Book Image

Python Data Analysis Cookbook

By : Ivan Idris
Book Image

Python Data Analysis Cookbook

By: Ivan Idris

Overview of this book

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.
Table of Contents (23 chapters)
Python Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Glossary
Index

Calculating the mean absolute error and the residual sum of squares


The mean absolute error (MeanAE) and residual sum of squares (RSS) are regression metrics given by the following equations:

The mean absolute error (10.11) is similar to the MSE and MedAE, but it differs in one step of the calculation. The common feature of these metrics is that they ignore the sign of the error and are analogous to variance. MeanAE values are larger than or ideally equal to zero.

The RSS (10.12) is similar to the MSE, except we don't divide by the number of residuals. For this reason, you get larger values with the RSS. However, an ideal fit gives you a zero RSS.

How to do it...

  1. The imports are as follows:

    import ch10util
    import dautil as dl
    from sklearn import metrics
    from IPython.display import HTML
  2. Plot the bootstrapped metrics as follows:

    sp = dl.plotting.Subplotter(3, 2, context)
    ch10util.plot_bootstrap('boosting',
                            metrics.mean_absolute_error, sp.ax)
    sp.label()
    
    ch10util.plot_bootstrap...