Visualize›Extra· 30 min read

Statistical Charts with Seaborn

Seaborn sits on top of Matplotlib and makes beautiful statistical charts — straight from a DataFrame — in one line.

What you will learn

Make a chart directly from a DataFrame
Use a histogram and a scatter plot
See why Seaborn is great for exploring

Prettier charts, less code

Seaborn is built on top of Matplotlib. It produces good-looking statistical charts with much less code, and — best of all — it works directly from a DataFrame by naming columns. It is the fastest way to explore data visually. By convention we import it as sns.

We will use this little table:

A small house dataset for Seaborn

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

df = pd.DataFrame({
    'size':  [50, 60, 80, 100, 120],   # house size
    'price': [40, 55, 70, 95, 110]     # house price
})

Note: Output: (No output — just the table. Five houses, with a size and a price. As size goes up, price seems to go up too. Let us see it.)

A histogram — the shape of one column

A histogram shows how the values of one column are spread out — where most of them sit. Seaborn draws it from the column name.

A histogram of the price column

sns.histplot(data=df, x='price')
plt.title('Distribution of Prices')
plt.show()

Note: Output: (A chart with bars showing how many houses fall in each price band. The prices spread fairly evenly from 40 to 110.) A histogram answers “what do typical values look like, and are there outliers?” at a glance.

A scatter plot — two columns together

A scatter plot shows the relationship between two number columns — one dot per row. It is the best way to see if two things move together.

A scatter plot of price against size

sns.scatterplot(data=df, x='size', y='price')
plt.title('Price vs Size')
plt.show()

Note: Output: (Five dots climbing from lower-left to upper-right.) The dots rise together: bigger houses cost more. That upward pattern is a positive relationship — the idea behind correlation, coming up in the Stats unit.

Seaborn function	Shows	Good for
`sns.histplot`	Spread of one column	Seeing typical values
`sns.scatterplot`	Two columns vs each other	Spotting relationships
`sns.barplot`	Average per category	Comparing groups
`sns.boxplot`	Spread per category	Comparing distributions

Tip: Seaborn and Matplotlib work together: draw with Seaborn for looks, then use plt functions like plt.title() to fine-tune. You are not choosing one — you use both.

Q. Which Seaborn chart best shows whether two number columns move together?

Answer: A scatter plot puts one dot per row using two columns as x and y, so an upward or downward pattern reveals their relationship.

✍️ Practice

Use sns.histplot to show the spread of one numeric column in a DataFrame.
Use sns.scatterplot to plot two numeric columns and describe the pattern you see.

🏠 Homework

On a real dataset, make a histogram of one column and a scatter plot of two columns. Write one sentence about what each chart tells you.