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Data Wrangling with Python

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  • 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

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.

  • 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
Page Count 452
Course Length 13 hours 33 minutes
Date Of Publication 28 Feb 2019
Applying Your Knowledge to a Real-life Data Wrangling Task
Activity 12: Data Wrangling Task – Fixing UN Data
Activity 13: Data Wrangling Task – Cleaning GDP Data
Activity 14: Data Wrangling Task – Merging UN Data and GDP Data
Activity 15: Data Wrangling Task – Connecting the New Data to the Database
An Extension to Data Wrangling


Dr. Tirthajyoti Sarkar

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

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.