Day 7 – Exploratory Data Analysis (EDA) with Seaborn & Plotly

Introduction

Exploratory Data Analysis (EDA) is the foundation of modern data science.
It helps data scientists detect patterns, spot anomalies, and uncover insights before building machine learning models.

At Curiosity Tech Nagpur, we focus on hands-on EDA training.
We teach learners how to use Python’s visualization libraries to make confident, data-driven decisions.

This guide explores Seaborn and Plotly—two powerful libraries for data visualization—and provides step-by-step examples, visual storytelling techniques, and practical insights.

Section 1 – What is EDA and Why It Matters

In simple terms, EDA is the process of summarizing and visualizing data to:

1. Identify missing or inconsistent values
2. Detect outliers
3. Discover trends, correlations, and patterns
4. Prepare data for modeling and business insights

Example Story: A retail startup at CuriosityTech used EDA to analyze sales data. By visualizing regional sales trends with Seaborn, they discovered one underperforming region contributing disproportionately to losses. The team implemented actionable insights.
As a result, revenue improved by 18%.

Infographic Description:

Section 2 – Seaborn: Statistical Visualizations Made Easy

Seaborn is built on Matplotlib.
In addition, it simplifies statistical plotting.

Key Plots with Examples

1. Distribution Plot (distplot / histplot)

import seaborn as sns

sns.histplot(df[‘Sales’], kde=True)

• Purpose: Check distribution of numeric variables
• Insight: Detect skewness or unusual spikes

2. Box Plot

sns.boxplot(x=’Region’, y=’Revenue’, data=df)

• Purpose: Identify outliers and distribution per category
• Insight: Highlights regions with unusually high or low revenue

3. Correlation Heatmap

sns.heatmap(df.corr(), annot=True)

• Purpose: Explore relationships between numeric features
• Insight: Determines features that influence target variables

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Section 3 – Plotly: Interactive Visualizations

Unlike static charts, Plotly visualizations allow users to explore data dynamically.

Key Visualizations

1. Interactive Scatter Plot

import plotly.express as px

fig = px.scatter(df, x=’Marketing_Spend’, y=’Revenue’, color=’Region’, size=’Sales’)

fig.show()

• Insight: Understand relationships between variables dynamically

2. Interactive Line Chart

fig = px.line(df, x=’Month’, y=’Revenue’, color=’Region’)

fig.show()

• Insight: Track trends over time for multiple regions

3. Interactive Pie Chart

fig = px.pie(df, names=’Product_Category’, values=’Revenue’)

fig.show()

• Insight: Visualize proportional contributions of product categories

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Section 4 – EDA Workflow: Step-by-Step

Step 1: Import Libraries & Dataset

import pandas as pd

import seaborn as sns

import plotly.express as px

df = pd.read_csv(‘retail_sales.csv’)

Step 2: Summary Statistics

• df.describe()
• df.info()
• Identify missing or erroneous values

Step 3: Univariate Analysis

• Histogram or Box Plot to explore single variables

Step 4: Bivariate/Multivariate Analysis

• Correlation heatmap
• Scatter plots for relationships between features

Step 5: Feature Insights

• Identify patterns like top-selling regions, seasonal trends, and revenue drivers

Step 6: Visual Storytelling

• Use Seaborn for detailed static plots
• Use Plotly for interactive dashboards
• Present findings to stakeholders with actionable recommendations

Infographic Description:

• A step ladder diagram showing each EDA stage
• Each step annotated with Python code snippet examples and expected insights

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Section 5 – Python Libraries Comparison for EDA

Feature	Seaborn	Plotly	Use Case
Interactivity	No	Yes	Dashboards, web reports
Ease of Use	High	Moderate	Quick exploratory plots
Statistical Plot Support	Excellent	Limited	Histograms, boxplots, KDE
Presentation Ready	Good	Excellent	Interactive presentations
Integration	Matplotlib	Jupyter, Web apps	Advanced dashboards

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Section 6 – Real-World Case Study

Step 1: Identify Outliers
Seaborn boxplots reveal that Region C has extreme revenue values.
Step 2: Analyze Correlations
Correlation heatmaps show that marketing spend strongly influences sales.
Step 3: Build Interactive Dashboard

Scenario: An FMCG company wants to identify sales trends and outliers across products and regions.

Impact: Using EDA, the company optimized marketing spend, corrected pricing anomalies, and improved forecast accuracy by 20%.

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Section 7 – Tips for EDA Mastery

1. Always visualize distributions before modeling
2. Detect outliers using boxplots or IQR method
3. Combine static and interactive charts for stakeholders
4. Practice on real datasets from Kaggle, UCI, or company projects
5. Document insights with storytelling: What the data shows → Why it matters → Suggested action

CuriosityTech Tip: Learners in our programs create complete EDA reports using Seaborn and Plotly, building a portfolio that impresses recruiters.

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Conclusion

EDA is the detective work of data science. By mastering Seaborn for statistical plots and Plotly for interactive dashboards, data scientists can unlock insights, detect anomalies, and communicate effectively.

At Curiosity Tech, Nagpur, our hands-on EDA training ensures learners transition from raw data to actionable insights with confidence. Contact us at +91-9860555369 or contact@curiositytech.in, and follow us on Instagram: CuriosityTechPark and LinkedIn: Curiosity Tech for resources and updates.