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

Analyzing peaks


The analysis of peaks is similar to that of valleys, since both are extreme values. SciPy has the argrelmax() function that finds the relative maxima. When we apply this function to daily temperature values, it not only finds hot days in summer but also hot days in winter unless we make the function consider a larger time frame. Of course, we can also check whether values are above a threshold or only select summer data using prior knowledge.

When we analyze peaks in time series data, we can apply two approaches. The first approach is to consider the highest peaks in a year, a month, or another fixed time interval and build a series with those values. The second approach is to define any value above a threshold as a peak. In this recipe, we will use the 95th percentile as the threshold. In the context of this approach, we can have multiple peaks in a sequence. Long streaks can have a negative impact, for instance, in the case of heat waves.

How to do it...

  1. The imports are as...