Book Image

Practical Data Analysis Cookbook

By : Tomasz Drabas
Book Image

Practical Data Analysis Cookbook

By: Tomasz Drabas

Overview of this book

Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors. This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more. First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews. By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.
Table of Contents (19 chapters)
Practical Data Analysis Cookbook
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
Index

Preparing a dataset to estimate discrete choice models


The dataset prepared for these recipes consists of 10,000 choice situations. The first column contains the ID of the chosen alternative and the second column contains a list of all the alternatives the person was choosing from; not all of the alternatives were available in all choice situations.

Getting ready

To execute this query, you will need pandas, NumPy, and the regular expressions module. No other prerequisites are required.

How to do it…

Python Biogeme requires a tab-separated dataset, where each row contains the attributes of all the alternatives and the flag for the chosen one (an integer) as well as an indicator whether the alternative was available to the decision-maker at the time when the decision was made. Only numerical values are allowed. The following code can be found in the dcm_dataPrep.py file:

import pandas as pd
import numpy as np
import re

# read datasets
observations_filename = '../../Data/Chapter10/observations...