End-to-End Exploratory Data Analysis (EDA) Project0%

End-to-End Exploratory Data Analysis (EDA) Project

Beginner12 min read•Updated: Jul 11, 2026
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End-to-End Exploratory Data Analysis (EDA) Project

Reading Time: 8 Mins | Level: Beginner to Advanced

EDA Pipeline WorkflowClick to Zoom
EDA Pipeline Workflow

1. What is Exploratory Data Analysis (EDA)?

Exploratory Data Analysis (EDA) is the scientific process of investigating a new, raw dataset to discover patterns, spot anomalies, test hypotheses, and verify assumptions using summary statistics and graphical representations.

In this capstone lesson, we walk through a complete, production-ready EDA project on an Indian E-Commerce Transactions dataset.

2. Step-by-Step EDA Implementation

Python
import pandas as pd
import numpy as np
 
# STEP 1: INGESTION & FIRST LOOK
df = pd.read_csv('ecommerce_orders.csv', parse_dates=['Order_Date'])
 
print("--- Dataset Shape ---")
print(f"Total Transactions: {df.shape[0]:,}, Total Columns: {df.shape[1]}")
 
print("\n--- Column Data Types & Non-Null Counts ---")
print(df.info())
 
print("\n--- Statistical 5-Point Summary ---")
print(df.describe().T)
 
# STEP 2: DATA CLEANING & AUDITING
# Check missing percentages
null_pct = (df.isna().sum() / len(df)) * 100
print("\n--- Missing Data % ---\n", null_pct[null_pct > 0])
 
# Impute missing Discount with 0 and drop rows missing Customer_ID
df['Discount_Pct'] = df['Discount_Pct'].fillna(0)
df = df.dropna(subset=['Customer_ID'])
 
# Remove duplicate orders
df = df.drop_duplicates(subset=['Order_ID'], keep='first')
 
# STEP 3: FEATURE ENGINEERING
# Net Revenue = (Quantity * Unit_Price) * (1 - Discount_Pct / 100)
df['Gross_Amount'] = df['Quantity'] * df['Unit_Price']
df['Net_Revenue'] = df['Gross_Amount'] * (1 - df['Discount_Pct'] / 100)
df['Order_Month'] = df['Order_Date'].dt.to_period('M')
 
# STEP 4: BUSINESS INSIGHTS & AGGREGATIONS
# Top 5 Product Categories by Revenue
top_categories = df.groupby('Category', as_index=False).agg(
Total_Revenue=pd.NamedAgg('Net_Revenue', 'sum'),
Total_Units=pd.NamedAgg('Quantity', 'sum'),
Avg_Order_Value=pd.NamedAgg('Net_Revenue', 'mean'),
Order_Count=pd.NamedAgg('Order_ID', 'count')
).sort_values(by='Total_Revenue', ascending=False)
 
print("\n--- Top Categories by Revenue ---\n", top_categories)
 
# Monthly Revenue Trend
monthly_trend = df.groupby('Order_Month')['Net_Revenue'].sum()
print("\n--- Monthly Revenue Trend ---\n", monthly_trend)

3. The 5 Core Questions Every EDA Must Answer

  1. 1
    Data Quality: Are there missing values, corrupted strings, or duplicate records?
  2. 2
    Distributions: Are numeric features normally distributed or skewed with extreme outliers?
  3. 3
    Key Drivers: Which categories, regions, or customer segments contribute 80% of revenue (Pareto Principle)?
  4. 4
    Time Trends: Is revenue growing month-over-month, and are there seasonal spikes?
  5. 5
    Actionable Recommendations: What strategic business action should executive leadership take based on the findings?
Interactive Quiz

Multiple Choice Questions

Question 1
What is the primary purpose of Exploratory Data Analysis (EDA)?
Question 2
Which method produces a 5-point statistical summary (count, mean, std, min, 25%, 50%, 75%, max) for numeric columns?
Question 3
In the Pareto Principle (80/20 rule) applied to sales analytics, what common pattern is frequently observed during EDA?
Question 4
Why should you always inspect df.info() at the start of any EDA project?
Question 5
What is 'Feature Engineering' in a data analysis workflow?

Related Lessons

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Practice Quiz

Test your understanding of this lesson with 5 questions. Each question has one correct answer.

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