Book Image

Hands-On Data Analysis with Pandas - Second Edition

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas - Second Edition

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


Use the stock_analysis package to complete the following exercises. Unless otherwise noted, use data from 2019 through the end of 2020. In case there are any issues collecting the data with the StockReader class, backup CSV files are provided in the exercises/ directory:

  1. Using the StockAnalyzer and StockVisualizer classes, calculate and plot three levels of support and resistance for Netflix's closing price.
  2. With the StockVisualizer class, look at the effect of after-hours trading on the FAANG stocks:

    a) As individual stocks

    b) As a portfolio using the make_portfolio() function from the stock_analysis.utils module

  3. Using the StockVisualizer.open_to_close() method, create a plot that fills the area between the FAANG stocks' opening price (as a portfolio) and its closing price each day in red if the price declined and in green if the price increased. As a bonus, do the same for a portfolio of bitcoin and the S&P 500.
  4. Mutual funds and exchange...