Data Wrangling with Python: Creating actionable data from raw sources

Book Description

Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices.

Key Features

• Focus on the basics of data wrangling
• Study various ways to extract the most out of your data in less time
• Boost your learning curve with bonus topics like random data generation and data integrity checks

Book Description

For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain.

The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You’ll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you’ll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets.

By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.

What you will learn

• Use and manipulate complex and simple data structures
• Harness the full potential of DataFrames and numpy.array at run time
• Perform web scraping with BeautifulSoup4 and html5lib
• Execute advanced string search and manipulation with RegEX
• Handle outliers and perform data imputation with Pandas
• Use descriptive statistics and plotting techniques
• Practice data wrangling and modeling using data generation techniques

Who this book is for

Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.

Table of Contents

1. Introduction to Data Wrangling with Python
2. Advanced Data Structures and File Handling
3. Introduction to Numpy, Pandas, and Matplotlib
4. A Deep Dive into Data Wrangling with Python
5. Getting Comfortable with Different Kinds of Data Sources
6. Learning the Hidden Secrets of Data Wrangling
7. Advanced Web Scraping and Data Gathering
8. RDBMS and SQL
9. Application of Data Wrangling in Real Life

中文：

书名：与Python的数据角力：从原始来源创建可操作的数据

使用这些实际操作的数据卫生提示、技巧和最佳实践简化您的ETL流程。

主要特点

• 关注数据争论的基础知识
• 研究各种方法，在更短的时间内最大限度地利用数据
• 通过随机数据生成和数据完整性检查等额外主题提升学习曲线

图书描述

为了让数据变得有用和有意义，必须对其进行管理和提炼。使用Python处理数据会教给您这些过程背后的核心思想，并让您掌握该领域最流行的工具和技术的知识。

本书从Python的绝对基础开始，主要关注数据结构。然后深入探讨了NumPy和Pandas库等数据争论的基本工具。您将探索有用的见解，了解为什么应该像在其他语言中那样远离传统的数据清理方式，并利用在Python语言中专门的预构建例程。这组Python技巧和技巧还将演示如何使用相同的Python后端，以及如何从包括Internet、大型数据库电子仓库和Excel财务表格在内的一系列源中提取/转换数据。为了帮助您为更具挑战性的场景做准备，您将介绍如何处理丢失或错误的数据，并根据下游分析工具的要求重新格式化数据。这本书将通过真实世界的例子和数据集进一步帮助你掌握概念。

到本书结束时，您将有信心使用各种来源高效地提取、清理、转换和格式化您的数据。

What you will learn

• 使用和操作复杂和简单的数据结构
• 在运行时充分利用DataFrames和numpy.array的潜力
• 使用BeautifulSoup4和html5lib执行Web抓取
• 使用RegEX执行高级字符串搜索和操作
• 处理离群值并使用Pandas执行数据推算
• 使用描述性统计和绘图技术
• 使用数据生成技术练习数据争论和建模

Who this book is for

使用Python进行数据辩论是为热衷于成为一名成熟的数据科学家或分析专家的开发人员、数据分析师和业务分析师设计的。虽然这本书是为初学者准备的，但是要想轻松掌握本文所涵盖的概念，就必须具备有关Python的基础工作知识。掌握关系数据库和SQL的基本知识也会有所帮助。

目录表

1. 介绍如何使用Python进行数据争用
2. 高级数据结构和文件处理
3. Numpy、Pandas和Matplotlib简介
4. 深入探讨与Python的数据争执
5. 熟悉不同类型的数据源
6. 了解数据争论的隐藏秘密
7. 高级Web抓取和数据收集
8. RDBMS和SQL
9. 数据辩论在现实生活中的应用