Book Image

Hands-On Data Preprocessing in Python

By : Roy Jafari
5 (2)
Book Image

Hands-On Data Preprocessing in Python

5 (2)
By: Roy Jafari

Overview of this book

Hands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who’s developed college-level courses on data preprocessing and related subjects. With this book, you’ll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing – data collection, data cleaning, data integration, data reduction, and data transformation – and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you’ll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.
Table of Contents (24 chapters)
1
Part 1:Technical Needs
6
Part 2: Analytic Goals
11
Part 3: The Preprocessing
18
Part 4: Case Studies

Example 3 (challenges 1, 3, 5, and 6)

In this example, we would like to figure out what makes a song rise to the top 10 songs on Billboard (https://www.billboard.com/charts/hot-100) and stay there for at least 5 weeks. Billboard magazine publishes a weekly chart that ranks popular songs based on sales, radio play, and online streaming in the United States. We will integrate three CSV files – billboardHot100_1999-2019.csv, songAttributes_1999-2019.csv, and artistDf.csv from https://www.kaggle.com/danield2255/data-on-songs-from-billboard-19992019 to do this.

This is going to be a long example with many pieces that come together. How you organize your thoughts and work in such data integration challenges is very important. So, before reading on, spend some time getting to know these three data sources and form a plan. This will be a very valuable practice.

Now that you've had a chance to think about how you would go about this, let's do this together. These datasets...