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)

6. Learning the Hidden Secrets of Data Wrangling

Activity 6.01: Handling Outliers and Missing Data


The steps to completing this activity are as follows:


The dataset to be used for this activity can be found at

  1. Load the data:
    import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    %matplotlib inline
  2. Read the .csv file:
    df = pd.read_csv("../datasets/visit_data.csv")


    Don't forget to change the path (highlighted) based on where the CSV file is saved on your system.

  3. Print the data from the DataFrame:

    The output is as follows:

    Figure 6.11: The contents of the CSV file

    As we can see, there is data where some values are missing, and if we examine this, we will see some outliers.

  4. Check for duplicates by using the following command:
    print("First name is duplicated - {}"\
    print("Last name is...