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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 word clouds

You may have seen word clouds produced by Wordle or other software 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 be able to beat the quality produced by Wordle yet. We can create a word cloud via the Wordle web page at 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 for this. The final code is in the ch-09.ipynb 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...
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