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

Installing Cython


The Cython programming language acts as glue between Python and C/C++. With the Cython tools, we can generate C code from plain Python code, which can then be compiled into binary, which is closer to the machine level. The cytoolz package contains utilities created by Cythonizing the handy Python toolz package. The following command will install cython and cytoolz:

$ pip3 install cython cytoolz

Just as in cooking shows, we will show the results of Cythonizing before going through the process involved (deferred to the next section). The timeit Python module measures time. We will use this module to measure different functions. Define the following function, which accepts a short code snippet, a function call, and the number of times the code will run as arguments:

def time(code, n): 
    times = min(timeit.Timer(code, setup=setup).repeat(3, n)) 
 
    return round(1000* np.array(times)/n, 3) 

Next, we predefine a large setup string containing all the code. The code is in...