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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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Tools
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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

Basic descriptive statistics with NumPy

In this book, we will try to use as many varied datasets as possible. This depends on the availability of the data. Unfortunately, this means that the subject of the data might not exactly match your interests. Every dataset has its own quirks, but the general skills you acquire in this book should transfer to your own field. In this chapter, we will load a number of Comma-separated Value (CSV) files into NumPy arrays in order to analyze the data.

To load the data, we will use the NumPy loadtxt() function as follows:

Note

The code for this example can be found in basic_stats.py in the code bundle.

import numpy as np
from scipy.stats import scoreatpercentile

data = np.loadtxt("mdrtb_2012.csv", delimiter=',', usecols=(1,), skiprows=1, unpack=True)

print "Max method", data.max()
print "Max function", np.max(data)

print "Min method", data.min()
print "Min function", np.min(data)

print "Mean...
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