
plt.show()

# 5. Violin Plot
# Used to show the distribution of the data across different categories. It combines aspects of a box plot and a kernel density plot.
sns.violinplot(data=tips, x='day', y='total_bill', hue='sex', split=True)
plt.title('Violin Plot of Total Bill by Day and Sex')
plt.show()

# 6. Bar Plot
# Used to plot the mean value of a numeric variable for each category.
sns.barplot(data=tips, x='day', y='total_bill', ci=None)
plt.title('Bar Plot of Average Total Bill by Day')
plt.show()

# 7. Count Plot
# Used to show the counts of observations in each categorical bin using bars.
sns.countplot(data=tips, x='day')
plt.title('Count Plot of Days')
plt.show()

# 8. Heatmap
# Used to display data in a matrix form where values are represented with color intensity.
pivot_table = flights.pivot('month', 'year', 'passengers')
sns.heatmap(pivot_table, annot=True, fmt="d", cmap='YlGnBu')
plt.title('Heatmap of Passengers')
plt.show()

# 9. Pair Plot
# Used to plot pairwise relationships in a dataset. It creates a matrix of scatter plots.
sns.pairplot(iris, hue='species')
plt.title('Pair Plot of Iris Dataset')
plt.show()

# 10. Joint Plot
# Used to draw a plot of two variables with bivariate and univariate graphs.
sns.jointplot(data=tips, x='total_bill', y='tip', kind='reg')
plt.title('Joint Plot of Total Bill and Tip')
plt.show()

# 11. Relational Plot
# Used to plot multiple relationships in a dataset. It serves as a general interface for plotting multiple data points.
sns.relplot(data=tips, x='total_bill', y='tip', hue='day', style='time')
plt.title('Relational Plot of Total Bill and Tip')
plt.show()

# 12. Facet Grid
# Used to plot multiple smaller plots (facets) in a grid layout.
g = sns.FacetGrid(tips, col='sex', hue='smoker')
g.map(sns.scatterplot, 'total_bill', 'tip')
g.add_legend()
plt.show()

# 13. Regression Plot
# Used to plot a linear regression model fit between two variables.
sns.regplot(data=tips, x='total_bill', y='tip')
plt.title('Regression Plot of Total Bill and Tip')
plt.show()

# 14. Residual Plot
# Used to plot the residuals of a linear regression.
sns.residplot(data=tips, x='total_bill', y='tip')
plt.title('Residual Plot of Total Bill and Tip')
plt.show()

# 15. KDE Plot
# Used to plot the distribution of a continuous variable. KDE stands for Kernel Density Estimate.
sns.kdeplot(data=diamonds, x='price', hue='cut', fill=True)
plt.title('KDE Plot of Diamond Prices by Cut')
plt.show()

# 16. Strip Plot
# Used to plot a categorical scatter plot.
sns.stripplot(data=tips, x='day', y='total_bill', jitter=True)
plt.title('Strip Plot of Total Bill by Day')
plt.show()

# 17. Swarm Plot
# Used to plot a categorical scatter plot with points adjusted to avoid overlap.
sns.swarmplot(data=tips, x='day', y='total_bill', hue='sex')
plt.title('Swarm Plot of Total Bill by Day and Sex')
plt.show()

# 18. Boxen Plot
# Used to plot the distribution of a quantitative variable and is particularly useful for large datasets.
sns.boxenplot(data=diamonds, x='cut', y='price')
plt.title('Boxen Plot of Diamond Prices by Cut')
plt.show()

# 19. Cat Plot
# A general interface for drawing categorical plots. It can be used to create many types of plots based on the kind parameter.
sns.catplot(data=tips, x='day', y='total_bill', kind='box', hue='sex')
plt.title('Cat Plot of Total Bill by Day and Sex')
plt.show()

# 20. Point Plot
# Used to plot the mean value of a numeric variable for each category with confidence intervals.
sns.pointplot(data=tips, x='day', y='total_bill', hue='sex')
plt.title('Point Plot of Total Bill by Day and Sex')
plt.show()

5. Using Kaggle

Create a Kaggle Account

1. Go to Kaggle.
2. Sign up for an account or log in if you already have one.

Start a New Kernel

1. Navigate to “Kernels” in the top menu.
2. Click on “New Kernel” and select “Notebook”.

Upload Your Data

1. In your new notebook, click on the “Add Data” button.
2. Search for the dataset you want to use or upload your own dataset.

Write Your Code

1. Write and execute your code in the notebook.
2. Use cells to organize your code and visualizations.

Save and Publish

1. Click “Save Version” to save your work.
2. Click “Publish” to share your notebook with others.

Example Kernel

• Example kernel demonstrating the analysis of customer segmentation: Example Kernel

6. Best Practices

• Data Preparation: Clean and preprocess your data before visualization.
• Visualization Choice: Choose the type of visualization that best represents your data.
• Color and Style: Use color palettes and styles that enhance readability and interpretation.

7. Conclusion

Advanced data visualization with Seaborn allows for deep insights into complex datasets. By utilizing various plotting techniques and customization options, you can create informative and aesthetically pleasing visualizations that help in making data-driven decisions. Explore different datasets, experiment with various plots, and apply best practices to enhance your data visualization skills.