Nivesh Bansal

Posted on Sep 3

Mastering Distribution & Category Plots in Data Visualization

#datavisualization #machinelearning #python #seaborn

Data visualization is one of the most powerful skills in data analysis, machine learning, and reporting. Among all visualization techniques, distribution plots and category plots are the two most essential families that every analyst, data scientist, or developer must master.

🔗 Full resource + code here: GitHub Repo

In this article, we’ll go step by step to understand:

What are Distribution Plots?
What are Category Plots?
Their types with comparison tables
Industry-level examples with Python & Seaborn code
Best practices and when to use which plot

By the end, you’ll know exactly which plot to use for your data storytelling.

What are Distribution Plots?

👉 Definition: Distribution plots are used to understand how data values are spread out. They help in analyzing the frequency, density, outliers, and shape of numeric variables.

👉 Use Case: Whenever you want to answer: “How are my values distributed?” (e.g., customer spending, test scores, sales revenue).

Top 5 Industry-Level Distribution Plots

Plot	Use Case	Example Code
Histogram	First step in EDA, shows frequency distribution of numeric values.	`sns.histplot(tips["total_bill"])`
KDE Plot	Smooth curve showing probability density (better for comparing).	`sns.kdeplot(tips["tip"])`
Box Plot	Detects outliers, median, quartiles. Standard in dashboards.	`sns.boxplot(x=tips["day"], y=tips["total_bill"])`
Violin Plot	Combination of Box + KDE. Shows full shape of distribution.	`sns.violinplot(x="day", y="tip", data=tips)`
Pair Plot	Scatterplot matrix for relationships between multiple numeric variables.	`sns.pairplot(tips, vars=["total_bill","tip","size"])`

Pro Tip: Start with a Histogram → then refine with KDE, Box, or Violin depending on what you need (frequency, density, or outliers).

What are Category Plots?

👉 Definition: Category plots are used when one variable is categorical (like gender, day, region) and another is numeric. They help in comparing groups or categories.

👉 Use Case: Whenever you want to answer: “How do categories compare on a metric?” (e.g., average sales by region, tips by day).

Top 5 Industry-Level Category Plots

Plot	Use Case	Example Code
Count Plot	Shows frequency of each category.	`sns.countplot(x="day", data=tips)`
Bar Plot	Shows mean/aggregate of numeric value per category.	`sns.barplot(x="day", y="tip", data=tips)`
Box Plot	Category-wise spread + outliers.	`sns.boxplot(x="day", y="total_bill", data=tips)`
Violin Plot	Category-wise distribution + density shape.	`sns.violinplot(x="day", y="tip", data=tips)`
Point Plot	Highlights category trends with confidence intervals.	`sns.pointplot(x="day", y="tip", data=tips)`

Pro Tip: Use Count/Bar for summary, Box/Violin for deeper distribution, and Point Plot for trends.

Distribution vs Category Plots (Comparison)

Feature	Distribution Plots	Category Plots
Data Type	Numeric-only	Categorical + Numeric
Purpose	Shape, spread, outliers of numeric data	Compare metrics across groups
Best First Step	Histogram	Count Plot
Industry Use	EDA, density analysis, outlier detection	Reporting, dashboards, comparisons

Code Previews (Seaborn + Tips Dataset)

Histogram Example

sns.histplot(tips["total_bill"])
plt.title("Histogram of Total Bill")
plt.show()

Count Plot Example

sns.countplot(x="day", data=tips)
plt.title("Count of Customers per Day")
plt.show()

Box Plot Example

sns.boxplot(x="day", y="total_bill", data=tips)
plt.title("Bill Distribution by Day")
plt.show()

Violin Plot Example

sns.violinplot(x="day", y="tip", data=tips)
plt.title("Tip Distribution by Day")
plt.show()

Pair Plot Example

sns.pairplot(tips, vars=["total_bill", "tip", "size"], hue="sex")
plt.suptitle("Pairwise Numeric Relationships")
plt.show()

Best Practices

Start simple: Use Histogram or Count Plot first.
For outlier detection, always check Box Plot.
For comparison of categories, prefer Bar/Point Plot.
For distribution shape, use KDE or Violin.
For multi-variable insights, use Pair Plot.

Final Thoughts

Distribution Plots = Shape & spread of numeric data.
Category Plots = Comparison across groups/categories.

Both are equally essential for industry-level data analysis, machine learning feature exploration, and dashboards. If you master these 10 plots, you’ll cover 80–90% of real-world visualization needs.