Visualizing Data with Seaborn - Mumbai West House Pricing

Easing Visuals with Seaborn

Learning Outcome

6

Basics of branding and messaging

5

Target audience and segmentation

4

Different marketing channels

3

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

2

Install and configure Seaborn correctly

1

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

Hook/Story/Analogy(Slide 4)

Transition from Analogy to Technical Concept(Slide 5)

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:

  1. sns → Seaborn plotting interface
     

  2. plt → rendering and fine control
     

  3. 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

  1. Automatic aggregation and confidence intervals
     
  2. Native handling of categorical variables
     
  3. Cleaner, more readable syntax
     
  4. Consistent visual styling

Summary

4

Customization enhances clarity and interpretation

3

Each plot type serves a specific analytical purpose

2

Works directly with pandas DataFrames

1

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

Easing Visuals with Seaborn

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Easing Visuals with Seaborn

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