The matplotlib color maps are getting a lot of criticism lately because they can be misleading; however, most colormaps are just fine in my opinion. The defaults are getting a makeover in matplotlib 2.0 as announced at http://matplotlib.org/style_changes.html (retrieved July 2015). Of course, there are some good arguments that do not support using certain matplotlib colormaps, such as jet
. In art, as in data analysis, almost nothing is absolutely true, so I leave it up to you to decide. In practical terms, I think it is important to consider how to deal with print publications and the various types of color blindness. In this recipe, I visualize relatively safe colormaps with colorbars. This is a tiny selection of the many colormaps in matplotlib.
Python Data Analysis Cookbook
By :
Python Data Analysis Cookbook
By:
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
Free Chapter
Laying the Foundation for Reproducible Data Analysis
Creating Attractive Data Visualizations
Statistical Data Analysis and Probability
Dealing with Data and Numerical Issues
Web Mining, Databases, and Big Data
Signal Processing and Timeseries
Selecting Stocks with Financial Data Analysis
Text Mining and Social Network Analysis
Ensemble Learning and Dimensionality Reduction
Evaluating Classifiers, Regressors, and Clusters
Analyzing Images
Parallelism and Performance
Glossary
Function Reference
Online Resources
Tips and Tricks for Command-Line and Miscellaneous Tools
Index
Customer Reviews