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

Speeding up embarrassingly parallel for loops with Joblib


Joblib is a Python library created by the developers of scikit-learn. Its main mission is to improve the performance of long-running Python functions. Joblib achieves these improvements through caching and parallelization using multiprocessing or threading under the hood. Install Joblib as follows:

$ pip3 install joblib

We will reuse the code from the previous example, only changing the parallel() function. Refer to the joblib_demo.py file in this book's code bundle:

def parallel(nprocs): 
    start = timeit.default_timer() 
    Parallel(nprocs)(delayed(simulate)(i) for i in xrange(10, 50)) 
 
    end = timeit.default_timer() - start 
    print(nprocs, "Parallel time", end) 
    return end 

Refer to the following plot for the end result (the number of processors is hardware dependent):