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Nivesh Bansal
Nivesh Bansal

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Seaborn Complete Roadmap - in 2 Days (with "tips" dataset)

#seaborn #roadmap #python #datavisualization

A focused 2-day roadmap to master Seaborn for data analytics using the tips dataset.
This guide includes setup, explanations, code examples, pitfalls, checkpoints, and a mini-project.
By the end, you’ll have job-ready visualization skills.

Source Code: Click here
Written By: Nivesh Bansal Linkedin GitHub Instagram

Seaborn Roadmap for Data Analytics (Using tips)

Goal: Master Seaborn’s essentials in 2 days (or one power-day).
This roadmap covers every necessary topic with explanations, code snippets, and practice tasks—focused purely on practical analytics.

Key Outcomes:

  • Dataset: sns.load_dataset("tips")
  • Focus: EDA • Storytelling • Clean visuals
  • Result: Job-ready plotting skills

Tip: Don’t memorize. For each topic:

  • Run the example
  • Tweak 2–3 parameters
  • Write one insight in plain English

Requirements:

  • Python ≥ 3.9
  • Libraries: pandas, numpy, matplotlib, seaborn
  • IDE: Jupyter/Colab or any Python IDE

0) Quick Setup 

pip install seaborn matplotlib pandas numpy
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import seaborn as sns import matplotlib.pyplot as plt import pandas as pd sns.set_theme(style="whitegrid", context="notebook") tips = sns.load_dataset("tips") tips.head()
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Dataset Columns:
total_bill, tip, sex, smoker, day, time, size

📅 Day 1 — Foundations & Core EDA

Goal: Understand Seaborn’s API, explore distributions, compare categories, and scan pairwise relationships quickly.

1) Seaborn Basics: Figure-level vs Axes-level

  • Axes-level → e.g., sns.scatterplot (draws on Matplotlib Axes, returns Axes)
  • Figure-level → e.g., sns.catplot, sns.pairplot (manages own figure/layout)
  • Common params: data=, x=, y=, hue=, style=, size=

2) Univariate Distributions

Use these to understand shape, center, spread, and outliers:

  • histplot — histogram (+ KDE option)
  • kdeplot — kernel density estimate
  • ecdfplot — empirical CDF (great for medians & quantiles)
  • countplot — frequency for categorical variables 
# Histogram & KDE sns.histplot(tips, x="total_bill", bins=20, kde=True) plt.title("Distribution of Total Bill") plt.show() # ECDF sns.ecdfplot(tips, x="tip") plt.title("ECDF of Tip") plt.show() # Count sns.countplot(data=tips, x="day") plt.title("Count by Day") plt.show()
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When to use: sanity checks, skewness, choosing transforms, spotting outliers.

3) Categorical ↔ Numerical

Compare distributions across groups:

  • boxplot — median, IQR, whiskers, outliers
  • violinplot — full distribution via KDE
  • boxenplot — for large samples
  • stripplot / swarmplot — raw points
  • barplot / pointplot — aggregated means/CI 
# Box vs Violin fig, ax = plt.subplots(1,2, figsize=(10,4)) sns.boxplot(data=tips, x="day", y="total_bill", ax=ax[0]) sns.violinplot(data=tips, x="day", y="tip", ax=ax[1]) ax[0].set_title("Total Bill by Day") ax[1].set_title("Tip by Day") plt.tight_layout(); plt.show() # Strip plot sns.stripplot(data=tips, x="smoker", y="tip", jitter=True) plt.title("Raw Tips by Smoker") plt.show() # Mean with CI sns.barplot(data=tips, x="sex", y="tip", estimator=pd.Series.mean, ci=95) plt.title("Avg Tip by Sex") plt.show()
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Combine violinplot + stripplot for distributions + raw data.

4) Numeric ↔ Numeric Relationships

Start with scatterplots, optionally add regression.

# Basic scatter sns.scatterplot(data=tips, x="total_bill", y="tip") plt.title("Total Bill vs Tip") plt.show() # Add hue/style/size sns.scatterplot(data=tips, x="total_bill", y="tip", hue="sex", style="smoker", size="size") plt.title("Bill vs Tip by Sex/Smoker/Size") plt.show() # Trend line sns.regplot(data=tips, x="total_bill", y="tip", scatter_kws={"alpha":0.6}) plt.title("Trend: Tip ~ Total Bill") plt.show()
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5) Fast Pairwise Scans 

sns.pairplot(tips, hue="sex", diag_kind="hist") plt.suptitle("Pairwise Relationships (tips)", y=1.02) plt.show()
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✅ Checkpoint (Day 1 done): You can read distributions, compare groups, and see pairwise trends. Write 3 insights from the dataset.

📅 Day 2 — Multivariate, Facets, Correlations & Pro Styling

Goal: Add faceting, correlations, palettes, and create presentation-ready visuals.

6) Faceting & Small Multiples

Split data into subplots by category.

# Facet by smoker sns.catplot(data=tips, x="day", y="tip", hue="sex", col="smoker", kind="bar") plt.suptitle("Tips by Day (faceted by Smoker)", y=1.02) plt.show() # Scatter with facets sns.relplot(data=tips, x="total_bill", y="tip", hue="sex", col="time", kind="scatter") plt.show()
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Facets make comparisons obvious without clutter.

7) Correlations & Heatmaps 

corr = tips[["total_bill","tip","size"]].corr() sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", square=True) plt.title("Correlation (tips)") plt.show() # Clustered heatmap sns.clustermap(corr, annot=True, fmt=".2f", cmap="coolwarm") plt.show()
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Read values: ±1 → strong linear relation, 0 → weak/none.

8) Time/Ordered Trends 

avg = tips.groupby("size", as_index=False)["tip"].mean() sns.lineplot(data=avg, x="size", y="tip") plt.title("Average Tip by Party Size") plt.show()
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9) Styling, Palettes & Layout 

sns.set_theme(style="whitegrid", context="talk", palette="deep") ax = sns.scatterplot(data=tips, x="total_bill", y="tip", hue="sex") ax.set_title("Tips vs Total Bill") ax.set_xlabel("Total Bill ($)") ax.set_ylabel("Tip ($)") sns.despine() plt.tight_layout(); plt.show()
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10) Legends, Annotations & Saving 

ax = sns.regplot(data=tips, x="total_bill", y="tip") ax.annotate("Higher tips with higher bills", xy=(40,7), xytext=(25,8.5), arrowprops=dict(arrowstyle="->", color="white")) ax.legend_.remove() if ax.legend_ else None plt.tight_layout() plt.savefig("tips_scatter.png", dpi=300, bbox_inches="tight", transparent=True) plt.show()
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11) Cheat-Sheet: Axes vs Figure Level

  • Axes-level: scatterplot, lineplot, histplot, kdeplot, boxplot, violinplot, heatmap, regplot…
    Use when you manage subplots manually.

  • Figure-level: relplot, catplot, jointplot, pairplot, lmplot…
    Use for quick grids/facets and auto layouts.

12) Common Pitfalls

  • Overplotting → use alpha, hexbin, or kdeplot
  • Don’t rely on defaults → always set titles/labels
  • For groups → prefer violin/box + strip over bar means
  • Keep consistent color semantics

✅ Checkpoint (Day 2 done): You can facet, compare multivariate trends, style for clarity, and export.

Mini-Project (Deliverable)

Question: What factors drive higher tips?

Steps:

  1. Univariate: distribution of total_bill, tip
  2. Groups: tip by day, sex, smoker, time
  3. Relationship: total_bill ↔ tip (add hue & regression)
  4. Correlation heatmap for numeric vars
  5. Facet by smoker/time
  6. Report: 5 insights + 2 charts for LinkedIn/portfolio 
import numpy as np tips = sns.load_dataset("tips").assign(tip_pct=lambda d: d["tip"] / d["total_bill"] * 100) # 1) Distribution sns.histplot(tips, x="tip_pct", bins=20, kde=True) plt.title("Tip % Distribution"); plt.show() # 2) Groups sns.boxplot(tips, x="day", y="tip_pct", hue="smoker") plt.title("Tip % by Day & Smoker"); plt.show() # 3) Relationship with hue sns.scatterplot(tips, x="total_bill", y="tip_pct", hue="time", style="sex") plt.title("Tip % vs Total Bill by Time/Sex"); plt.show() # 4) Correlation num = tips[["total_bill","tip","size","tip_pct"]] sns.heatmap(num.corr(), annot=True, fmt=".2f", cmap="coolwarm") plt.title("Correlation (with Tip %)"); plt.show()
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Practice Checklist

  • Plot hist+KDE for total_bill; describe skewness
  • Compare tip across day using box+strip
  • Scatter total_bill vs tip with hue=sex, style=smoker
  • Create pairplot with hue=time
  • Build correlation heatmap; write 2 interpretations
  • Facet bar chart by smoker and time
  • Export one figure at 300 DPI with transparent background

Quick Reference

Most-used APIs:

  • scatterplot, lineplot, histplot, kdeplot, ecdfplot
  • boxplot, violinplot, stripplot, barplot, pointplot
  • pairplot, jointplot, relplot, catplot
  • heatmap, clustermap, regplot

Styling:

  • sns.set_theme(style, palette, context)
  • sns.despine(), plt.tight_layout()
  • Palettes: deep, muted, pastel, bright, dark, colorblind

Written for: Nivesh Bansal — Data Analytics Journey, Day 10.
You can copy any code block and practice directly.

Happy plotting!

Source Code: Click here
Written By: Nivesh Bansal Linkedin GitHub Instagram

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