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

Modeling performance using historical data

The goal of this section is to give us a taste of how to build some models; as such, the following examples are not meant to be the best possible model, but rather a simple and relatively quick implementation for learning purposes. Once again, the stock_analysis package has a class for this section's task: StockModeler.

Important note

To fully understand the statistical elements of this section and modeling in general, we need a solid understanding of statistics; however, the purpose of this discussion is to show how modeling techniques can be applied to financial data without dwelling on the underlying mathematics.

The StockModeler class

The StockModeler class will make it easier for us to build and evaluate some simple financial models without needing to interact directly with the statsmodels package. In addition, we will reduce the number of steps that are needed to generate a model with the methods we create. The following...