Book Image

Data Wrangling with Python

By : Dr. Tirthajyoti Sarkar, Shubhadeep Roychowdhury
Book Image

Data Wrangling with Python

By: Dr. Tirthajyoti Sarkar, Shubhadeep Roychowdhury

Overview of this book

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.
Table of Contents (12 chapters)
Data Wrangling with Python
Preface
Appendix

Advanced Data Structures


We will start this chapter by discussing advanced data structures. We will do that by revisiting lists. We will construct a stack and a queue, explore multiple element membership checking, and throw a bit of functional programming in for good measure. If all of this sounds intimidating, then do not worry. We will get to things step by step, like in the previous chapter, and you will feel confident once you have finished this chapter.

To start this chapter, you have to open an empty notebook. To do that, you can simply input the following command in a shell. It is advised that you first navigate to an empty directory using cd before you enter the command:

docker run -p 8888:8888 -v 'pwd':/notebooks -it rcshubhadeep/packt-data-wrangling-base:latest

Once the Docker container is running, point your browser to http://localhost:8888 and use dw_4_all as the passcode to access the notebook interface.

Iterator

We will start off this topic with lists. However, before we get into...