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Python Data Mining Quick Start Guide

You're reading from   Python Data Mining Quick Start Guide A beginner's guide to extracting valuable insights from your data

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789800265
Length 188 pages
Edition 1st Edition
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Author (1):
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Nathan Greeneltch Nathan Greeneltch
Author Profile Icon Nathan Greeneltch
Nathan Greeneltch
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Table of Contents (9) Chapters Close

Preface 1. Data Mining and Getting Started with Python Tools 2. Basic Terminology and Our End-to-End Example FREE CHAPTER 3. Collecting, Exploring, and Visualizing Data 4. Cleaning and Readying Data for Analysis 5. Grouping and Clustering Data 6. Prediction with Regression and Classification 7. Advanced Topics - Building a Data Processing Pipeline and Deploying It 8. Other Books You May Enjoy

Access, search, and sanity checks with pandas

Pandas includes some built-in access functions and search/filter functions to make life easier for users. Pandas also has some sanity checks that are available for you to quickly view your data and ensure that you have the correct batch loaded. For example, we've used the head() method, which displays the first five rows with column names, as a way to check which data we loaded in the beginning of this chapter. Don't by shy about sanity checks; if your company has a lot of money riding on the outcome of your analysis, then the last thing you want to do is to mistakenly work with the wrong data loaded.

For ad hoc work in the IPython console, you don't have to include print statements in order to send your output to a console. For example, you can simply pass df.head() into the IPython console and return the first five...
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