Book Image

The Data Analysis Workshop

By : Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev
Book Image

The Data Analysis Workshop

By: Gururajan Govindan, Shubhangi Hora, Konstantin Palagachev

Overview of this book

Businesses today operate online and generate data almost continuously. While not all data in its raw form may seem useful, if processed and analyzed correctly, it can provide you with valuable hidden insights. The Data Analysis Workshop will help you learn how to discover these hidden patterns in your data, to analyze them, and leverage the results to help transform your business. The book begins by taking you through the use case of a bike rental shop. You'll be shown how to correlate data, plot histograms, and analyze temporal features. As you progress, you’ll learn how to plot data for a hydraulic system using the Seaborn and Matplotlib libraries, and explore a variety of use cases that show you how to join and merge databases, prepare data for analysis, and handle imbalanced data. By the end of the book, you'll have learned different data analysis techniques, including hypothesis testing, correlation, and null-value imputation, and will have become a confident data analyst.
Table of Contents (12 chapters)
7. Analyzing the Heart Disease Dataset
9. Analysis of the Energy Consumed by Appliances

6. Analysis of Credit Card Defaulters

Activity 6.01: Evaluating the Correlation between Columns Using a Heatmap

  1. Plot the heatmap for all the columns in the DataFrame (other than the ID column) by using sns.heatmap and keep the figure size as 30,10 for better visibility:
    sns.set_context("talk", font_scale=0.7)
  2. Use Spearman as the method parameter to compute Spearman's rank correlation coefficient:
    sns.heatmap(df.iloc[:,1:].corr(method='spearman'), \
                cmap='rainbow_r', annot=True)

    The output of the heatmap is as follows:

    Figure 6.28: Heatmap for Spearman's rank correlation

  3. In order to get the exact correlation coefficients of each column with the DEFAULT column, apply the .corr() function on each column with respect to the DEFAULT column:
    df.drop("DEFAULT", axis=1)\
    .apply(lambda x: x.corr(df.DEFAULT...