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5. Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is the most critical step in any data science project. It helps you understand the data, discover patterns, detect anomalies, find relationships, and form hypotheses — all before building any model.

Why EDA is important in 2026:

• Prevents garbage-in-garbage-out (bad data → bad model)

• Saves time & money by identifying issues early

• Guides feature engineering and model selection

• Creates compelling stories for stakeholders/reports

Core tools for EDA:

• Pandas (data manipulation)

• NumPy (numerical operations)

• Matplotlib + Seaborn (visualization)

• Missingno, Sweetviz, Pandas Profiling (automated EDA reports)

5.1 Understanding Data Distribution & Summary Statistics

First step: Load & inspect data

Python

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # Load example dataset (or use your own CSV) df = sns.load_dataset("titanic") # Quick overview print(df.head()) print(df.info()) print(df.shape) # (rows, columns) print(df.describe()) # numerical summary print(df.describe(include='object')) # categorical summary

Key summary statistics

• Mean / Median / Mode → central tendency

• Standard deviation / IQR → spread

• Min / Max / Percentiles → range & outliers

• Skewness & Kurtosis → shape of distribution

Visualize distribution (histogram + KDE)

Python

plt.figure(figsize=(10, 6)) sns.histplot(df['age'].dropna(), kde=True, bins=30, color='teal') plt.title("Age Distribution of Titanic Passengers") plt.xlabel("Age") plt.ylabel("Count") plt.axvline(df['age'].mean(), color='red', linestyle='--', label=f'Mean = {df["age"].mean():.1f}') plt.axvline(df['age'].median(), color='green', linestyle='--', label=f'Median = {df["age"].median():.1f}') plt.legend() plt.show()

Check skewness

Python

print("Skewness of Age:", df['age'].skew()) # positive → right-skewed

5.2 Univariate, Bivariate & Multivariate Analysis

Univariate Analysis – Study one variable at a time

Python

# Categorical sns.countplot(x='class', data=df, palette='Set2') plt.title("Passenger Class Distribution") plt.show() # Numerical sns.boxplot(x='fare', data=df, color='lightblue') plt.title("Fare Distribution (with outliers)") plt.show()

Bivariate Analysis – Relationship between two variables

Python

# Numerical vs Numerical sns.scatterplot(x='age', y='fare', hue='survived', data=df, palette='coolwarm') plt.title("Age vs Fare by Survival") plt.show() # Categorical vs Numerical sns.boxplot(x='class', y='fare', hue='sex', data=df) plt.title("Fare by Passenger Class & Gender") plt.show() # Categorical vs Categorical pd.crosstab(df['class'], df['survived'], normalize='index').plot(kind='bar', stacked=True) plt.title("Survival Rate by Passenger Class") plt.show()

Multivariate Analysis – More than two variables

Python

# Pair plot (best for quick multivariate look) sns.pairplot(df[['age', 'fare', 'survived']], hue='survived', diag_kind='kde') plt.suptitle("Multivariate Relationships – Titanic Dataset", y=1.02) plt.show()

5.3 Correlation Analysis & Feature Relationships

Correlation Matrix (Pearson)

Python

# Select only numeric columns numeric_df = df.select_dtypes(include=['number']) corr = numeric_df.corr() plt.figure(figsize=(10, 8)) sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5, vmin=-1, vmax=1) plt.title("Correlation Matrix – Titanic Features") plt.show()

Interpretation tips:

• Values near +1 → strong positive correlation

• Values near -1 → strong negative correlation

• Values near 0 → no linear relationship

• Correlation ≠ causation!

Advanced: Spearman / Kendall correlation (good for non-linear or ordinal data)

Python

corr_spearman = numeric_df.corr(method='spearman') sns.heatmap(corr_spearman, annot=True, cmap='viridis') plt.title("Spearman Correlation") plt.show()

5.4 Real-World EDA Case Study

Dataset: Titanic (classic but very educational)

Complete EDA workflow (copy-paste ready)

Python

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt df = sns.load_dataset("titanic") # 1. Overview print("Shape:", df.shape) print("\nMissing Values:\n", df.isnull().sum()) print("\nData Types:\n", df.dtypes) # 2. Univariate plt.figure(figsize=(12, 5)) plt.subplot(1, 2, 1) sns.histplot(df['age'].dropna(), kde=True, color='teal') plt.title("Age Distribution") plt.subplot(1, 2, 2) sns.countplot(x='class', data=df, palette='Set2') plt.title("Passenger Class Distribution") plt.tight_layout() plt.show() # 3. Bivariate plt.figure(figsize=(10, 6)) sns.boxplot(x='class', y='fare', hue='survived', data=df) plt.title("Fare by Class & Survival") plt.show() # 4. Correlation numeric = df.select_dtypes(include=['number']) corr = numeric.corr() sns.heatmap(corr, annot=True, cmap='coolwarm', fmt='.2f') plt.title("Correlation Heatmap") plt.show() # 5. Survival Rate by Gender & Class pd.crosstab([df['sex'], df['class']], df['survived'], normalize='index').plot(kind='bar', stacked=True) plt.title("Survival Rate by Gender & Class") plt.show() print("Key Insights:") print("- Females had much higher survival rate than males") print("- Higher class (1st) had better survival and higher fares") print("- Age had missing values – needs imputation") print("- Fare is highly skewed – consider log transformation")

Key Insights from Titanic EDA (typical findings):

• Women & children had higher survival rates

• 1st class passengers survived more

• Fare is a strong indicator of class & survival

• Age has missing values (esp. in cabin) → imputation needed

• Many categorical variables → encoding required

This completes the full Exploratory Data Analysis (EDA) section — now you know how to deeply understand any dataset before modeling!tes the full Classes and Objects – Basic Building Blocks section — the heart of OOP in Python!

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