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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

Creating word clouds


You may have seen word clouds produced by Wordle or others before. If not, you will see them soon enough in this chapter. A couple of Python libraries can create word clouds; however, these libraries don't seem to beat the quality produced by Wordle yet. We can create a word cloud via the Wordle web page on http://www.wordle.net/advanced. Wordle requires a list of words and weights in the following format:

Word1 : weight
Word2 : weight

Modify the code from the previous example to print the word list. As a metric, we will use the word frequency and select the top percent. We don't need anything new and the final code is in the cloud.py file in this book's code bundle:

from nltk.corpus import movie_reviews
from nltk.corpus import stopwords
from nltk import FreqDist
import string

sw = set(stopwords.words('english'))
punctuation = set(string.punctuation)

def isStopWord(word):
    return word in sw or word in punctuation
review_words = movie_reviews.words()
filtered = [w.lower...
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