• Data Science Foundations
  • I PREFACE
  • Welcome to the General Data Science – Visualization (VIZ) Layer
    • 📘 What You’ll Learn
    • 🌟 Turn Data Into Insight
  • II DATA VISUALIZATION
  • 1 What are common data types in Python and R?
    • 1.1 Explanation
    • 1.2 Common Data Types in Python and R
  • 2 How do you inspect variable types in a dataset?
    • 2.1 Explanation
    • 2.2 Python Code
    • 2.3 R Code
  • 3 How do you convert variable types in a dataset?
    • 3.1 Explanation
    • 3.2 Python Code
    • 3.3 R Code
  • 4 What is the difference between categorical and numerical variables?
    • 4.1 Explanation
      • 4.1.1 🔷 Categorical Variables
      • 4.1.2 🔶 Numerical Variables
  • 5 How do you summarize numerical and categorical variables?
    • 5.1 Explanation
    • 5.2 Python Code
    • 5.3 R Code
  • 6 What are the most effective plots for comparing values across categories?
    • 6.1 Explanation
      • 🔹 Common Visualization Types
      • 📌 Choosing the Right Plot
  • 7 How do you visualize category counts using a bar plot?
    • 7.1 Explanation
    • 7.2 Python Code
    • 7.3 R Code
  • 8 How do you compare group distributions using a boxplot?
    • 8.1 Explanation
    • 8.2 Python Code
    • 8.3 R Code
  • 9 How do you compare distribution shape and summary stats using a violin plot?
    • 9.1 Explanation
    • 9.2 Python Code
    • 9.3 R Code
  • 10 How do you visualize overlapping group distributions using a ridge plot?
    • 10.1 Explanation
    • 10.2 Python Code
    • 10.3 R Code
  • 11 How do you display individual data points by category using a swarm plot?
    • 11.1 Explanation
    • 11.2 Python Code
    • 11.3 R Code
  • 12 How do you show raw observations by group using a strip plot?
    • 12.1 Explanation
    • 12.2 Python Code
    • 12.3 R Code
  • 13 How do you show group means and variability using a bar plot?
    • 13.1 Explanation
    • 13.2 Python Code
    • 13.3 R Code
  • 14 How do you show group summaries using a dot plot?
    • 14.1 Explanation
    • 14.2 Python Code
    • 14.3 R Code
  • 15 How do you show frequency patterns using a histogram?
    • 15.1 Explanation
    • 15.2 Python Code
    • 15.3 R Code
  • 16 How do you visualize a smoothed distribution with a density plot?
    • 16.1 Explanation
    • 16.2 Python Code
    • 16.3 R Code
  • 17 How do you visualize two categorical variables with a grouped bar plot?
    • 17.1 Explanation
    • 17.2 Python Code
    • 17.3 R Code
  • 18 How do you visualize trends across ordered groups using a line plot?
    • 18.1 Explanation
    • 18.2 Python Code
    • 18.3 R Code
  • 19 How do you visualize trends for multiple groups using a line plot?
    • 19.1 Explanation
    • 19.2 Python Code
    • 19.3 R Code
  • 20 How do you show overall trend patterns using a smoothed line?
    • 20.1 Explanation
    • 20.2 Python Code
    • 20.3 R Code
  • III PATTERN RECOGNITION AND RELATIONSHIPS
  • 21 How do you visualize patterns and relationships in multivariate data?
    • 21.1 Explanation
      • 21.1.1 Key tools for visualizing relationships
      • 21.1.2 👇 Core Questions Explored in This Section
  • 22 How do you uncover relationships between multiple variables using a pair plot?
    • 22.1 Explanation
    • 22.2 Python Code
    • 22.3 R Code
  • 23 How do you compare distributions across groups using facet plots?
    • 23.1 Explanation
    • 23.2 Python Code
    • 23.3 R Code
  • 24 How do you enhance scatter plots by adding group color and trend lines?
    • 24.1 Explanation
    • 24.2 Python Code
    • 24.3 R Code
  • 25 How do you quantify linear relationships between numerical variables using a correlation heatmap?
    • 25.1 Explanation
    • 25.2 Python Code
    • 25.3 R Code
  • 26 How do you visualize patterns across multiple numeric features using a parallel coordinates plot?
    • 26.1 Explanation
    • 26.2 Python Code
    • 26.3 R Code
  • 27 How do you uncover structure in high-dimensional data using a PCA plot?
    • 27.1 Explanation
    • 27.2 Python Code
    • 27.3 R Code
  • 28 How do you visualize clustering patterns in high-dimensional data using a t-SNE plot?
    • 28.1 Explanation
    • 28.2 Python Code
    • 28.3 R Code
  • 29 How do you explore complex patterns in high-dimensional data using a UMAP plot?
    • 29.1 Explanation
    • 29.2 Python Code
    • 29.3 R Code (UMAP via uwot)
  • 30 How do you visualize simple proportions using a pie chart?
    • 30.1 Explanation
    • 30.2 Python Code
    • 30.3 R Code
  • 31 How do you create a donut chart to show part-to-whole proportions?
    • 31.1 Explanation
    • 31.2 R Code
  • 32 How do you visualize hierarchical part-to-whole relationships using a treemap?
    • 32.1 Explanation
    • 32.2 Python Code — Interactive
    • 32.3 Python Code — Static
    • 32.4 R Code
  • 33 How do you visualize overlaps using a Venn diagram?
    • 33.1 Explanation
    • 33.2 Python Code
    • 33.3 R Code
  • VIZ Summary
    • 🎨 What You’ve Accomplished
    • 📐 What Comes After Visualization?
    • 🚀 Continue Learning with CDI
  • Explore More Guides

Visualizing Data Patterns with Python and R

Visualizing Data Patterns with Python and R


Last updated: July 06, 2025