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Practical Data Analysis Cookbook

You're reading from   Practical Data Analysis Cookbook Over 60 practical recipes on data exploration and analysis

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Product type Paperback
Published in Apr 2016
Publisher
ISBN-13 9781783551668
Length 384 pages
Edition 1st Edition
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Author (1):
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Tomasz Drabas Tomasz Drabas
Author Profile Icon Tomasz Drabas
Tomasz Drabas
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Toc

Table of Contents (13) Chapters Close

Preface 1. Preparing the Data FREE CHAPTER 2. Exploring the Data 3. Classification Techniques 4. Clustering Techniques 5. Reducing Dimensions 6. Regression Methods 7. Time Series Techniques 8. Graphs 9. Natural Language Processing 10. Discrete Choice Models 11. Simulations Index

Chapter 1. Preparing the Data

In this chapter, we will cover the basic tasks of reading, storing, and cleaning data using Python and OpenRefine. You will learn the following recipes:

  • Reading and writing CSV/TSV files with Python
  • Reading and writing JSON files with Python
  • Reading and writing Excel files with Python
  • Reading and writing XML files with Python
  • Retrieving HTML pages with pandas
  • Storing and retrieving from a relational database
  • Storing and retrieving from MongoDB
  • Opening and transforming data with OpenRefine
  • Exploring the data with OpenRefine
  • Removing duplicates
  • Using regular expressions and GREL to clean up the data
  • Imputing missing observations
  • Normalizing and standardizing features
  • Binning the observations
  • Encoding categorical variables
You have been reading a chapter from
Practical Data Analysis Cookbook
Published in: Apr 2016
Publisher:
ISBN-13: 9781783551668
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