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

Summary


In this chapter, we started with the basics of NumPy arrays, including how to create them and their essential properties. We discussed and showed how a NumPy array is optimized for vectorized element-wise operations and differs from a regular Python list. Then, we moved on to practicing various operations on NumPy arrays such as indexing, slicing, filtering, and reshaping. We also covered special one-dimensional and two-dimensional arrays, such as zeros, ones, identity matrices, and random arrays.

In the second major topic of this chapter, we started with pandas series objects and quickly moved on to a critically important object – pandas DataFrames. It is analogous to Excel or MATLAB or a database tab, but with many useful properties for data wrangling. We demonstrated some basic operations on DataFrames, such as indexing, subsetting, row and column addition, and deletion.

Next, we covered the basics of plotting with matplotlib, the most widely used and popular Python library for...