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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

Creating a multidimensional array


Now that we know how to create a vector, we are set to create a multidimensional NumPy array. After we produce the matrix we will again need to show it, as demonstrated in the following code snippets:

  1. Create a multidimensional array as follows:

            In: m = np.array([np.arange(2), np.arange(2)]) 
            In: m 
            Out: 
            array([[0, 1], 
                   [0, 1]]) 
    
  2. We can show the array shape as follows:

            In: m.shape 
            Out: (2, 2) 
    

We made a 2x2 array with the arange() subroutine. The array() function creates an array from an object that you pass to it. The object has to be an array, for example, a Python list. In the previous example, we passed a list of arrays. The object is the only required parameter of the array() function. NumPy functions tend to have a heap of optional arguments with predefined default options.

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