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

Preprocessing the data

The very first step in preprocessing data for prediction and classification models is to be clear about how far in the future you are planning to make predictions. As discussed, our goal in this case study is to make a prediction for two full weeks (that is, 14 days) in the future. This is critical to know before we start the preprocessing.

The next step is to design a dataset that has two characteristics:

  • First, it must support our prediction needs. For instance, in this case, we want to use historical data to predict hospitalizations in two weeks.
  • Second, the dataset must be filled with all of the data we have collected. In this example, the data includes covid19hospitalbycounty.csv, covid19cases_test.csv, covid19vaccinesbyzipcode_test.csv, and the dates of US public holidays.

One of the very first things we will do codewise, of course, is to read these datasets into pandas DataFrames. The following list shows the name we used for the...