Visualizing Data with Seaborn - Mumbai West House Pricing

Easing Visuals with Seaborn

Learning Outcome

Basics of branding and messaging

Target audience and segmentation

Different marketing channels

Understand the structure and meaning of the Mumbai West house pricing dataset

Install and configure Seaborn correctly

Explain what Seaborn is and why it is used

Previously covered topics :

DataFrames and Series

Data preparation and inspection

Cleaning and transforming data before analysis

Analytical validation before visualization

Visualization using Matplotlib

A Data analyst is preparing a report for stakeholders...

You calculate values yourself

Decide every label and color

Adjust spacing manually

Redo work when data changes

By using Matplotlib, It’s like drawing charts on a blank sheet:

The analysis is correct, but most of your time goes into building the chart, not reading the data

Now imagine using a ready-made professional template:

It understands your data tables

Highlights trends automatically

Compares categories clearly

Keeps visuals consistent

You focus on insights, not chart mechanics

What did we use in this case

Seaborn builds on these concepts by reducing plotting effort while adding statistical intelligence

In that scenario Seaborn was used

Matplotlib → manual control, more effort

Seaborn → insight-first visualization

When the goal is to explain patterns clearly,
Seaborn makes visuals easier, faster, and more meaningful

Let's understand it in detail....

What is Seaborn

Definition

Seaborn is a Python visualization library built on Matplotlib that offers high-level, statistically informed plots for pandas DataFrames.

Why Seaborn exist?

Matplotlib requires extensive manual configuration

Statistical plots require additional logic

DataFrame-based workflows need native support

Installing and Setting Up Seaborn

Explanation:

sns → Seaborn plotting interface
plt → rendering and fine control
pd → tabular data handling

Installation

pip install seaborn

Imports

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

Understanding the Mumbai West House Pricing Dataset

This dataset represents Mumbai western-region housing listings for price analysis

Key Columns:

bhk – number of bedrooms, halls, kitchens

type – apartment, villa, independent house, studio apartment

locality – neighborhood information

area – size in square feet

price – numeric price value

price_unit – L (Lakh) or Cr (Crore)

status – ready to move / under construction

age – new, resale, unknown

region – geographic region

Why Use Seaborn Over Matplotlib?

Seaborn is preferred when the goal is to visualize patterns and distributions, not manually assemble plots

Key advantages

Automatic aggregation and confidence intervals
Native handling of categorical variables
Cleaner, more readable syntax
Consistent visual styling

Line Plot in Seaborn

A line plot is used to visualize trends or continuous relationships between variables.

data = pd.DataFrame({
    "Year": [2010, 2011, 2012, 2013, 2014],
    "Value": [100, 150, 200, 250, 300]
})

sns.lineplot(x="Year", y="Value", data=data)
plt.show()

Year is mapped to the x-axis

Value is mapped to the y-axis

Seaborn aggregates automatically if duplicate x-values exist

Code Explanation:

Code:

Bar Plot in Seaborn

A bar plot compares aggregated numerical values across categories

Code:

data = pd.DataFrame({
    "Category": ["Apartment", "Villa", "Studio"],
    "Values": [120, 300, 80]
})

sns.barplot(x="Category", y="Values", data=data)
plt.show()

Seaborn computes the mean for each category

Bars represent aggregated statistics

Code Explanation:

Histogram in Seaborn

A histogram shows the distribution of a single continuous variable

Code:

data = pd.DataFrame({
    "Values": [1, 2, 2, 3, 4, 4, 5, 6, 7, 8, 9]
})

sns.histplot(data["Values"], bins=5, kde=True)
plt.show()

Code Explanation:

Values are grouped into bins

Bar height shows frequency

KDE curve shows distribution shape

Box Plot in Seaborn

A box plot summarizes data using quartiles and highlights potential outliers

Code:

data = pd.DataFrame({
    "Type": ["Apartment", "Apartment", "Villa", "Villa"],
    "Price": [100, 120, 300, 280]
})

sns.boxplot(x="Type", y="Price", data=data)
plt.show()

Code Explanation:

Box shows interquartile range

Line inside box shows median

Points beyond whiskers indicate outliers

Scatter Plot in Seaborn

A scatter plot visualizes the relationship between two continuous variables

Code:

data = pd.DataFrame({
    "Area": [400, 600, 800, 1000, 1200],
    "Price": [50, 90, 150, 220, 300]
})

sns.scatterplot(x="Area", y="Price", data=data)
plt.show()

Code Explanation:

Each point represents one house

Position reflects area–price relationship

Summary

Customization enhances clarity and interpretation

Each plot type serves a specific analytical purpose

Works directly with pandas DataFrames

Seaborn simplifies statistical visualization

Quiz

Which Seaborn plot is best for identifying outliers across house types?

A. Line plot

B. Histogram

C. Box plot

D. Scatter plot

Quiz-Answer

Which Seaborn plot is best for identifying outliers across house types?

A. Line plot

B. Histogram

C. Box plot

D. Scatter plot

Visualizing Data with Seaborn - Mumbai West House Pricing

Easing Visuals with Seaborn

Easing Visuals with Seaborn

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