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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

Writing CSV files with NumPy and pandas

In the previous chapters, we learned about reading CSV files. Writing CSV files is just as straightforward, but uses different functions and methods. Let's first generate some data to be stored in the CSV format. Generate a 3 x 4 NumPy array after seeding the random generator in the following code snippet.

Set one of the array values to NaN:

np.random.seed(42)

a = np.random.randn(3, 4)
a[2][2] = np.nan
print a

This code will print the array as follows:

[[ 0.49671415 -0.1382643   0.64768854  1.52302986]
 [-0.23415337 -0.23413696  1.57921282  0.76743473]
 [-0.46947439  0.54256004         nan -0.46572975]]

The NumPy savetxt() function is the counterpart of the NumPy loadtxt() function and can save arrays in delimited file formats such as CSV. Save the array we created with the following function call:

np.savetxt('np.csv', a, fmt='%.2f', delimiter=',', header=" #1,  #2,  #3,  #4")

In the preceding function call...

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