The Complete Guide to Exploratory Data Analysis: From Theory to Practice

Machine learning Mathematics in Machine Learning

Posted by tintin_2003 on 2025-10-13 20:36:38 | Last Updated by tintin_2003 on 2025-10-16 01:36:29

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The Complete Guide to Exploratory Data Analysis: From Theory to Practice

Introduction: The Detective Work ofData Science

Imagine you're a detective who just arrived at a crime scene. Before jumping to conclusions about "whodunit," you'd carefully examine the evidence, look for patterns, notice anomalies, and form hypotheses. Exploratory Data Analysis (EDA) is exactly this detective work, but for data.

EDA is the critical first step in any data analysis project where you investigate your dataset to discover patterns, spot anomalies, test hypotheses, and check assumptions through summary statistics and visual representations. It's about getting to know your data intimately before you build complex models or draw conclusions.

Why EDA Matters: The Foundation of Good Analysis

Think of EDA like a doctor's initial examination before diagnosis. A doctor doesn't immediately prescribe treatment—they check your vital signs, ask questions, and run tests. Similarly, you can't build reliable models or draw accurate conclusions without first understanding:

What story does your data tell?
Are there missing values or outliers?
How are variables related to each other?
What patterns or trends exist?

Skipping EDA is like trying to bake a cake without checking if you have all the ingredients. You might end up with something, but it probably won't be what you wanted.

The Core Components of EDA

1. Understanding Data Structure

The Analogy: Before reading a book, you check how many chapters it has, the author, and perhaps skim the table of contents.

What We Do:

Check dimensions (rows and columns)
Identify data types (numeric, categorical, datetime)
Examine the first and last few rows
Look at column names and their meanings

2. Summary Statistics

The Analogy: Like checking a restaurant's average rating, busiest hours, and price range before visiting.

Key Metrics:

Central Tendency: Mean (μ), Median, Mode
Dispersion: Standard Deviation (σ), Variance (σ²), Range
Distribution Shape: Skewness, Kurtosis

The Mathematics:

Mean (Average):

μ = (Σ xi) / n = (x₁ + x₂ + ... + xₙ) / n

Variance (spread of data):

σ² = Σ(xi - μ)² / n

Standard Deviation:

σ = √(σ²)

Real-World Example: Analyzing salary data at a company:

Mean salary: $75,000 (gives overall picture)
Median salary: $65,000 (middle value, less affected by CEO's $500K salary)
Standard deviation: $25,000 (shows how spread out salaries are)

3. Data Quality Assessment

The Analogy: Like checking produce at a grocery store—you're looking for bruises, mold, or missing items.

What We Check:

Missing values (NaN, null, empty strings)
Duplicate records
Inconsistent formatting
Impossible values (e.g., age = -5 or 200)

4. Univariate Analysis

The Analogy: Examining each ingredient separately before cooking—tasting the tomatoes, smelling the basil, checking the pasta quality.

Techniques:

Histograms (distribution of single variable)
Box plots (quartiles, outliers)
Bar charts (for categorical data)
Frequency tables

5. Bivariate and Multivariate Analysis

The Analogy: Understanding how ingredients work together—does salt enhance sweetness? How does oil affect texture?

Techniques:

Scatter plots (relationship between two numeric variables)
Correlation matrices (strength of relationships)
Heatmaps (visualizing multiple relationships)
Pair plots (all combinations at once)

The Mathematics of Correlation:

Pearson correlation coefficient measures linear relationship between variables X and Y:

r = Σ[(xi - μx)(yi - μy)] / √[Σ(xi - μx)² × Σ(yi - μy)²]

Where:

r ranges from -1 to +1
r = +1: perfect positive correlation
r = 0: no linear correlation
r = -1: perfect negative correlation

Real-World Example: Analyzing a coffee shop's sales data might reveal:

Temperature vs. Iced Coffee Sales (r = +0.85) — strong positive correlation
Temperature vs. Hot Chocolate Sales (r = -0.72) — strong negative correlation

Practical EDA: A Complete Example

Let me walk you through a comprehensive EDA workflow with code you can actually use.

"""
Complete Exploratory Data Analysis (EDA) Guide
A comprehensive walkthrough of EDA techniques with practical examples
"""

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import stats
import warnings
warnings.filterwarnings('ignore')

# Set style for better-looking plots
sns.set_style("whitegrid")
plt.rcParams['figure.figsize'] = (12, 6)

# ============================================================================
# STEP 1: DATA LOADING AND INITIAL INSPECTION
# ============================================================================

def create_sample_data():
    """
    Create sample dataset: E-commerce customer data
    Real-world scenario: Online retail store analyzing customer behavior
    """
    np.random.seed(42)
    n = 1000
    
    data = {
        'customer_id': range(1, n + 1),
        'age': np.random.normal(35, 12, n).astype(int),
        'income': np.random.lognormal(10.5, 0.5, n),
        'time_on_site': np.random.exponential(15, n),
        'pages_viewed': np.random.poisson(8, n),
        'purchase_amount': np.random.gamma(2, 50, n),
        'device': np.random.choice(['mobile', 'desktop', 'tablet'], n, p=[0.5, 0.4, 0.1]),
        'conversion': np.random.choice([0, 1], n, p=[0.7, 0.3])
    }
    
    df = pd.DataFrame(data)
    
    # Add some realistic relationships
    df['purchase_amount'] = df['purchase_amount'] + df['income'] * 0.001
    df['conversion'] = ((df['time_on_site'] > 10) & 
                       (df['pages_viewed'] > 5)).astype(int)
    
    # Introduce missing values (realistic scenario)
    df.loc[np.random.choice(df.index, 50, replace=False), 'income'] = np.nan
    df.loc[np.random.choice(df.index, 30, replace=False), 'purchase_amount'] = np.nan
    
    return df

# Load data
df = create_sample_data()

print("=" * 80)
print("EXPLORATORY DATA ANALYSIS WALKTHROUGH")
print("=" * 80)
print("\n📊 Dataset: E-commerce Customer Behavior Analysis\n")

# ============================================================================
# STEP 2: BASIC DATA STRUCTURE
# ============================================================================

print("\n" + "="*80)
print("1. DATA STRUCTURE OVERVIEW")
print("="*80)

print(f"\n✓ Dataset Shape: {df.shape[0]} rows × {df.shape[1]} columns")
print(f"✓ Memory Usage: {df.memory_usage(deep=True).sum() / 1024:.2f} KB")

print("\n📋 First 5 Rows:")
print(df.head())

print("\n📋 Data Types:")
print(df.dtypes)

print("\n📊 Basic Info:")
print(df.info())

# ============================================================================
# STEP 3: SUMMARY STATISTICS
# ============================================================================

print("\n" + "="*80)
print("2. SUMMARY STATISTICS")
print("="*80)

print("\n📈 Numerical Variables:")
print(df.describe())

print("\n📊 Categorical Variables:")
print(df.describe(include='object'))

# Custom statistics
print("\n🎯 Custom Statistics for Key Metrics:")
for col in ['age', 'income', 'purchase_amount']:
    if col in df.columns:
        print(f"\n{col.upper()}:")
        print(f"  Mean (μ):     {df[col].mean():.2f}")
        print(f"  Median:       {df[col].median():.2f}")
        print(f"  Std Dev (σ):  {df[col].std():.2f}")
        print(f"  Variance (σ²): {df[col].var():.2f}")
        print(f"  Skewness:     {df[col].skew():.2f}")
        print(f"  Kurtosis:     {df[col].kurtosis():.2f}")

# ============================================================================
# STEP 4: DATA QUALITY ASSESSMENT
# ============================================================================

print("\n" + "="*80)
print("3. DATA QUALITY CHECK")
print("="*80)

print("\n🔍 Missing Values:")
missing = df.isnull().sum()
missing_pct = (missing / len(df)) * 100
missing_df = pd.DataFrame({
    'Missing_Count': missing,
    'Percentage': missing_pct
})
print(missing_df[missing_df['Missing_Count'] > 0])

print("\n🔍 Duplicate Rows:")
print(f"Number of duplicates: {df.duplicated().sum()}")

print("\n🔍 Outlier Detection (using IQR method):")
for col in df.select_dtypes(include=[np.number]).columns:
    Q1 = df[col].quantile(0.25)
    Q3 = df[col].quantile(0.75)
    IQR = Q3 - Q1
    outliers = ((df[col] < (Q1 - 1.5 * IQR)) | (df[col] > (Q3 + 1.5 * IQR))).sum()
    if outliers > 0:
        print(f"  {col}: {outliers} outliers detected")

# ============================================================================
# STEP 5: UNIVARIATE ANALYSIS
# ============================================================================

print("\n" + "="*80)
print("4. UNIVARIATE ANALYSIS")
print("="*80)

# Create visualizations
fig, axes = plt.subplots(2, 3, figsize=(15, 10))
fig.suptitle('Univariate Analysis: Distribution of Variables', fontsize=16, y=1.02)

# Histogram - Age
axes[0, 0].hist(df['age'], bins=30, edgecolor='black', alpha=0.7)
axes[0, 0].set_title('Age Distribution')
axes[0, 0].set_xlabel('Age')
axes[0, 0].set_ylabel('Frequency')
axes[0, 0].axvline(df['age'].mean(), color='r', linestyle='--', label=f'Mean: {df["age"].mean():.1f}')
axes[0, 0].legend()

# Histogram - Income
axes[0, 1].hist(df['income'].dropna(), bins=30, edgecolor='black', alpha=0.7, color='green')
axes[0, 1].set_title('Income Distribution')
axes[0, 1].set_xlabel('Income ($)')
axes[0, 1].set_ylabel('Frequency')

# Box plot - Purchase Amount
axes[0, 2].boxplot(df['purchase_amount'].dropna(), vert=True)
axes[0, 2].set_title('Purchase Amount (Box Plot)')
axes[0, 2].set_ylabel('Amount ($)')

# Bar chart - Device
device_counts = df['device'].value_counts()
axes[1, 0].bar(device_counts.index, device_counts.values, color=['#FF6B6B', '#4ECDC4', '#45B7D1'])
axes[1, 0].set_title('Device Usage Distribution')
axes[1, 0].set_xlabel('Device Type')
axes[1, 0].set_ylabel('Count')

# Histogram - Time on Site
axes[1, 1].hist(df['time_on_site'], bins=30, edgecolor='black', alpha=0.7, color='purple')
axes[1, 1].set_title('Time on Site Distribution')
axes[1, 1].set_xlabel('Time (minutes)')
axes[1, 1].set_ylabel('Frequency')

# Pie chart - Conversion
conversion_counts = df['conversion'].value_counts()
axes[1, 2].pie(conversion_counts.values, labels=['No Purchase', 'Purchase'], 
               autopct='%1.1f%%', colors=['#FFB6C1', '#90EE90'])
axes[1, 2].set_title('Conversion Rate')

plt.tight_layout()
plt.savefig('univariate_analysis.png', dpi=300, bbox_inches='tight')
print("\n✓ Univariate analysis plots saved as 'univariate_analysis.png'")

# ============================================================================
# STEP 6: BIVARIATE ANALYSIS
# ============================================================================

print("\n" + "="*80)
print("5. BIVARIATE ANALYSIS")
print("="*80)

# Correlation matrix
print("\n📊 Correlation Matrix:")
numeric_cols = df.select_dtypes(include=[np.number]).columns
correlation_matrix = df[numeric_cols].corr()
print(correlation_matrix)

# Visualize correlation
fig, axes = plt.subplots(1, 2, figsize=(15, 6))

# Heatmap
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, 
            ax=axes[0], fmt='.2f', square=True)
axes[0].set_title('Correlation Heatmap')

# Scatter plot - Income vs Purchase Amount
axes[1].scatter(df['income'], df['purchase_amount'], alpha=0.5)
axes[1].set_xlabel('Income ($)')
axes[1].set_ylabel('Purchase Amount ($)')
axes[1].set_title('Income vs Purchase Amount')

# Add regression line
mask = df[['income', 'purchase_amount']].notna().all(axis=1)
z = np.polyfit(df.loc[mask, 'income'], df.loc[mask, 'purchase_amount'], 1)
p = np.poly1d(z)
axes[1].plot(df.loc[mask, 'income'], p(df.loc[mask, 'income']), 
             "r--", alpha=0.8, label=f'Trend line')
axes[1].legend()

plt.tight_layout()
plt.savefig('bivariate_analysis.png', dpi=300, bbox_inches='tight')
print("\n✓ Bivariate analysis plots saved as 'bivariate_analysis.png'")

# ============================================================================
# STEP 7: MULTIVARIATE ANALYSIS
# ============================================================================

print("\n" + "="*80)
print("6. MULTIVARIATE ANALYSIS")
print("="*80)

# Group analysis
print("\n📊 Purchase Behavior by Device:")
device_stats = df.groupby('device').agg({
    'purchase_amount': ['mean', 'median', 'std'],
    'time_on_site': 'mean',
    'conversion': 'mean'
}).round(2)
print(device_stats)

# Visualization
fig, axes = plt.subplots(1, 2, figsize=(15, 6))

# Box plot by device
df.boxplot(column='purchase_amount', by='device', ax=axes[0])
axes[0].set_title('Purchase Amount by Device Type')
axes[0].set_xlabel('Device')
axes[0].set_ylabel('Purchase Amount ($)')

# Scatter with color
scatter = axes[1].scatter(df['time_on_site'], df['purchase_amount'], 
                         c=df['age'], cmap='viridis', alpha=0.6)
axes[1].set_xlabel('Time on Site (minutes)')
axes[1].set_ylabel('Purchase Amount ($)')
axes[1].set_title('Purchase Amount vs Time on Site (colored by Age)')
plt.colorbar(scatter, ax=axes[1], label='Age')

plt.tight_layout()
plt.savefig('multivariate_analysis.png', dpi=300, bbox_inches='tight')
print("\n✓ Multivariate analysis plots saved as 'multivariate_analysis.png'")

# ============================================================================
# STEP 8: KEY INSIGHTS SUMMARY
# ============================================================================

print("\n" + "="*80)
print("7. KEY INSIGHTS & RECOMMENDATIONS")
print("="*80)

print("\n🔍 Statistical Tests:")
# T-test: Do mobile and desktop users have different purchase amounts?
mobile_purchases = df[df['device'] == 'mobile']['purchase_amount'].dropna()
desktop_purchases = df[df['device'] == 'desktop']['purchase_amount'].dropna()
t_stat, p_value = stats.ttest_ind(mobile_purchases, desktop_purchases)
print(f"\nT-test (Mobile vs Desktop purchases):")
print(f"  t-statistic: {t_stat:.4f}")
print(f"  p-value: {p_value:.4f}")
print(f"  Result: {'Significant difference' if p_value < 0.05 else 'No significant difference'} (α=0.05)")

print("\n✅ SUMMARY:")
print(f"  • Average customer age: {df['age'].mean():.1f} years")
print(f"  • Conversion rate: {df['conversion'].mean()*100:.1f}%")
print(f"  • Average purchase: ${df['purchase_amount'].mean():.2f}")
print(f"  • Most common device: {df['device'].mode()[0]}")
print(f"  • Average time on site: {df['time_on_site'].mean():.1f} minutes")

print("\n" + "="*80)
print("EDA COMPLETE! 🎉")
print("="*80)

Output

 ================================================================================
EXPLORATORY DATA ANALYSIS WALKTHROUGH
================================================================================

📊 Dataset: E-commerce Customer Behavior Analysis


================================================================================
1. DATA STRUCTURE OVERVIEW
================================================================================

✓ Dataset Shape: 1000 rows × 8 columns
✓ Memory Usage: 108.93 KB

📋 First 5 Rows:
   customer_id  age        income  time_on_site  pages_viewed  \
0            1   40  73106.877027      7.841107             8   
1            2   33  57659.877200      1.024341             5   
2            3   42  37414.558971      6.434550            13   
3            4   53  26279.162694      1.764839            11   
4            5   32  51488.391671     24.772286             9   

   purchase_amount   device  conversion  
0       209.096910  desktop           0  
1       106.050147   mobile           0  
2        76.738036  desktop           0  
3        87.160300  desktop           0  
4       176.088426   mobile           1  

📋 Data Types:
customer_id          int64
age                  int64
income             float64
time_on_site       float64
pages_viewed         int64
purchase_amount    float64
device              object
conversion           int64
dtype: object

📊 Basic Info:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000 entries, 0 to 999
Data columns (total 8 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   customer_id      1000 non-null   int64  
 1   age              1000 non-null   int64  
 2   income           950 non-null    float64
 3   time_on_site     1000 non-null   float64
 4   pages_viewed     1000 non-null   int64  
 5   purchase_amount  970 non-null    float64
 6   device           1000 non-null   object 
 7   conversion       1000 non-null   int64  
dtypes: float64(3), int64(4), object(1)
memory usage: 62.6+ KB
None

================================================================================
2. SUMMARY STATISTICS
================================================================================

📈 Numerical Variables:
       customer_id          age         income  time_on_site  pages_viewed  \
count  1000.000000  1000.000000     950.000000   1000.000000   1000.000000   
mean    500.500000    34.743000   42589.014832     14.816672      7.940000   
std     288.819436    11.748233   22470.513502     14.710187      2.612896   
min       1.000000    -3.000000    8348.237207      0.000175      1.000000   
25%     250.750000    27.000000   26738.636508      4.265412      6.000000   
50%     500.500000    35.000000   37528.896212     10.412286      8.000000   
75%     750.250000    42.000000   52167.175928     20.688405     10.000000   
max    1000.000000    81.000000  179253.051840    102.086268     16.000000   

       purchase_amount   conversion  
count       970.000000  1000.000000  
mean        141.919292     0.434000  
std          71.224968     0.495873  
min          26.164697     0.000000  
25%          90.471529     0.000000  
50%         124.583948     0.000000  
75%         181.834238     1.000000  
max         443.639481     1.000000  

📊 Categorical Variables:
        device
count     1000
unique       3
top     mobile
freq       506

🎯 Custom Statistics for Key Metrics:

AGE:
  Mean (μ):     34.74
  Median:       35.00
  Std Dev (σ):  11.75
  Variance (σ²): 138.02
  Skewness:     0.12
  Kurtosis:     0.06

INCOME:
  Mean (μ):     42589.01
  Median:       37528.90
  Std Dev (σ):  22470.51
  Variance (σ²): 504923977.06
  Skewness:     1.59
  Kurtosis:     4.05

PURCHASE_AMOUNT:
  Mean (μ):     141.92
  Median:       124.58
  Std Dev (σ):  71.22
  Variance (σ²): 5073.00
  Skewness:     1.08
  Kurtosis:     1.23

================================================================================
3. DATA QUALITY CHECK
================================================================================

🔍 Missing Values:
                 Missing_Count  Percentage
income                      50         5.0
purchase_amount             30         3.0

🔍 Duplicate Rows:
Number of duplicates: 0

🔍 Outlier Detection (using IQR method):
  age: 11 outliers detected
  income: 39 outliers detected
  time_on_site: 52 outliers detected
  purchase_amount: 21 outliers detected

================================================================================
4. UNIVARIATE ANALYSIS
================================================================================

✓ Univariate analysis plots saved as 'univariate_analysis.png'

================================================================================
5. BIVARIATE ANALYSIS
================================================================================

📊 Correlation Matrix:
                 customer_id       age    income  time_on_site  pages_viewed  \
customer_id         1.000000  0.034477 -0.029467      0.021665      0.038674   
age                 0.034477  1.000000 -0.032213      0.060674      0.017432   
income             -0.029467 -0.032213  1.000000      0.009634      0.021277   
time_on_site        0.021665  0.060674  0.009634      1.000000      0.051226   
pages_viewed        0.038674  0.017432  0.021277      0.051226      1.000000   
purchase_amount    -0.009915  0.012952  0.338461      0.093551     -0.006759   
conversion          0.014720  0.049579  0.026122      0.597277      0.237212   

                 purchase_amount  conversion  
customer_id            -0.009915    0.014720  
age                     0.012952    0.049579  
income                  0.338461    0.026122  
time_on_site            0.093551    0.597277  
pages_viewed           -0.006759    0.237212  
purchase_amount         1.000000    0.072301  
conversion              0.072301    1.000000  

✓ Bivariate analysis plots saved as 'bivariate_analysis.png'

================================================================================
6. MULTIVARIATE ANALYSIS
================================================================================

📊 Purchase Behavior by Device:
        purchase_amount                time_on_site conversion
                   mean  median    std         mean       mean
device                                                        
desktop          141.58  123.75  70.93        14.62       0.44
mobile           142.05  124.99  71.25        14.74       0.43
tablet           142.82  128.60  73.44        16.27       0.42

✓ Multivariate analysis plots saved as 'multivariate_analysis.png'

================================================================================
7. KEY INSIGHTS & RECOMMENDATIONS
================================================================================

🔍 Statistical Tests:

T-test (Mobile vs Desktop purchases):
  t-statistic: 0.0987
  p-value: 0.9214
  Result: No significant difference (α=0.05)

✅ SUMMARY:
  • Average customer age: 34.7 years
  • Conversion rate: 43.4%
  • Average purchase: $141.92
  • Most common device: mobile
  • Average time on site: 14.8 minutes

================================================================================
EDA COMPLETE! 🎉
================================================================================

Deep Dive: The Mathematics Behind EDA

Understanding Distribution Shapes

Skewness measures asymmetry in data distribution:

Skewness = E[(X - μ)³] / σ³

Positive skew (right-skewed): Long tail on the right (e.g., income—most people earn average, few earn millions)
Negative skew (left-skewed): Long tail on the left (e.g., age at retirement—most retire around 65, few retire very early)
Zero skew: Symmetric distribution (e.g., heights in a population)

Kurtosis measures "tailedness" of distribution:

Kurtosis = E[(X - μ)⁴] / σ⁴

High kurtosis: Heavy tails, more outliers (e.g., stock returns)
Low kurtosis: Light tails, fewer outliers

The Interquartile Range (IQR) Method

A robust way to detect outliers:

IQR = Q₃ - Q₁
Lower Bound = Q₁ - 1.5 × IQR
Upper Bound = Q₃ + 1.5 × IQR

Real-World Example: Detecting fraudulent credit card transactions. If most transactions are $20-$200, but suddenly there's a $10,000 charge, the IQR method flags it as an outlier for investigation.

Chi-Square Test for Categorical Relationships

Testing if two categorical variables are independent:

χ² = Σ [(Observed - Expected)² / Expected]

Real-World Example: Testing if there's a relationship between "season" (spring, summer, fall, winter) and "ice cream flavor preference" (vanilla, chocolate, strawberry).

Real-World EDA Success Stories

Case Study 1: Netflix Recommendation System

When Netflix analyzes viewing patterns through EDA, they discover:

Peak viewing times (8-11 PM)
Binge-watching patterns (users who watch 1 episode watch 3+ on average)
Genre preferences by demographics
Correlation between show completion rate and ratings

This EDA drives their recommendation algorithm worth billions.

Case Study 2: Healthcare Patient Analysis

A hospital conducts EDA on patient data:

Discovers readmission rates spike 7 days after discharge
Finds correlation between medication adherence and age groups
Identifies seasonal patterns in certain illnesses
Detects outliers indicating data entry errors

Result: Better patient care protocols and reduced readmissions by 15%.

Case Study 3: Retail Inventory Optimization

A supermarket chain's EDA reveals:

Bread and milk sales correlate with rainy days
Friday evening has 3x higher checkout times
Certain product combinations are frequently bought together
Seasonal demand patterns differ by location

Result: Optimized staffing, inventory, and product placement.

Common EDA Pitfalls to Avoid

Confirmation Bias: Looking only for patterns that confirm your hypothesis
- Solution: Stay open to unexpected findings
Ignoring Missing Data Patterns: Assuming missing data is random
- Solution: Investigate if missingness has a pattern
Over-relying on Mean: When data is skewed
- Solution: Use median and examine distribution
Correlation ≠ Causation: Ice cream sales correlate with drowning deaths (both peak in summer!)
- Solution: Always question the underlying mechanism
Cherry-Picking Visualizations: Only showing graphs that support your story
- Solution: Present the full picture, including uncomfortable truths

Your EDA Checklist

Before moving to modeling or conclusions:

✅ Understand data dimensions and types
✅ Check for missing values and outliers
✅ Calculate summary statistics (mean, median, std dev)
✅ Visualize distributions of all variables
✅ Examine correlations between variables
✅ Look for patterns across different groups
✅ Test assumptions statistically
✅ Document unusual findings
✅ Consider domain knowledge and context

Conclusion: The Art and Science of EDA

EDA is both an art and a science. The science lies in the statistical methods and mathematical rigor. The art lies in knowing which questions to ask, which visualizations to create, and how to interpret what you find.

Like a detective solving a mystery, a skilled data analyst uses EDA to:

Let the data speak for itself
Challenge assumptions
Discover hidden patterns
Prepare for deeper analysis

Remember: Good EDA doesn't just find answers—it asks better questions.

The code provided above gives you a complete framework to start your EDA journey. Adapt it to your specific dataset, stay curious, and always question what the data is trying to tell you.

"The greatest value of a picture is when it forces us to notice what we never expected to see." — John Tukey, pioneering statistician who coined the term "Exploratory Data Analysis"