Python Data Analysis Cookbook

Chapter 4. Dealing with Data and Numerical Issues

The recipes in this chapter are as follows:

• Clipping and filtering outliers
• Winsorizing data
• Measuring central tendency of noisy data
• Normalizing with the Box-Cox transformation
• Transforming data with the power ladder
• Transforming data with logarithms
• Rebinning data
• Applying logit() to transform proportions
• Fitting a robust linear model
• Taking variance into account with weighted least squares
• Using arbitrary precision for optimization
• Using arbitrary precision for linear algebra

Introduction

In the real world, data rarely matches textbook definitions and examples. We have to deal with issues such as faulty hardware, uncooperative customers, and disgruntled colleagues. It is difficult to predict what kind of ...