Book Image

Python Data Analysis - Second Edition

By : Ivan Idris
Book Image

Python Data Analysis - Second Edition

By: Ivan Idris

Overview of this book

Data analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks. With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis. The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.
Table of Contents (22 chapters)
Python Data Analysis - Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Key Concepts
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): 
 ...