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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 d...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing,
2023.
|
| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Oluleye, Ayodele, |e author. | |
| 245 | 1 | 0 | |a Exploratory data analysis with Python cookbook : |b over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data / |c Ayodele Oluleye. |
| 250 | |a 1st edition. | ||
| 264 | 1 | |a Birmingham : |b Packt Publishing, |c 2023. | |
| 300 | |a 1 online resource (xx, 361 pages) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a computer |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 588 | |a Online resource; title from PDF title page (EBSCO, viewed September 8, 2023). | ||
| 500 | |a Includes index. | ||
| 500 | |a 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 in Python -- Analysing Text data in Python -- Dealing with Outliers and Missing values -- Performing Automated exploratory data analysis in Python. | ||
| 520 | |a 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 DescriptionIn 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. What you will learn Perform EDA with leading python data visualization libraries Execute univariate, bivariate and multivariate analysis on tabular data Uncover patterns and relationships within time series data Identify hidden patterns within textual data Learn different techniques to prepare data for analysis Overcome 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. | ||
| 505 | 0 | |a 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. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 776 | 0 | 8 | |i Print version: |z 9781803231105 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781803231105/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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