Step-by-Step Guide to Performing EDA in Python

Before jumping into complex data models or machine learning, it’s important to understand your data first. This process is called Exploratory Data Analysis (EDA) — a crucial step that helps you clean, visualize, and uncover patterns in your dataset.

Python is one of the most popular tools for EDA because of its simplicity and the powerful libraries it offers. In this guide, we’ll walk through the basic steps to perform EDA in Python — perfect for beginners and aspiring data scientists.

What Is Exploratory Data Analysis (EDA)?

Exploratory Data Analysis (EDA) is the process of exploring a dataset to understand its structure, patterns, and relationships. It helps identify missing values, detect outliers, and reveal insights that might influence future analysis or modeling.

In simple words, EDA is like taking a closer look at your data before deciding what to do with it.

Step 1: Import the Necessary Libraries

Python has several libraries that make EDA simple and efficient. You’ll mainly use:

Pandas – for loading and managing data
NumPy – for numerical calculations
Matplotlib and Seaborn – for visualizing data

Step 2: Load Your Dataset

You can load your dataset (for example, a CSV file) using Pandas:

Once loaded, use:

to view the first few rows and get a quick idea of what your data looks like.

Step 3: Understand the Data Structure

Start by checking the shape and basic details of your dataset:

shape shows how many rows and columns you have.
info() displays column names, data types, and missing values.
describe() gives you basic statistics like mean, median, and standard deviation.

Step 4: Handle Missing Values

Missing or null values are common in real-world data. You can check them using:

To handle missing values, you can either remove them or fill them in:

Step 5: Explore Relationships and Patterns

Use simple visualizations to see how variables are related:

You can also create specific plots:

These visuals help identify correlations, outliers, and trends in your data.

Step 6: Check for Outliers

Outliers can affect the accuracy of your analysis. You can visualize them using boxplots:

Once identified, you can decide whether to remove or handle them based on your project’s needs.

Step 7: Summarize Your Findings

After exploring your dataset, write down your observations:

Are there any missing values?
Which features are most important?
What patterns or trends did you find?

This summary helps you prepare for the next step — building predictive models or deeper analysis.

Conclusion

Performing Exploratory Data Analysis (EDA) in Python helps you turn raw data into meaningful insights. By using libraries like Pandas, Matplotlib, and Seaborn, you can clean, visualize, and understand your data step by step.

If you want to learn these techniques hands-on and build a strong foundation in analytics, joining a data science training in Gurgaon can be a great way to master EDA and other essential data science skills.