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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 2. NumPy Arrays FREE CHAPTER 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

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. Let us create an array b that we shall use for practicing the further examples:

In: b = np.arange(24).reshape(2,3,4) 
 
In: print(b) 
 
Out: [[[ 0,  1,  2,  3], 
        [ 4,  5,  6,  7], 
        [ 8,  9, 10, 11]], 
 
       [[12, 13, 14, 15], 
        [16, 17, 18, 19], 
        [20, 21, 22, 23]]]) 

We can manipulate array shapes using the following functions:

  • Ravel: We can accomplish this with the ravel() function as follows:

            In: 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]]]) 
    ...
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Python Data Analysis, Second Edition - Second Edition
Published in: Mar 2017
Publisher: Packt
ISBN-13: 9781787127487
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