Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
5 (1)
Book Image

Hands-On Data Analysis with Pandas - Second Edition

5 (1)
By: Stefanie Molin

Overview of this book

Extracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.
Table of Contents (21 chapters)
Section 1: Getting Started with Pandas
Section 2: Using Pandas for Data Analysis
Section 3: Applications – Real-World Analyses Using Pandas
Section 4: Introduction to Machine Learning with Scikit-Learn
Section 5: Additional Resources


Complete the following exercises to practice the skills covered in this chapter. Be sure to consult the Machine learning workflow section in the Appendix as a refresher on the process of building models:

  1. Predict star temperature with elastic net linear regression as follows:

    a) Using the data/stars.csv file, build a pipeline to normalize the data with a MinMaxScaler object and then run elastic net linear regression using all the numeric columns to predict the temperature of the star.

    b) Run grid search on the pipeline to find the best values for alpha, l1_ratio, and fit_intercept for the elastic net in the search space of your choice.

    c) Train the model on 75% of the initial data.

    d) Calculate the R2 of your model.

    e) Find the coefficients for each regressor and the intercept.

    f) Visualize the residuals using the plot_residuals() function from the ml_utils.regression module.

  2. Perform multiclass classification of white wine quality using a support vector machine and feature...