Eda Exploratory Data Analysis In Python Geeksforgeeks

Leo Migdal

-Nov 17, 2025, 9:54 PM

eda exploratory data analysis in python geeksforgeeks

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. This article provides a comprehensive guide to performing Exploratory Data Analysis (EDA) using Python focusing on the use of NumPy and Pandas for data manipulation and analysis. To perform EDA in Python we need to import several libraries that provide useful tools for data manipulation and statistical analysis.

In this step we load a dataset using Pandas and explore its structure. We can check the type of data and print the first and last 10 records to get a idea of the dataset. Derived columns are new columns created from existing ones. For example here we are converting the population into millions to make it more readable. Sometimes, we may need to rename columns when column names contain special characters or spaces which cause issues in data manipulation. To do this we use .rename() function.

Exploratory Data Analysis (EDA) serves as the foundation of any data science project. It is an essential step where data scientists investigate datasets to understand their structure, identify patterns, and uncover insights. Data preparation involves several steps, including cleaning, transforming, and exploring data to make it suitable for analysis. To effectively work with data, it’s essential to first understand the nature and structure of data. EDA helps answer critical questions about the dataset and guides the necessary preprocessing steps before applying any algorithms. For instance:

Imagine you’re working with a student performance dataset. If some rows are missing test scores, or the names of subjects are inconsistently spelled (e.g., "Math" and "Mathematics"), you’ll need to address these issues before proceeding. EDA helps to identify such problems and clean the data to ensure reliable analysis. Now, we will understand core packages for exploratory data analysis (EDA), including NumPy, Pandas, Seaborn, and Matplotlib. NumPy is used for working with numerical data in Python. Exploratory Data Analysis (EDA) is an important step in data science and data analytics as it visualizes data to understand its main features, find patterns and discover how different parts of the data are...

There are various types of EDA based on nature of records. Depending on the number of columns we are analyzing we can divide EDA into three types: Univariate analysis focuses on studying one variable to understand its characteristics. It helps to describe data and find patterns within a single feature. Various common methods like histograms are used to show data distribution, box plots to detect outliers and understand data spread and bar charts for categorical data. Summary statistics like mean, median, mode, variance and standard deviation helps in describing the central tendency and spread of the data

Bivariate Analysis focuses on identifying relationship between two variables to find connections, correlations and dependencies. It helps to understand how two variables interact with each other. Some key techniques include: Multivariate Analysis identify relationships between two or more variables in the dataset and aims to understand how variables interact with one another which is important for statistical modeling techniques. It include techniques like: In Python, EDA stands for Exploratory Data Analysis.

It is a critical step in the data analysis process that involves examining and visualizing data sets to understand their main characteristics, uncover patterns, identify outliers, and gain insights into the data's underlying structure. Are you interested in learning about Exploratory Data Analysis (EDA) in Python? This tutorial will introduce you to the fundamental concepts and techniques of EDA, which is a crucial step in the data analysis process. EDA helps you understand the underlying patterns, relationships, and structure of your data, allowing you to make informed decisions before proceeding to more advanced analyses or modeling. Exploratory Data Analysis (EDA) is the process of analyzing datasets to summarize their main characteristics, often using visual methods. It involves examining the data to discover patterns, spot anomalies, test hypotheses, and check assumptions.

EDA is an essential step in any data science project as it provides insights that guide the selection of appropriate modeling techniques. Exploratory Data Analysis (EDA) is a critical first step in any data science project. It helps you understand your data's structure, relationships, and potential issues, providing a foundation for further analysis and model building. By leveraging tools like Pandas, Matplotlib, and Seaborn, you can efficiently explore and visualize your data, leading to better insights and more informed decisions. For a detailed step-by-step guide, check out the full article: https://www.geeksforgeeks.org/exploratory-data-analysis-in-python-set-1/. 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 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.

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.

Eda Exploratory Data Analysis In Python Geeksforgeeks

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