Exploratory Data Analysis In Python A Step By Step Process

Leo Migdal

-Nov 18, 2025, 2:22 AM

exploratory data analysis in python a step by step process

What is exploratory analysis, how it is structured and how to apply it in Python with the help of Pandas and other data analysis and… Exploratory data analysis (EDA) is an especially important activity in the routine of a data analyst or scientist. It enables an in depth understanding of the dataset, define or discard hypotheses and create predictive models on a solid basis. It uses data manipulation techniques and several statistical tools to describe and understand the relationship between variables and how these can impact business. In fact, it’s thanks to EDA that we can ask ourselves meaningful questions that can impact business. Let’s face it: staring at a raw dataset for the first time can feel overwhelming.

You’ve got rows of numbers, cryptic column names, and a lingering question: “Where do I even start?” That’s where Exploratory Data Analysis (EDA) comes in. Think of EDA as your detective toolkit for uncovering hidden patterns, spotting errors, and asking better questions about your data. In this article, I’ll walk you through a practical, step-by-step EDA process using Python. You’ll learn how to clean, visualize, and interpret data efficiently—no PhD in statistics is required. I’ll even share a real-world example to keep things relatable. Let’s dive in.

EDA is the process of investigating a dataset to summarize its key characteristics, such as mean, median, and data types. It helps identify errors like missing values, outliers, and duplicates. Additionally, EDA uncovers relationships between variables and guides your next steps, such as feature engineering or model selection. Think of it like getting to know a new friend: you ask questions, notice quirks, and learn what makes them tick. Imagine building a house on a faulty foundation—without EDA, your data analysis or machine learning model risks the same fate. Here’s why EDA is non-negotiable:

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. Exploratory Data Analysis (EDA) is a critical first step in any data science or machine learning project. It allows you to understand your dataset’s structure, uncover patterns, detect anomalies, and prepare the data for further modeling.

In this article, you’ll find a comprehensive, step-by-step cheat sheet for conducting EDA using Python’s powerful Pandas library. Whether you’re just starting out or need a handy reference, this guide covers everything from loading data to advanced transformations and visualizations. Before any analysis, you need to import your dataset. Pandas supports many data formats: Identify and fix missing or inconsistent data: Transform your data to make it more insightful:

Exploratory Data Analysis (EDA) is a crucial initial step in the data science pipeline. It involves summarizing, visualizing, and understanding the main characteristics of a dataset. EDA helps data scientists and analysts to identify patterns, detect outliers, test hypotheses, and check assumptions before applying more complex statistical or machine learning techniques. Python, with its rich ecosystem of libraries such as Pandas, NumPy, Matplotlib, and Seaborn, provides a powerful environment for performing EDA efficiently. In this blog post, we will take you through a step-by-step guide on how to perform EDA using Python. We’ll cover the fundamental concepts, usage methods, common practices, and best practices.

We’ll start by importing the necessary libraries. These libraries will be used throughout the EDA process. For this example, we’ll use the famous Iris dataset, which can be easily loaded using the seaborn library. Before diving into the analysis, it’s important to understand the structure and content of the dataset. Data cleaning is an essential step in EDA. It involves handling missing values, duplicates, and outliers.

Exploratory Data Analysis (EDA) is a crucial initial step in any data analysis project. It involves examining and summarizing data to understand its structure, identify patterns, and uncover insights. Python, with its powerful libraries like Pandas, Matplotlib, Seaborn, and Plotly, provides an ideal environment for conducting EDA effectively. In this step-by-step guide, we will walk you through the process of performing EDA in Python. Before diving into exploratory data analysis, you need to set up your Python environment and load the dataset you want to analyze. In this section, we’ll cover:

Clean and well-structured data is essential for meaningful analysis. In this section, we’ll focus on: This section is the heart of EDA, where we explore and visualize the data to gain insights: Feature engineering involves creating new features or modifying existing ones to enhance the dataset’s quality and the model’s performance. Here, we’ll cover: This article is about Exploratory Data Analysis(EDA) in Pandas and Python.

The article will explain step by step how to do Exploratory Data Analysis plus examples. EDA is an important step in Data Science. The goal of EDA is to identify errors, insights, relations, outliers and more. The image below illustrate the data science workflow and where EDA is located: Source: Exploratory Data Analysis - wikipedia Imagine that you are expecting royal guests for dinner.

You are asked to research a special menu from a cooking book with thousands of recipes. As they are very pretentious you need to avoid some ingredients or find exact quantities for others. Dinner and launch menus are needed. First, let’s start by loading the dataset into our Python environment. We will use the pandas library, a powerful tool for data manipulation and analysis. Here’s an example of how to read a CSV file and store it as a pandas DataFrame:

Visualizing the data is a crucial step in EDA as it allows us to gain insights and identify patterns or trends. Let’s explore some common data visualization techniques using Python’s matplotlib and seaborn libraries. A histogram provides a graphical representation of the distribution of a numerical variable. It helps us understand the data’s central tendency, spread, and shape. Scatter plots are useful for visualizing the relationship between two numerical variables. They help us identify any patterns, correlations, or outliers.

Summary statistics provide a concise summary of the main characteristics of a dataset. Let’s calculate some common summary statistics using Python’s pandas library.

Exploratory Data Analysis In Python A Step By Step Process

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