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


Using the CSV files in the exercises/ folder and what we have learned so far in this book, complete the following exercises:

  1. With the earthquakes.csv file, select all the earthquakes in Japan with a magnitude of 4.9 or greater using the mb magnitude type.
  2. Create bins for each full number of earthquake magnitude (for instance, the first bin is (0, 1], the second is (1, 2], and so on) with the ml magnitude type and count how many are in each bin.
  3. Using the faang.csv file, group by the ticker and resample to monthly frequency. Make the following aggregations:

    a) Mean of the opening price

    b) Maximum of the high price

    c) Minimum of the low price

    d) Mean of the closing price

    e) Sum of the volume traded

  4. Build a crosstab with the earthquake data between the tsunami column and the magType column. Rather than showing the frequency count, show the maximum magnitude that was observed for each combination. Put the magnitude type along the columns.
  5. Calculate the rolling...