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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 2. NumPy Arrays FREE CHAPTER 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

Manipulating array shapes


We have already learned about the reshape() function. Another repeating chore is the flattening of arrays. Flattening in this setting entails transforming a multidimensional array into a one-dimensional array. The code for this example is in the shapemanipulation.py file in this book's code bundle.

import numpy as np

# Demonstrates multi dimensional arrays slicing.
#
# Run from the commandline with
#
#  python shapemanipulation.py
print "In: b = arange(24).reshape(2,3,4)"
b = np.arange(24).reshape(2,3,4)

print "In: b"
print b
#Out: 
#array([[[ 0,  1,  2,  3],
#        [ 4,  5,  6,  7],
#        [ 8,  9, 10, 11]],
#
#       [[12, 13, 14, 15],
#        [16, 17, 18, 19],
#        [20, 21, 22, 23]]])

print "In: b.ravel()"
print b.ravel()
#Out: 
#array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,
#       17, 18, 19, 20, 21, 22, 23])

print "In: b.flatten()"
print b.flatten()
#Out: 
#array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,...
You have been reading a chapter from
Python Data Analysis
Published in: Oct 2014
Publisher:
ISBN-13: 9781783553358
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