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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

Case 2 – reading a dataset using the open method of Python

pandas is a very robust and comprehensive library to read, explore, and manipulate a dataset. But, it might not give an optimal performance with very big datasets as it reads the entire dataset, all at once, and blocks the majority of computer memory. Instead, you can try one of the Python's file handling methods—open. One can read the dataset line by line or in chunks by running a for loop over the rows and delete the chunks from the memory, once they have been processed. Let us look at some of the use case examples of the open method.

Reading a dataset line by line

As you might be aware that while reading a file using the open method, we can specify to use a particular mode that is read, write, and so on. By default, the method opens a file in the read-mode. This method can be useful while reading a big dataset, as this method reads data line-by-line (not at once, unlike what pandas does). You can read datasets...

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