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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

Various methods of importing data in Python


pandas is the Python library/package of choice to import, wrangle, and manipulate datasets. The datasets come in various forms; the most frequent being in the .csv format. The delimiter (a special character that separates the values in a dataset) in a CSV file is a comma. Now we will look at the various methods in which you can read a dataset in Python.

Case 1 – reading a dataset using the read_csv method

Open an IPython Notebook by typing ipython notebook in the command line.

Download the Titanic dataset from the shared Google Drive folder (any of .xls or .xlsx would do). Save this file in a CSV format and we are good to go. This is a very popular dataset that contains information about the passengers travelling on the famous ship Titanic on the fateful sail that saw it sinking. If you wish to know more about this dataset, you can go to the Google Drive folder and look for it.

A common practice is to share a variable description file with the dataset...

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