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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Table of Contents (16) Chapters Close

Preface 1. Getting Started 2. Preprocessing Data FREE CHAPTER 3. Getting to Grips with 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 Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Getting started with pandas

The pandas library is a great library for data manipulation and analysis, written by Wes McKinney. The pandas library provides us with the optimized data structures, 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 can find more information about pandas from its website:

http://pandas.pydata.org/

Working with Time Series

Time Series help us to understand the changes in a variable over 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 like this:

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
.  .  .

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