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

Fitting data to the exponential distribution


The exponential distribution is a special case of the gamma distribution, which we will also encounter in this chapter. The exponential distribution can be used to analyze extreme values for rainfall. It can also be used to model the time it takes to serve a customer in a queue. For zero and negative values, the probability distribution function (PDF) of the exponential distribution is zero. For positive values, the PDF decays exponentially:

We will use rain data as an example, which is a good candidate for an exponential distribution fit. Obviously, the amount of rain cannot be negative and we know that heavy rain is less likely than no rain at all. In fact, a day without rain is very likely.

How to do it...

The following steps fit the rain data to the exponential distribution:

  1. The imports are as follows:

    from scipy.stats.distributions import expon
    import matplotlib.pyplot as plt
    import dautil as dl
    from IPython.display import HTML
  2. I made a wrapper...