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Exploratory Data Analysis Python And Pandas With Examples

Leo Migdal
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exploratory data analysis python and pandas with examples

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. 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. 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: I have learn a few excellent stuff here. Certainly value bookmarking for revisiting. I surprise how so much effort you set to make the sort of wonderful informative website.

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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. 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. 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:

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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 - ...

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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. Exploratory Data Analysis (EDA) is a important step in data analysis which focuses on understanding patterns, trends an...

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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. 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 Explorato...

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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 uncover...