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

Profiling the code


Profiling involves identifying parts of the code that need performance tuning because they are either too slow or use a large amount of resources, such as processor power or memory. We will profile a modified version of sentiment analysis code from Chapter 9, Analyzing Textual Data and Social Media. The code is refactored to comply with multiprocessing programming guidelines (you will learn about multiprocessing later in this chapter). We also simplified the stopwords filtering. The third change was to have fewer word features in the code so that the reduction doesn't impact accuracy. This last change has the most impact. The original code ran for about 20 seconds. The new code runs faster than that and will serve as the baseline in this chapter. Some changes are related to profiling and will be explained later in this section. Please refer to the prof_demo.py file in this book's code bundle:

import random 
from nltk.corpus import movie_reviews 
from nltk.corpus import...