Plotting graph using Seaborn | Python (original) (raw)

Last Updated : 24 Feb, 2026

Seaborn is a Python data visualization library built on top of Matplotlib. It provides a high-level interface for drawing attractive, informative statistical graphics. Unlike Matplotlib, Seaborn works seamlessly with Pandas DataFrames, making it a preferred tool for quick exploratory data analysis and advanced statistical plotting.

Comes with built-in datasets like iris, tips, etc.
Provides statistical plots such as boxplots, violin plots, swarm plots, etc.
Handles categorical data visualization better than Matplotlib.
Supports aesthetic customization (themes, color palettes, styles).
Simplifies working with DataFrames by auto-labeling axes.

Different Plots in Seaborn

Let's see the various types of plots in seaborn,

1. Strip Plot

A strip plot is a categorical scatter plot where data points are plotted along one categorical axis. It is useful for visualizing the distribution of values but may suffer from overlapping points.

**Applications

Used when we want to visualize raw distribution of numerical data across categories.
Helpful for detecting clusters or general spread of values.

**Advantages

Simple and easy to interpret.
Shows individual data points clearly.

**Limitations

Overlapping points may cause loss of clarity in dense datasets. Python `

import matplotlib.pyplot as plt import seaborn as sns

x = ['sun', 'mon', 'fri', 'sat', 'tue', 'wed', 'thu'] y = [5, 6.7, 4, 6, 2, 4.9, 1.8]

ax = sns.stripplot(x=x, y=y) ax.set(xlabel='Days', ylabel='Amount Spent') plt.title('Daily Spending (Custom Data)') plt.show()

**Output:

plot

Simple Plot

2. Swarm Plot

A swarm plot is similar to a strip plot, but points are arranged to avoid overlap. This ensures all data points are visible, making it more informative.

**Applications

Useful when dataset is small/medium and we want to show all observations.
Comparing sub-groups clearly without stacking.

**Advantages

Prevents overlap of data points.
Provides clearer visual insight than strip plot.

**Limitations

Can be slow for large datasets.
May look cluttered when categories have thousands of points. Python `

sns.set(style="whitegrid") iris = sns.load_dataset("iris") sns.swarmplot(x="species", y="sepal_length", data=iris) plt.title("Swarm Plot of Sepal Length by Species") plt.show()

**Output:

swarn

Swarm Plot

3. Bar Plot

A bar plot shows the average (by default mean) of a numerical variable across categories. It can use different estimators (mean, median, std, etc.) for aggregation.

**Applications

Comparing average values across categories.
Displaying results of group-by operations visually.

**Advantages

Easy to interpret and widely used.
Flexible can use different statistical functions.

**Limitations

Does not show individual data distribution.
Can hide variability when using only mean. Python `

tips = sns.load_dataset("tips") sns.barplot(x="sex", y="total_bill", data=tips, palette="plasma") plt.title("Average Total Bill by Gender") plt.show()

**Output:

bar-plot

Bar Plot

4. Count Plot

A count plot simply counts the occurrences of each category. It is like a histogram for categorical variables.

**Applications

Checking frequency distribution of categorical values.
Understanding class imbalance in data.

**Advantages

Very simple and quick to interpret.
No need for numerical data, only categorical required.

**Limitations

Cannot display numerical spread inside categories. Python `

tips = sns.load_dataset("tips") sns.countplot(x="sex", data=tips) plt.title("Count of Gender in Dataset") plt.show()

**Output:

count-plot

Count Plot

5. Box Plot

A box plot (or whisker plot) summarizes numerical data using quartiles, median and outliers. It helps in detecting variability and spread.

**Applications

Detecting outliers.
Comparing spread of distributions across categories.

**Advantages

Highlights summary statistics effectively.
Useful for large datasets.

**Limitations

Does not show exact data distribution shape. Python `

tips = sns.load_dataset("tips") sns.boxplot(x="day", y="total_bill", data=tips, hue="smoker") plt.title("Total Bill Distribution by Day & Smoking Status") plt.show()

**Output:

boxplot

Box Plot

6. Violin Plot

A violin plot combines a box plot with a density plot, showing both summary stats and distribution shape.

**Applications

Comparing distributions more deeply than boxplot.
Helpful for detecting multimodal distributions.

**Advantages

Shows both summary statistics and data distribution.
Easier to see differences in distribution shapes.

**Limitations

Can be harder to interpret for beginners.
May be misleading if sample size is small. Python `

tips = sns.load_dataset("tips") sns.violinplot(x="day", y="total_bill", data=tips, hue="sex", split=True) plt.title("Violin Plot of Total Bill by Day and Gender") plt.show()

**Output:

violin-plot

Violin Plot

7. Strip Plot with Hue

This is an enhanced strip plot where categories are further divided using hue. It allows comparing multiple sub-groups within a category.

**Applications

Comparing subgroups inside categories.
Visualizing interaction between two categorical variables.

**Advantages

Adds extra dimension to strip plot.
Useful for multivariate visualization.

**Limitations

Overlap issue exists. Python `

tips = sns.load_dataset("tips") sns.stripplot(x="day", y="total_bill", data=tips, jitter=True, hue="smoker", dodge=True) plt.title("Total Bill Distribution with Smoking Status") plt.show()

**Output:

strip-with-hue

Strip Plot with Hue

Applications

**Exploratory Data Analysis (EDA): Identifying trends, outliers and patterns.
**Feature Analysis: Comparing numerical features across categories.
**Data Presentation: Creating professional, publication-ready plots.
**Model Preparation: Checking class imbalance or spread before training models.