Key Features

Develop the skills to perform data cleaning, data integration, data reduction, and data transformation
Get ready to make the most of your data with powerful data transformation and massaging techniques
Perform thorough data cleaning, such as dealing with missing values and outliers

Book Description

Data preprocessing is the first step in data visualization, data analytics, and machine learning, where data is prepared for analytics functions to get the best possible insights. Around 90% of the time spent on data analytics, data visualization, and machine learning projects is dedicated to performing data preprocessing.

This book will equip you with the optimum data preprocessing techniques from multiple perspectives. 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. This book will provide a comprehensive articulation of data preprocessing, its whys and hows, and help you identify opportunities where data analytics could lead to more effective decision making. It also demonstrates 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.
What you will learn

Use Python to perform analytics functions on your data
Understand the role of databases and how to effectively pull data from databases
Perform data preprocessing steps defined by your analytics goals
Recognize and resolve data integration challenges
Identify the need for data reduction and execute it
Detect opportunities to improve analytics with data transformation