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Python Data Analysis

You're reading from   Python Data Analysis Learn how to apply powerful data analysis techniques with popular open source Python modules

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Product type Paperback
Published in Oct 2014
Publisher
ISBN-13 9781783553358
Length 348 pages
Edition 1st Edition
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Author (1):
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Ivan Idris Ivan Idris
Author Profile Icon Ivan Idris
Ivan Idris
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Toc

Table of Contents (17) Chapters Close

Preface 1. Getting Started with Python Libraries FREE CHAPTER 2. NumPy Arrays 3. Statistics and Linear Algebra 4. pandas Primer 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
Index

Preprocessing

In the previous chapter, we did 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 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 to get the daily precipitation amounts in mm.

The data has the somewhat quirky feature that values below 0.05 mm are quoted as -1. We will set those values equal to 0.025 (0.05 divided by...

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