Book Image

Python Data Analysis Cookbook

By : Ivan Idris
Book Image

Python Data Analysis Cookbook

By: Ivan Idris

Overview of this book

Data analysis is a rapidly evolving field and Python is a multi-paradigm programming language suitable for object-oriented application development and functional design patterns. As Python offers a range of tools and libraries for all purposes, it has slowly evolved as the primary language for data science, including topics on: data analysis, visualization, and machine learning. Python Data Analysis Cookbook focuses on reproducibility and creating production-ready systems. You will start with recipes that set the foundation for data analysis with libraries such as matplotlib, NumPy, and pandas. You will learn to create visualizations by choosing color maps and palettes then dive into statistical data analysis using distribution algorithms and correlations. You’ll then help you find your way around different data and numerical problems, get to grips with Spark and HDFS, and then set up migration scripts for web mining. In this book, you will dive deeper into recipes on spectral analysis, smoothing, and bootstrapping methods. Moving on, you will learn to rank stocks and check market efficiency, then work with metrics and clusters. You will achieve parallelism to improve system performance by using multiple threads and speeding up your code. By the end of the book, you will be capable of handling various data analysis techniques in Python and devising solutions for problem scenarios.
Table of Contents (23 chapters)
Python Data Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Glossary
Index

Launching multiple tasks with the concurrent.futures module


The concurrent.futures module is a Python module with which we can execute callables asynchronously. If you are familiar with Java and go through the module, you will notice some similarities with the equivalent Java API, such as class names and architecture. According to the Python documentation, this is not a coincidence.

A task in this context is an autonomous unit of work. For instance, printing a document can be considered a task, but usually we consider much smaller tasks, such as adding two numbers.

How to do it...

  1. The imports are as follows:

    import dautil as dl
    import ch12util
    from functools import partial
    import matplotlib.pyplot as plt
    import numpy as np
    from scipy.stats import skew
    import concurrent.futures
    from IPython.display import HTML
    
    STATS = []
  2. Define the following function to resample:

    def resample(arr):
        sample = ch12util.bootstrap(arr)
        STATS.append((sample.mean(), sample.std(), skew(sample)))
  3. Define the following...