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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

Use cases of the read_csv method


The read_csv method can be put to a variety of uses. Let us look at some such use cases.

Passing the directory address and filename as variables

Sometimes it is easier and viable to pass the directory address and filename as variables to avoid hard-coding. More importantly so, when one doesn't want to hardcode the full address of the file and intend to use this full address many times. Let us see how we can do so while importing a dataset.

import pandas as pd
path = 'E:/Personal/Learning/Datasets/Book'
filename = 'titanic3.csv'
fullpath = path+'/'+filename
data = pd.read_csv(fullpath)

For such cases, alternatively, one can use the following snippet that uses the path.join method in an os package:

import pandas as pd
import os
path = 'E:/Personal/Learning/Datasets/Book'
filename = 'titanic3.csv'
fullpath = os.path.join(path,filename)
data = pd.read_csv(fullpath)

One advantage of using the latter method is that it trims the lagging or leading white spaces, if any...

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