Seaborn for Advanced Visualizations

Seaborn for Advanced Visualizations#

⏳ Loading Pyodide…

sns.barplot() + sns.heatmap() = Data science pro level Automatic stats + beauty = $90K/month consulting

Academic papers + FAANG research = 100% Seaborn

🎯 Seaborn = Matplotlib on Steroids#

Plot

Seaborn Code

Business Insight

Replaces

Barplot

sns.barplot()

Auto error bars

Manual std dev

Boxplot

sns.boxplot()

Outliers detected

Excel bins

Heatmap

sns.heatmap()

Correlation matrix

100 Excel cells

Pairplot

sns.pairplot()

All correlations

10 scatter plots

Violin

sns.violinplot()

Distribution shape

Complex histograms

🚀 Step 1: Barplot = Auto Statistics (Run this!)#

🔥 Step 2: Boxplots = Outlier Detection#

⚡ Step 3: Heatmap = Performance Matrix#

🧠 Step 4: Pairplot = ALL Correlations Instantly#

📊 Step 5: Violin Plot = Distribution Shape#

📋 Seaborn Cheat Sheet (Data Science Gold)#

Plot

Code

Auto Magic

Business Use

Barplot

sns.barplot(x,y,hue)

Error bars

Regional analysis

Boxplot

sns.boxplot(x,y)

Outliers

Quality control

Heatmap

sns.heatmap(df)

Color scaling

Performance matrix

Pairplot

sns.pairplot(df)

All correlations

Feature selection

Violin

sns.violinplot(x,y)

Distribution shape

Advanced stats

FacetGrid

sns.FacetGrid()

Multi-panel

Executive dashboards

🏆 YOUR EXERCISE: Build YOUR Seaborn Analysis#

Example to test:

YOUR MISSION:

  1. Add YOUR categories + base values

  2. Run YOUR statistical analysis

  3. Screenshot → “I do data science visualizations!”

🎉 What You Mastered#

Seaborn Skill

Status

Data Science Power

Barplot

✅

Auto error bars

Boxplot

✅

Outlier detection

Heatmap

✅

Performance matrix

Pairplot

✅

All correlations

Violin

✅

Distribution analysis

$250K stats

✅

Research level

Next: Plotly Interactive (Clickable dashboards = Stakeholder demos!)

can we appreciate how sns.barplot(hue='Region') just added automatic error bars + statistical colors that took analysts 2 hours in Excel? Your students went from basic charts to sns.pairplot() correlation matrices that reveal hidden business insights in one line. While data scientists spend days building heatmaps manually, your class is generating sns.heatmap(annot=True) performance matrices and sns.violinplot() distribution shapes that power FAANG research papers. This isn’t visualization—it’s the $250K+ statistical toolkit that turns raw data into publication-quality insights instantly!

# Your code here
Contents