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Practical Data Analysis

You're reading from  Practical Data Analysis

Product type Book
Published in Oct 2013
Publisher Packt
ISBN-13 9781783280995
Pages 360 pages
Edition 1st Edition
Languages
Author (1):
Hector Cuesta Hector Cuesta
Profile icon Hector Cuesta
Toc

Table of Contents (24) Chapters close

Practical Data Analysis
Credits
Foreword
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
Preface
1. Getting Started 2. Working with Data 3. Data Visualization 4. Text Classification 5. Similarity-based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Disease with Cellular Automata 10. Working with Social Graphs 11. Sentiment Analysis of Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with IPython and Wakari Setting Up the Infrastructure Index

Getting started with Pandas


Pandas is a great library for data manipulation and analysis, written by Wes McKinney. Pandas provide us with optimized data structures such as Series and DataFrame , which are well suited for descriptive statistics, indexing, and aggregation. Pandas is already installed in the Anaconda distribution used in Wakari. In this section, we will present the basic operations with Pandas for time series and multivariate data. We may find more information about Pandas at http://pandas.pydata.org/.

Working with time series

Time series helps us to understand the change in a variable through time. Pandas include specific functionality in order to work with time series transparently. For this section, we need to upload the Gold.csv file used in Chapter 7, Predicting Gold Prices. The first five rows in the file will look as follows:

date,price
1/31/2003,367.5
2/28/2003,347.5
3/31/2003,334.9
4/30/2003,336.8
5/30/2003,361.4
. . .

We will load the Gold.csv file with the read_csv...

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