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 aggregated data to the gamma distribution


The gamma distribution can be used to model the size of insurance claims, rainfall, and the distribution of inter-spike intervals in brains. The PDF for the gamma distribution is defined by shape k and scale θ as follows:

There is also a definition that uses an inverse scale parameter (used by SciPy). The mean and variance of the gamma distribution are described by (3.3) and (3.4). As you can see, we can estimate the shape parameter from the mean and variance using simple algebra.

How to do it...

Let's fit aggregates for the rain data for January to the gamma distribution:

  1. Start with the following imports:

    from scipy.stats.distributions import gamma
    import matplotlib.pyplot as plt
    import dautil as dl
    import pandas as pd
    from IPython.display import HTML
  2. Load the data and select aggregates for January:

    rain = dl.data.Weather.load()['RAIN'].resample('M').dropna()
    rain = dl.ts.groupby_month(rain)
    rain = rain.get_group(1)
  3. Derive a value for k from the...