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Learning Predictive Analytics with Python

You're reading from  Learning Predictive Analytics with Python

Product type Book
Published in Feb 2016
Publisher
ISBN-13 9781783983261
Pages 354 pages
Edition 1st Edition
Languages
Authors (2):
Ashish Kumar Ashish Kumar
Profile icon Ashish Kumar
Gary Dougan Gary Dougan
View More author details

Table of Contents (19) Chapters

Learning Predictive Analytics with Python
Credits
Foreword
About the Author
Acknowledgments
About the Reviewer
www.PacktPub.com
Preface
1. Getting Started with Predictive Modelling 2. Data Cleaning 3. Data Wrangling 4. Statistical Concepts for Predictive Modelling 5. Linear Regression with Python 6. Logistic Regression with Python 7. Clustering with Python 8. Trees and Random Forests with Python 9. Best Practices for Predictive Modelling A List of Links
Index

Reading the data – variations and examples


Before we delve deeper into the realm of data, let us familiarize ourselves with a few terms that will appear frequently from now on.

Data frames

A data frame is one of the most common data structures available in Python. Data frames are very similar to the tables in a spreadsheet or a SQL table. In Python vocabulary, it can also be thought of as a dictionary of series objects (in terms of structure). A data frame, like a spreadsheet, has index labels (analogous to rows) and column labels (analogous to columns). It is the most commonly used pandas object and is a 2D structure with columns of different or same types. Most of the standard operations, such as aggregation, filtering, pivoting, and so on which can be applied on a spreadsheet or the SQL table can be applied to data frames using methods in pandas.

The following screenshot is an illustrative picture of a data frame. We will learn more about working with them as we progress in the chapter:

Fig...

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