Data Visualisation with Seaborn

Data Visualisation with Seaborn#

In this chapter, we’ll learn how to create beautiful and informative visualisations using seaborn. Data visualisation is one of the most important skills in data analysis — a good chart can reveal patterns that are invisible in raw numbers.

Why Visualise Data?#

Consider this: you have a dataset with 10,000 rows of sales data. You could:

Scroll through thousands of numbers (tedious and ineffective)
Calculate summary statistics (helpful but limited)
Create a chart that shows the pattern instantly (this is what we want!)

Visualisations help you:

Explore your data to find patterns
Communicate findings to others
Identify outliers and anomalies
Compare groups or categories

Python Visualisation Libraries#

There are several libraries for data visualisation in Python:

Library	Description
Matplotlib	The most versatile and customisable — but verbose
Seaborn	Built on matplotlib, easier for beginners, beautiful defaults
Plotnine	Library that is very close to R’s ggplot2 (probably not updated regularly)
Lets-plot	Another library that is very close to ggplot2 (it is maintained regularly)

We’ll focus on seaborn because it’s easy to learn and produces publication-quality plots with minimal code. I would suggest you learn Matplotlib later at some point.

Setting Up#

Let’s import the libraries we need:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Apply seaborn's default theme for nicer-looking plots
sns.set_theme()

Note

We import matplotlib as plt because seaborn is built on top of it. We’ll use plt.show() to display our plots.

Loading Example Data#

Seaborn comes with several built-in datasets that are perfect for learning. Let’s load the famous “tips” dataset:

df_tips = sns.load_dataset('tips')
df_tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

This dataset contains information about restaurant bills and tips. Let’s explore it:

df_tips.describe()

	total_bill	tip	size
count	244.000000	244.000000	244.000000
mean	19.785943	2.998279	2.569672
std	8.902412	1.383638	0.951100
min	3.070000	1.000000	1.000000
25%	13.347500	2.000000	2.000000
50%	17.795000	2.900000	2.000000
75%	24.127500	3.562500	3.000000
max	50.810000	10.000000	6.000000

Scatter Plots#

A scatter plot shows the relationship between two numerical variables. Each point represents one observation.

Basic Scatter Plot#

Let’s see if there’s a relationship between the total bill and the tip:

sns.scatterplot(data=df_tips, x=df_tips['total_bill'], y=df_tips['tip'])
plt.show()

../_images/1107fb657a2d08f6bf36bef9dd745d3c376dab7e4435fb1a0bf4cce12138a26e.png

Notice the syntax:

data=df_tips — the DataFrame to use
x=df_tips['total_bill'] — the column for the x-axis
y=df_tips['tip'] — the column for the y-axis

The plot shows a positive relationship: higher bills tend to have higher tips. This makes intuitive sense!

Adding Colour (Hue)#

We can add a third variable using colour. Let’s see if the pattern differs by gender:

sns.scatterplot(data=df_tips, x=df_tips['total_bill'], y=df_tips['tip'], hue=df_tips['sex'])
plt.show()

../_images/7d7e231d6214ce4b345c38ea33361044aa0e8764961ef62cbad608c28a972409.png

The hue parameter colours the points by the ‘sex’ column. Seaborn automatically:

Assigns different colours to each category
Adds a legend

Now we can compare the tipping patterns of male and female customers at a glance.

Tip

The hue parameter works with most seaborn plots. It’s a powerful way to add a categorical dimension to your visualisation.

Histograms#

A histogram shows the distribution of a single numerical variable. It divides the data into bins and counts how many values fall into each bin.

Basic Histogram#

Let’s see how total bills are distributed:

sns.histplot(data=df_tips, x=df_tips['total_bill'])
plt.show()

../_images/b4fc6516c549a1db8dfb6c8f3bbaf933153e7c06a2732c04d94ca22176af0bc8.png

Most bills are between \(10 and \)25, with fewer very low or very high bills.

Adjusting the Number of Bins#

The bins parameter controls how many bars the histogram has:

sns.histplot(data=df_tips, x=df_tips['total_bill'], bins=40)
plt.show()

../_images/9177733063f7fdbef5791d668f0b29ea8469376fd482fb254ee386b9734efcf3.png

More bins show more detail, but can look noisy. Fewer bins show the overall shape but hide detail. Experiment to find the right balance.

Different Statistics#

By default, histograms show counts. You can change this with the stat parameter:

# Show percentages instead of counts
sns.histplot(data=df_tips, x=df_tips['total_bill'], bins=40, stat='percent')
plt.show()

../_images/5cf912bc060ec11ecfdd0572d5899f59b20969c6f1bf5059f40d41732f130482.png

# Show frequency (same as count)
sns.histplot(data=df_tips, x=df_tips['total_bill'], bins=30, stat='frequency')
plt.show()

../_images/655c3f7e5894912ee6770cc5051a8f1d26567aead5bbb560f4b1d569754d9cb1.png

# Show density (area under curve = 1)
sns.histplot(data=df_tips, x=df_tips['total_bill'], bins=30, stat='density')
plt.show()

../_images/414f181d3aeadabcee66d1c35b6cd63c68e9a433cf0ee4ad2eec847397fdbd1c.png

Stat	Description
`'count'`	Number of observations in each bin (default)
`'frequency'`	Same as count
`'percent'`	Percentage of total observations
`'density'`	Normalised so the area equals 1

Adding a Density Curve (KDE)#

You can overlay a smooth density curve using kde=True:

sns.histplot(data=df_tips, x=df_tips['total_bill'], kde=True)
plt.show()

../_images/60f4dce294e35cff537d8f9eaadd180fa7aa1f8c8ab012bbdd3fa3a5a9e27bfe.png

The KDE (Kernel Density Estimate) curve shows a smoothed version of the distribution.

Count Plots#

So far we’ve been visualising numerical variables (like total bill amounts). But what if you have a categorical variable — like the day of the week or a customer’s gender — and you simply want to know: how many observations are in each category?

That’s where countplot comes in.

Basic Count Plot#

Let’s see how many observations we have for each day:

sns.countplot(data=df_tips, x=df_tips['day'])
plt.show()

../_images/137285c8d88eb6b89cb1107404991803799ba6d278d31b4822349f60fd9ebb4e.png

Notice that Saturday has the most observations in our dataset, while Friday has the fewest. The function automatically counts the rows for each category — you don’t need to calculate anything yourself.

Adding Colour with Hue#

What if we want to break each day down further — say, by gender? We can add the hue parameter:

sns.countplot(data=df_tips, x=df_tips['day'], hue=df_tips['sex'])
plt.title('Number of Visits by Day and Gender')
plt.show()

../_images/bd5d705b6d38bafa6eeb0ccda0501de2aa679b1362d79e2637e0b7bbe3396667.png

Now we can see how many male and female customers visited on each day. Notice how seaborn automatically creates a legend and uses distinct colours for each group.

Count Plot vs Bar Plot — What’s the Difference?#

This is a common point of confusion. Both look like bar charts, so when do you use which?

Plot	What it shows	When to use it
`countplot`	How many observations in each category	“How many smokers vs nonsmokers?”
`barplot`	The mean of a numerical variable per category	“What’s the average tip for each day?”

Tip

Think of it this way: countplot counts rows, barplot summarises a column. If you’re counting, use countplot. If you’re averaging (or summing), use barplot.

Bar Plots#

A bar plot shows the relationship between a categorical variable and a numerical variable. By default, it shows the mean of the numerical variable for each category.

Basic Bar Plot#

Let’s compare average total bills for lunch vs dinner:

sns.barplot(data=df_tips, x=df_tips['time'], y=df_tips['total_bill'])
plt.show()

../_images/ff9355fbab8dfe6a04e83cf8f31edb651773f6e1db407cdebd2be8710dea8eb2.png

Dinner bills are higher on average than lunch bills.

Grouped Bar Plot#

Add hue to compare across another category:

sns.barplot(data=df_tips, x=df_tips['time'], y=df_tips['total_bill'], hue=df_tips['sex'], errorbar=None)
plt.show()

../_images/633ddcd3f414e7ae3501ce1a16d6895d15ec3954af1c47ee44252cce1cfe3775.png

Now we can see that male customers have slightly higher bills on average, for both lunch and dinner.

Note

The errorbar=None removes the error bars. By default, seaborn shows 95% confidence intervals, which can be useful but also cluttered.

Box Plots#

A box plot (or box-and-whisker plot) shows the distribution of a numerical variable. It displays:

The median (middle line)
The interquartile range or IQR (the box — middle 50% of data)
Whiskers extending to 1.5 × IQR
Outliers as individual points beyond the whiskers

Basic Box Plot#

sns.boxplot(data=df_tips, x=df_tips['total_bill'])
plt.show()

../_images/489e5d4049ce086a7dbf5ee3db531f298cf42fb06455f8f33d6655a584d1a8d1.png

This shows the distribution of total bills. The box shows that 50% of bills are roughly between \(13 and \)24, with a few high outliers.

Comparing Groups#

Box plots are excellent for comparing distributions across categories:

sns.boxplot(data=df_tips, x=df_tips['total_bill'], y=df_tips['sex'])
plt.show()

../_images/16202d0c0657700c35b54f40de6287a6bdc201532d831d3bea8725b3c10e02d6.png

This shows the distribution of total bills separately for males and females. We can see that:

Male customers have a slightly higher median bill
Both groups have outliers at the high end

Adding a Title and Labels#

You can customise the plot using the .set() method:

sns.boxplot(data=df_tips, x=df_tips['total_bill'], y=df_tips['sex']).set(
    title='Distribution of Bills by Gender',
    xlabel='Total Bill ($)',
    ylabel='Gender'
)
plt.show()

../_images/e3946638155035f8756427d18d5dc2eb5ca5b6490d71fd35d3f387588c55d7d0.png

Line Plots#

A line plot shows how a numerical variable changes over time or another ordered variable. It’s particularly useful for time series data.

Loading Time Series Data#

Let’s use the flights dataset, which shows monthly airline passenger numbers:

df_flights = sns.load_dataset('flights')
df_flights.head()

	year	month	passengers
0	1949	Jan	112
1	1949	Feb	118
2	1949	Mar	132
3	1949	Apr	129
4	1949	May	121

Basic Line Plot#

sns.lineplot(data=df_flights, x='year', y='passengers', errorbar=None)
plt.show()

../_images/f6a16e59ade55ce43011ad6c496f3197200d232ecdc4c382cbe45f1a791a0509.png

This shows the average number of passengers per year. The clear upward trend shows that air travel grew significantly from 1949 to 1960.

Multiple Lines#

Use hue to show separate lines for each category:

sns.lineplot(data=df_flights, x='year', y='passengers', hue='month')
plt.show()

../_images/e5c875955a87b091f07a4dc0b6a5e97562bb4ce466e70f42c5ee0341485b525e.png

Now we can see the trend for each month. Notice that:

All months show an upward trend
Summer months (especially July and August) consistently have more passengers
The seasonal pattern is clear

Faceted Plots with displot#

Sometimes you want to create separate plots for different categories. The displot function (distribution plot) makes this easy:

sns.displot(data=df_tips, x='total_bill', col='time', kde=True)
plt.show()

../_images/7cf82cf7cf93fe7ac457a56b538feabb8203617da228b3c396a4f8084f5baab9.png

This creates two histograms side by side — one for Lunch and one for Dinner. The col='time' parameter creates a separate column for each value of ‘time’.

Choosing the Right Chart#

Here’s a quick guide to choosing the right visualisation:

Data Type	Question	Chart Type
1 numerical	What’s the distribution?	Histogram, Box plot
2 numerical	What’s the relationship?	Scatter plot
1 categorical + 1 numerical	How do groups compare?	Bar plot, Box plot
Time series	How does it change over time?	Line plot
1 categorical	What are the frequencies?	Count plot (bar chart)

Saving Your Plots#

To save a plot as an image file, use matplotlib’s savefig:

sns.histplot(data=df_tips, x=df_tips['total_bill'], bins=30)
plt.savefig('my_histogram.png')
plt.show()

You can save in various formats: .png, .jpg, .pdf, .svg.

Tip

Call plt.savefig() before plt.show(). Once show() is called, the figure is cleared.

Common Customisations#

Changing Figure Size#

plt.figure(figsize=(10, 6))  # Width, Height in inches
sns.histplot(data=df_tips, x=df_tips['total_bill'])
plt.show()

../_images/29a2ea6c52d2f62321876c1a546e2c9747031ef736cd542177e1317c870c9921.png

Adding Titles and Labels#

sns.scatterplot(data=df_tips, x=df_tips['total_bill'], y=df_tips['tip'])
plt.title('Relationship Between Bill and Tip')
plt.xlabel('Total Bill ($)')
plt.ylabel('Tip ($)')
plt.show()

../_images/9562b744506e36d969bfffc7ea7ce66518d5963171ee4a0bdcce6be580ddd73d.png

Changing Colours#

Seaborn has many built-in colour palettes:

sns.scatterplot(data=df_tips, x=df_tips['total_bill'], y=df_tips['tip'], hue=df_tips['day'], palette='Set2')
plt.show()

../_images/dcb52813a4d27780f4c1cb5431d10c9fe1dfcfce3eb13c2a3a1f2d8303af9641.png

Popular palettes include: 'Set1', 'Set2', 'deep', 'muted', 'pastel', 'bright'.

Exercises#

Exercise 16

Exercise 1: Create a Scatter Plot

Using the tips dataset:

Create a scatter plot with total_bill on the x-axis and tip on the y-axis
Colour the points by the day column
Add a title: “Tips by Total Bill and Day”

Solution to Exercise 16

import seaborn as sns
import matplotlib.pyplot as plt

df_tips = sns.load_dataset('tips')

sns.scatterplot(data=df_tips, x=df_tips['total_bill'], y=df_tips['tip'], hue=df_tips['day'])
plt.title('Tips by Total Bill and Day')
plt.show()

Exercise 17

Exercise 2: Create Histograms

Using the tips dataset:

Create a histogram of the tip column
Use 20 bins
Add a KDE curve
Change the stat to show percentages

Solution to Exercise 17

import seaborn as sns
import matplotlib.pyplot as plt

df_tips = sns.load_dataset('tips')

sns.histplot(data=df_tips, x=df_tips['tip'], bins=20, kde=True, stat='percent')
plt.title('Distribution of Tips')
plt.xlabel('Tip ($)')
plt.show()

Exercise 18

Exercise 3: Compare Distributions

Using the tips dataset:

Create a box plot comparing total_bill across different days (day column)
Add appropriate title and labels
What day has the highest median bill?

Solution to Exercise 18

import seaborn as sns
import matplotlib.pyplot as plt

df_tips = sns.load_dataset('tips')

sns.boxplot(data=df_tips, x=df_tips['day'], y=df_tips['total_bill'])
plt.title('Total Bill Distribution by Day')
plt.xlabel('Day of Week')
plt.ylabel('Total Bill ($)')
plt.show()

# Answer: Sunday has the highest median bill

Exercise 19

Exercise 4: Complete Visualisation

Load the ‘penguins’ dataset from seaborn:

penguins = sns.load_dataset('penguins')

Create the following visualisations:

A scatter plot of flipper_length_mm vs body_mass_g, coloured by species
A histogram of bill_length_mm with separate colours for each species
A box plot comparing body_mass_g across species

What patterns do you notice?

Solution to Exercise 19

import seaborn as sns
import matplotlib.pyplot as plt

penguins = sns.load_dataset('penguins')

# 1. Scatter plot
plt.figure(figsize=(10, 6))
sns.scatterplot(data=penguins, x=penguins['flipper_length_mm'], y=penguins['body_mass_g'], hue=penguins['species'])
plt.title('Penguin Size by Species')
plt.xlabel('Flipper Length (mm)')
plt.ylabel('Body Mass (g)')
plt.show()

# 2. Histogram
plt.figure(figsize=(10, 6))
sns.histplot(data=penguins, x=penguins['bill_length_mm'], hue=penguins['species'], bins=20)
plt.title('Bill Length Distribution by Species')
plt.xlabel('Bill Length (mm)')
plt.show()

# 3. Box plot
plt.figure(figsize=(10, 6))
sns.boxplot(data=penguins, x=penguins['species'], y=penguins['body_mass_g'])
plt.title('Body Mass by Species')
plt.xlabel('Species')
plt.ylabel('Body Mass (g)')
plt.show()

# Patterns:
# - Gentoo penguins are the largest (highest body mass)
# - Adelie penguins have shorter bills
# - There's a clear relationship between flipper length and body mass
# - The three species form distinct clusters in the scatter plot

Data Visualisation with Seaborn

Contents

Data Visualisation with Seaborn#

Why Visualise Data?#

Python Visualisation Libraries#

Setting Up#

Loading Example Data#

Scatter Plots#

Basic Scatter Plot#

Adding Colour (Hue)#

Histograms#

Basic Histogram#

Adjusting the Number of Bins#

Different Statistics#

Adding a Density Curve (KDE)#

Count Plots#

Basic Count Plot#

Adding Colour with Hue#

Count Plot vs Bar Plot — What’s the Difference?#

Bar Plots#

Basic Bar Plot#

Grouped Bar Plot#

Box Plots#

Basic Box Plot#

Comparing Groups#

Adding a Title and Labels#

Line Plots#

Loading Time Series Data#

Basic Line Plot#

Multiple Lines#

Faceted Plots with displot#

Choosing the Right Chart#

Saving Your Plots#

Common Customisations#

Changing Figure Size#

Adding Titles and Labels#

Changing Colours#

Exercises#