In today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data.
This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights.
Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries.
By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.

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Spis treści

Exploratory Data Analysis with Python Cookbook. Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data eBook -- spis treści

• 1. Generating Summary Statistics
• 2. Preparing Data for EDA
• 3. Visualising Data in Python
• 4. Performing Univariate Analysis in Python
• 5. Performing Bivariate analysis in Python
• 6. Performing Multivariate analysis in Python
• 7. Analysing Time Series data
• 8. Analysing Text data
• 9. Dealing with Outliers and Missing values
• 10. Performing Automated EDA in Python