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

Comparing with a dummy regressor


The scikit-learn DummyRegressor class implements several strategies for random guessing, which can serve as baseline for regressors. The strategies are as follows:

  • mean: This predicts the mean of the training set.

  • median: This predicts the median of the training set.

  • quantile: This predicts a specified quantile of the training set when provided with the quantile parameter. We will apply this strategy by specifying the first and third quartile.

  • constant: This predicts a constant value that is provided by the user.

We will compare the dummy regressors with the regressors from Chapter 9, Ensemble Learning and Dimensionality Reduction, using R-squared, MSE, MedAE, and MPE.

How to do it...

  1. The imports are as follows:

    import numpy as np
    from sklearn.dummy import DummyRegressor
    import ch10util
    from sklearn import metrics
    import dautil as dl
    from IPython.display import HTML
  2. Load the temperature data as follows:

    y_test = np.load('temp_y_test.npy')
    X_train = np.load('temp_X_train...