Python Exploratory Data Analysis A Hands On Tutorial

Leo Migdal

-Nov 18, 2025, 2:22 AM

python exploratory data analysis a hands on tutorial

Whether you're just a beginner in data analysis or you're an expert, facing a blank screen when confronted with raw data can be overwhelming. Regardless of your experience level, large and unstructured datasets often present a first look that can be intimidating. To overcome this, data analysts need to be equipped with solid Exploratory Data Analysis (EDA) techniques. EDA helps to uncover patterns, trends, and relationships between variables, as well as potential issues such as missing values or outliers, all of which are crucial for making data-driven decisions. Python Exploratory Data Analysis is not just about data exploration; it’s about asking the right questions and using the answers to guide deeper analysis. Python provides a powerful toolkit to support this process.

Libraries like NumPy (for scientific computing), Pandas (for data manipulation), and Matplotlib (for data visualization) make it easier to explore and understand your data. In this blog post, we'll walk through how to perform EDA using these data analysis Python libraries. We'll also see how Quadratic, an AI tool for data analysis, simplifies and accelerates data exploration. With Quadratic, you can ask questions about your data and instantly get actionable insights without writing code. We mentioned that Python provides several libraries that help with exploratory data analysis, so the first step is to import these libraries for use in the project. Here:

Data encompasses a collection of discrete objects, events out of context, and facts. Processing such data provides a multitude of information. Processing such information based on our experience, judgment or jurisdiction elicits knowledge as the result of learning. But the million-dollar question is - how do we get meaningful information from such data? The answer to this is Exploratory Data Analysis (EDA) as a process for investigating datasets, elucidating subjects, and visualizing the outcomes. EDA is an approach for data analysis that applies a diversity of techniques to maximize certain insights into a data set; reveal underlying structure; extract significant variables; detect outliers and anomalies; test underlying assumptions;...

This book "Hands-On Exploratory Data Analysis with Python" is built on providing practical knowledge about the main pillars of EDA including data cleaning, data preparation, data exploration, and data visualization. Why visualization? Well, several research studies reveal portraying data in graphical form is clearer and makes complex statistical data analyses and business intelligence more marketable. The readers will get the opportunity to explore open-source datasets including healthcare data, demographics data, Titanic data set, Wine Quality data set, Boston housing pricing dataset, and many others. Using these real-life datasets, the readers get hands-on practice to understand the data, summarize their characteristics and visualize them for business intelligence. The book expects readers to use Pandas, a powerful library for working with data, and other core Python libraries including NumPy and SciPy, StatsModels for regression, and Matplotlib for visualization.

Please note we tested the codes presented in this book with the specific version of pandas, matplotlib, python and other Python libraries. Running the code with a newer or older version might result in warnings and errors. If you encounter any errors, feel free to raise an issue here, and we will try our best to sort it out. Page 13 (Chapter 1): "and real estate industries storehouse..." should be "and real estate industries store house..." Page 14 (Chapter 1): “... there are four observations (001, 002, 003, 004, 005).” should be "...

there are five observations (001, 002, 003, 004, 005)." Exploratory Data Analysis (EDA) is a important step in data analysis which focuses on understanding patterns, trends and relationships through statistical tools and visualizations. Python offers various libraries like pandas, numPy, matplotlib, seaborn and plotly which enables effective exploration and insights generation to help in further modeling and analysis. In this article, we will see how to perform EDA using python. Lets see various steps involved in Exploratory Data Analysis: We need to install Pandas, NumPy, Matplotlib and Seaborn libraries in python to proceed further.

1. df.shape(): This function is used to understand the number of rows (observations) and columns (features) in the dataset. This gives an overview of the dataset's size and structure. 2. df.info(): This function helps us to understand the dataset by showing the number of records in each column, type of data, whether any values are missing and how much memory the dataset uses. In the modern data-driven landscape, exploratory data analysis with Python (EDA) stands as an essential pillar in the field of data science.

EDA serves as the starting point for analyzing and understanding data, helping uncover patterns, anomalies, and relationships that guide deeper statistical and machine learning processes. This article explores the fundamentals of exploratory data analysis, emphasizing its significance in data science. We’ll delve into critical concepts such as data types, measurement scales, and key Python tools like NumPy, Pandas, SciPy, and Matplotlib. Furthermore, we’ll compare EDA with classical and Bayesian analyses to highlight its unique role. Data science involves extracting meaningful insights from data through a blend of mathematics, statistics, and computational techniques. Within this domain, EDA acts as a bridge between raw data and sophisticated analytics.

It enables data scientists to understand the structure, trends, and peculiarities of data before making informed decisions. The importance of exploratory data analysis lies in its ability to illuminate the intricacies of a dataset, ensuring it is ready for deeper analysis. Here are the core contributions of EDA: Beyond the technical tasks, EDA fosters curiosity. It encourages analysts to pose critical questions such as: By emphasizing these questions, EDA aligns data analysis with business objectives and ensures that the resulting insights are actionable.

Hands-On Exploratory Data Analysis with Python is an essential step in data science. It can help you get a feel for the structure, patterns, and potentially interesting relationships in your data before you dive into machine learning. For newcomers, Python would be the best option as it has great libraries for EDA. In this article, we will be performing EDA with Python, with hands-on live examples of each step. So What is Exploratory Data Analysis? To build machine learning models or draw conclusions from data, it’s crucial to understand it well.

EDA helps you: Let’s walk through some practical EDA steps using Python. Step 1: Loading Your Data It’s easy to load your dataset in Python using libraries like pandas and numpy. Most data comes in CSV format and can be loaded with just a few lines of code. Checking Data Shape and Info Once your data is loaded, check its dimensions and basic information.

Python Exploratory Data Analysis A Hands On Tutorial

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Data Encompasses A Collection Of Discrete Objects, Events Out Of

This Book "Hands-On Exploratory Data Analysis With Python" Is Built

Please Note We Tested The Codes Presented In This Book