Exploratory Data Analysis with Python Cookbook: Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data

Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide

Purchase of the print or Kindle book includes a free PDF eBook

Key Features:

• Gain practical experience in conducting EDA on a single variable of interest in Python
• Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python
• Get well versed in data visualization using leading Python libraries like Matplotlib and seaborn

Book Description:

Exploratory data analysis (EDA) is a crucial step in data analysis and machine learning projects as it helps in uncovering relationships and patterns and provides insights into structured and unstructured datasets. With various techniques and libraries available for performing EDA, choosing the right approach can sometimes be challenging. This hands-on guide provides you with practical steps and ready-to-use code for conducting exploratory analysis on tabular, time series, and textual data.

The book begins by focusing on preliminary recipes such as summary statistics, data preparation, and data visualization libraries. As you advance, you'll discover how to implement univariate, bivariate, and multivariate analyses on tabular data. Throughout the chapters, you'll become well versed in popular Python visualization and data manipulation libraries such as seaborn and pandas.

By the end of this book, you will have mastered the various EDA techniques and implemented them efficiently on structured and unstructured data.

What You Will Learn:

• Perform EDA with leading Python data visualization libraries
• Execute univariate, bivariate, and multivariate analyses on tabular data
• Uncover patterns and relationships within time series data
• Identify hidden patterns within textual data
• Discover different techniques to prepare data for analysis
• Overcome the challenge of outliers and missing values during data analysis
• Leverage automated EDA for fast and efficient analysis

Who this book is for:

If you are a data analyst interested in the practical application of exploratory data analysis in Python, then this book is for you. This book will also benefit data scientists, researchers, and statisticians who are looking for hands-on instructions on how to apply EDA techniques using Python libraries. Basic knowledge of Python programming and a basic understanding of fundamental statistical concepts is a prerequisite.

透過本全面指南，利用各種分析和視覺化技術從數據中提取有價值的洞察力。

購買印刷版或Kindle電子書，即可免費獲得PDF電子書。

主要特點：
- 在Python中獲得對單一變量進行探索性數據分析（EDA）的實踐經驗
- 學習在Python中分析和探索表格、時間序列和文本數據的不同技術
- 熟練使用Matplotlib和seaborn等領先的Python庫進行數據可視化

書籍描述：
探索性數據分析（EDA）是數據分析和機器學習項目中的關鍵步驟，它有助於揭示關係和模式，並提供結構化和非結構化數據的洞察力。由於有多種技術和庫可用於進行EDA，選擇正確的方法有時可能具有挑戰性。本實踐指南為您提供了進行表格、時間序列和文本數據的探索性分析的實際步驟和即用代碼。

本書首先關注摘要統計、數據準備和數據可視化庫等初步技巧。隨著您的進一步學習，您將發現如何在表格數據上實施單變量、雙變量和多變量分析。在各章中，您將熟練掌握流行的Python可視化和數據操作庫，如seaborn和pandas。

通過閱讀本書，您將掌握各種EDA技術，並在結構化和非結構化數據上高效實施它們。

學到什麼：
- 使用領先的Python數據可視化庫進行EDA
- 在表格數據上執行單變量、雙變量和多變量分析
- 揭示時間序列數據中的模式和關係
- 識別文本數據中的隱藏模式
- 發現不同的數據準備技術
- 在數據分析過程中克服異常值和缺失值的挑戰
- 利用自動化EDA進行快速高效的分析

本書適合對Python中探索性數據分析的實際應用感興趣的數據分析師。本書還將對希望使用Python庫進行EDA技術的數據科學家、研究人員和統計學家有所裨益。具備Python編程的基本知識和基本統計概念的基礎是必要的先決條件。