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

Applying logit() to transform proportions


We can transform proportions or ratios with the SciPy logit() function. The result should be a more Gaussian distribution. This function is defined by the following equation:

As you can see in equation (4.4), the logit is the logarithm of the odds. What we want to achieve with this transformation is getting a more symmetric distribution—a skew close to zero. As the proportions approach zero and one, the logit asymptotically approaches minus infinity and infinity, so we have to be careful in those cases.

As an example of a proportion, we will take the monthly proportions of rainy days. We get these proportions by turning rain amounts into a binary variable and then averaging over each month.

How to do it...

Transform the ratios by following this guide:

  1. The imports are as follows:

    import dautil as dl
    import seaborn as sns
    import matplotlib.pyplot as plt
    import pandas as pd
    import math
    import statsmodels.api as sm
    from scipy.special import logit
    from IPython...