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

Combining box plots and kernel density plots with violin plots


Violin plots combine box plots and kernel density plots or histograms in one type of plot. Seaborn and matplotlib both offer violin plots. We will use Seaborn in this recipe on z-scores of weather data. The z-scoring is not essential, but without it, the violins will be more spread out.

How to do it...

  1. Import the required libraries as follows:

    import seaborn as sns
    from dautil import data
    import matplotlib.pyplot as plt
  2. Load the weather data and calculate z-scores:

    df = data.Weather.load()
    zscores = (df - df.mean())/df.std()
  3. Plot a violin plot of the z-scores:

    %matplotlib inline
    plt.figure()
    plt.title('Weather Violin Plot')
    sns.violinplot(zscores.resample('M'))
    plt.ylabel('Z-scores')

    Refer to the following plot for the first violin plot:

  4. Plot a violin plot of rainy and dry (the opposite of rainy) days against wind speed:

    plt.figure()
    plt.title('Rainy Weather vs Wind Speed')
    categorical = df
    categorical['RAIN'] = categorical['RAIN'] &gt...