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)
1
Section 1: Getting Started with Pandas
4
Section 2: Using Pandas for Data Analysis
9
Section 3: Applications – Real-World Analyses Using Pandas
12
Section 4: Introduction to Machine Learning with Scikit-Learn
16
Section 5: Additional Resources
18
Solutions

Exercises

Create the following visualizations using what you have learned up to this point in this book. Use the data from this chapter's data/ directory:

  1. Plot the rolling 20-day minimum of the Facebook closing price using pandas.
  2. Create a histogram and KDE of the change from open to close in the price of Facebook stock.
  3. Using the earthquake data, create box plots for the magnitudes of each magType used in Indonesia.
  4. Make a line plot of the difference between the weekly maximum high price and the weekly minimum low price for Facebook. This should be a single line.
  5. Plot the 14-day moving average of the daily change in new COVID-19 cases in Brazil, China, India, Italy, Spain, and the USA:

    a) First, use the diff() method that was introduced in the Working with time series data section of Chapter 4, Aggregating Pandas DataFrames, to calculate the day-over-day change in new cases. Then, use rolling() to calculate the 14-day moving average.

    b) Make three subplots...