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Python Data Analysis, Second Edition

You're reading from Python Data Analysis, Second Edition Data manipulation and complex data analysis with Python

Product type Paperback

Published in Mar 2017

Publisher Packt

ISBN-13 9781787127487

Length 330 pages

Edition 2nd Edition

Languages

Python

Tools

Scikit-learn

Concepts

Data Analysis

Author (1):

Ivan Idris

View More author details

Table of Contents (16) Chapters

Preface

1. Getting Started with Python Libraries FREE CHAPTER

2. NumPy Arrays

3. The Pandas Primer

4. Statistics and Linear Algebra

5. Retrieving, Processing, and Storing Data

6. Data Visualization

7. Signal Processing and Time Series

8. Working with Databases

9. Analyzing Textual Data and Social Media

10. Predictive Analytics and Machine Learning

11. Environments Outside the Python Ecosystem and Cloud Computing

12. Performance Tuning, Profiling, and Concurrency

A. Key Concepts

B. Useful Functions

C. Online Resources

Preprocessing

Library scikit-learn has a preprocessing module, which is the topic of this section. In the previous chapter, Chapter 9, Analyzing Textual Data and Social Media, we installed scikit-learn, and we practiced a form of data preprocessing by filtering out stopwords. Some machine learning algorithms have trouble with data that is not distributed as a Gaussian with a mean of 0 and a variance of 1. The sklearn.preprocessing module takes care of this issue. We will be demonstrating it in this section. We will preprocess the meteorological data from the Dutch KNMI institute (original data for De Bilt weather station from http://www.knmi.nl/climatology/daily_data/datafiles3/260/etmgeg_260.zip). The data is just one column of the original datafile and contains daily rainfall values. It is stored in the .npy format discussed in Chapter 5, Retrieving, Processing, and Storing Data. We can load the data into a NumPy array. The values are integers that we have to multiply by 0.1 in order...

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Authors (1)

Idris

Idris

Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan Idris enjoys writing clean, testable code and interesting technical articles. Ivan Idris is the author of NumPy 1.5. Beginner's Guide and NumPy Cookbook by Packt Publishing.

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