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Learning Predictive Analytics with Python

You're reading from   Learning Predictive Analytics with Python Gain practical insights into predictive modelling by implementing Predictive Analytics algorithms on public datasets with Python

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Product type Paperback
Published in Feb 2016
Publisher
ISBN-13 9781783983261
Length 354 pages
Edition 1st Edition
Languages
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Authors (2):
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Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
Gary Dougan Gary Dougan
Author Profile Icon Gary Dougan
Gary Dougan
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Toc

Table of Contents (12) Chapters Close

Preface 1. Getting Started with Predictive Modelling FREE CHAPTER 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. A List of Links
Index

Subsetting a dataset


As discussed in the introductory section, the task of subsetting a dataset can entail a lot of things. Let us look at them one by one. In order to demonstrate it, let us first import the Customer Churn Model dataset, which we used in the last chapter:

import pandas as pd
data=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Customer Churn Model.txt')

Selecting columns

Very frequently, an analyst might come across situations wherein only a handful of columns among a vast number of columns are useful and are required in the model. It then becomes important, to select particular columns. Let us see how to do that.

If one wishes to select the Account Length variable of the data frame we just imported, one can simply write:

account_length=data['Account Length']
account_length.head()

The square bracket ([ ]) syntax is used to subset a column of a data frame. One just needs to type the appropriate column name in the square brackets. Selecting one column returns...

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