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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

Window functions


NumPy has a number of window routines that can compute weights in a rolling window as we did in the previous section.

A window function is a function that is defined within an interval (the window) or is otherwise zero valued. We can use window functions for spectral analysis and filter design (for more background information, refer to http://en.wikipedia.org/wiki/Window_function). The boxcar window is a rectangular window with the following formula:

w(n) = 1

The triangular window is shaped like a triangle and has the following formula:

In the preceding formula, L can be equal to N, N+1, or N-1. In the last case, the window function is called the Bartlett window. The Blackman window is bell shaped and defined as follows:

The Hanning window is also bell shaped and defined as follows:

In the pandas API, the rolling_window() function provides the same functionality with different values of the win_type string parameter corresponding to different window functions. The other parameter...

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