Exploratory Data Analysis with Python Cookbook Over 50 recipes to analyze, visualize, and extract insights
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Free Download Exploratory Data Analysis with Python Cookbook
by Ayodele Oluleye
English | 2023 | ISBN: 1803231106 | 382 pages | True PDF EPUB | 19 MB
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 MatDescriptionlib and seaborn
Book Description
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, MatDescriptionlib, 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.
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
Whether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights.
It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.
Table of Contents
Generating Summary Statistics
Preparing Data for EDA
Visualising Data in Python
Performing Univariate Analysis in Python
Performing Bivariate analysis in Python
Performing Multivariate analysis in Python
Analysing Time Series data
Analysing Text data
Dealing with Outliers and Missing values
Performing Automated EDA in Python
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