Book Image

The Data Wrangling Workshop - Second Edition

By : Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar
Book Image

The Data Wrangling Workshop - Second Edition

By: Brian Lipp, Shubhadeep Roychowdhury, Dr. Tirthajyoti Sarkar

Overview of this book

While a huge amount of data is readily available to us, it is not useful in its raw form. For data to be meaningful, it must be curated and refined. If you’re a beginner, then The Data Wrangling Workshop will help to break down the process for you. You’ll start with the basics and build your knowledge, progressing from the core aspects behind data wrangling, to using the most popular tools and techniques. This book starts by showing you how to work with data structures using Python. Through examples and activities, you’ll understand why you should stay away from traditional methods of data cleaning used in other languages and take advantage of the specialized pre-built routines in Python. Later, you’ll learn how to use the same Python backend to extract and 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, the book teaches you how to handle missing or incorrect data, and reformat it based on the requirements from your downstream analytics tool. By the end of this book, you will have developed a solid understanding of how to perform data wrangling with Python, and learned several techniques and best practices to extract, clean, transform, and format your data efficiently, from a diverse array of sources.
Table of Contents (11 chapters)

Lists, Sets, Strings, Tuples, and Dictionaries

Now that we have touched upon a few advantages of using Python, we will start by exploring various basic data structures in Python. We will also learn about a few techniques we can use to handle these data structures. This is invaluable for a data practitioner.


Lists are fundamental Python data structures that have continuous memory locations and can host different data types (such as strings, numbers, floats, and doubles) and can be accessed by the index.

We will start with a list and list comprehension. A list comprehension is a syntactic sugar (or shorthand) for a for loop, which iterates over a list. We will generate a list of numbers, and then examine which ones among them are even. We will sort, reverse, and check for duplicates. We will also see the different ways we can access the list elements, iterating over them and checking the membership of an element.

The following is an example of a simple list:

list_example = [51, 27, 34, 46, 90, 45, -19]

The following is also an example of a list:

list_example2 = [15, "Yellow car", True, 9.456, [12, "Hello"]]

As you can see, a list can contain any number of the allowed data types, such as int, float, string, and boolean, and a list can also be a mix of different data types (including nested lists).

If you are coming from a strongly typed language, such as C, C++, or Java, then this will probably be strange as you are not allowed to mix different kinds of data types in a single array in those languages. Lists in Python are loosely typed, that is, they are not restricted to a single type. Lists are somewhat like arrays in the sense that they are both based on continuous memory locations and can be accessed using indexes. But the power of Python lists comes from the fact that they can host different data types and you are allowed to manipulate the data.

In Python, there is a concept of creating a slice of a list. Here is the syntax:

my_list [ inclusive start index : exclusive end index ]

Known as list slicing, this returns a smaller list from the original list by extracting only a part of it. To slice a list, we need two integers. The first integer will denote the start of the slice and the second integer will denote the end. Notice that slicing does not include the third index or the end element. A slice is a chunk of the list tuple or string. The range is from 0 to 1 minus the total length. The first number given represents the first position to include in the slice. The second number is used to indicate which place you want to stop at, but not include. A slice can have an index of –1 to indicate the last element.

The indices will be automatically assigned, as follows:

Figure 1.3: List showing the forward and backward indices

Figure 1.3: List showing the forward and backward indices


Be careful, though, as the very power of lists, and the fact that you can mix different data types in a single list, can actually create subtle bugs that can be very difficult to track.