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
- Introduction to Data Wrangling with Python
- Advanced Data Structures and File Handling
- Introduction to Numpy, Pandas, and Matplotlib
- A Deep Dive into Data Wrangling with Python
- Getting Comfortable with Different Kinds of Data Sources
- Learning the Hidden Secrets of Data Wrangling
- Advanced Web Scraping and Data Gathering
- RDBMS and SQL
- Application of Data Wrangling in Real Life
Dr. Tirthajyoti Sarkar works as a senior principal engineer in the semiconductor technology domain, where he applies cutting-edge data science/machine learning techniques for design automation and predictive analytics. He writes regularly about Python programming and data science topics. He holds a Ph.D. from the University of Illinois and certifications in artificial intelligence and machine learning from Stanford and MIT.
Shubhadeep Roychowdhury works as a senior software engineer at a Paris-based cybersecurity startup, where he is applying state-of-the-art computer vision and data engineering algorithms and tools to develop cutting-edge products. He often writes about algorithm implementation in Python and similar topics. He holds a master’s degree in computer science from West Bengal University Of Technology and certifications in machine learning from Stanford.
