Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
Online Resources

Genetic algorithms


This is the most controversial section in the book so far. Genetic algorithms are based on the biological theory of evolution (see http://en.wikipedia.org/wiki/Evolutionary_algorithm). This type of algorithm is useful for searching and optimization. For instance, we can use it to find the optimal parameters for a regression or classification problem.

Humans and other life forms on Earth carry genetic information in chromosomes. Chromosomes are frequently modeled as strings. A similar representation is used in genetic algorithms. The first step is to initialize the population with random individuals and related representations of genetic information. We can also initialize with already-known candidate solutions for the problem. After that, we go through many iterations, called generations. During each generation, individuals are selected for mating based on a predefined fitness function. The fitness function evaluates how close an individual is to the desired solution.

Two...