Introduction to EDA

What is EDA?

EDA stands for Exploratory Data Analysis. It means looking at your data before doing anything else.

Think of it like checking ingredients before cooking. You need to know what you have!

Why Do EDA?

EDA helps you:

• Understand your data
• Find problems (missing values, errors)
• Discover patterns
• Decide what analysis to do next

Basic EDA Steps

import pandas as pd

# Load data
df = pd.read_csv('your_data.csv')

# Step 1: How big is the data?
print(df.shape)  # (rows, columns)

# Step 2: What columns exist?
print(df.columns)

# Step 3: What types of data?
print(df.dtypes)

# Step 4: Quick look at data
print(df.head())

# Step 5: Basic statistics
print(df.describe())

# Step 6: Missing values
print(df.isna().sum())

The 5 Questions of EDA

1. How much data? - rows and columns
2. What kind of data? - numbers, text, dates
3. Any missing data? - empty cells
4. Any weird values? - outliers, errors
5. Any patterns? - relationships between columns

Quick Info Summary

# Get everything at once
print(df.info())

This shows:

• Number of rows
• Column names
• Data types
• Missing values

Sample Data for Practice

import pandas as pd

# Create sample data
df = pd.DataFrame({
    'Name': ['John', 'Sarah', 'Mike', 'Emma', 'Tom'],
    'Age': [25, 30, 28, 35, 22],
    'City': ['NYC', 'LA', 'NYC', 'Chicago', 'LA'],
    'Salary': [50000, 60000, 55000, 70000, 45000]
})

# Start exploring!
print("Shape:", df.shape)
print("\nFirst rows:")
print(df.head())
print("\nStatistics:")
print(df.describe())

Key Points

• EDA = looking at data before analysis
• Always start with shape, types, and missing values
• describe() gives quick statistics
• info() gives overview
• Take time to understand your data first

What's Next?

Learn about descriptive statistics - the numbers that summarize your data.