Exploratory Data Analysis (EDA): Step-by-Step Tutorial with Python

Before building machine learning models, it’s essential to understand your data. This process is called Exploratory Data Analysis (EDA). EDA helps uncover patterns, detect anomalies, test assumptions, and summarize key insights using visual and statistical techniques.

In this tutorial, we’ll walk step-by-step through the EDA process with Python, using popular libraries like Pandas, Matplotlib, and Seaborn.

Step 1: Import Libraries and Dataset

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

# Load dataset (example: Titanic dataset)
df = pd.read_csv("titanic.csv")
df.head()

Step 2: Understand the Data Structure

Check the size, data types, and basic summary.

print(df.shape)       # rows and columns
print(df.info())      # column types and missing values
print(df.describe())  # statistical summary

This helps you know whether you’re working with numerical, categorical, or mixed data.

Step 3: Handle Missing Values

# Check missing values
print(df.isnull().sum())

# Example: Fill missing age values with mean
df['Age'].fillna(df['Age'].mean(), inplace=True)

Step 4: Univariate Analysis

Explore single variables with histograms, boxplots, or value counts.

sns.histplot(df['Age'], kde=True)
plt.show()

print(df['Sex'].value_counts())

Step 5: Bivariate Analysis

Explore relationships between two variables.

sns.boxplot(x="Sex", y="Age", data=df)
plt.show()

sns.barplot(x="Sex", y="Survived", data=df)
plt.show()

Step 6: Correlation Analysis

Check relationships between numerical variables.

plt.figure(figsize=(8,6))
sns.heatmap(df.corr(), annot=True, cmap="coolwarm")
plt.show()

Step 7: Outlier Detection

Identify extreme values that may affect analysis.

sns.boxplot(df['Fare'])
plt.show()

Step 8: Summarize Insights

At this stage, you should be able to answer:

• Which variables strongly influence the target variable?
• Are there missing values or outliers that need attention?
• What features may need transformation or encoding?

EDA is the foundation of data science projects. By following these steps—loading data, handling missing values, analyzing distributions, exploring relationships, and detecting outliers—you gain a deep understanding of your dataset and set the stage for effective modeling.

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