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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

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Product type Paperback
Published in Mar 2017
Publisher Packt
ISBN-13 9781787127487
Length 330 pages
Edition 2nd Edition
Languages
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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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Table of Contents (16) Chapters Close

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

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 DataFrame.rolling() function provides the same functionality with different values of the win_type string parameter corresponding to different window functions...

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