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

Technical analysis of financial instruments

With technical analysis of assets, metrics (such as cumulative returns and volatility) are calculated to compare various assets to each other. As with the previous two sections in this chapter, we will be writing a module with classes to help us. We will need the StockAnalyzer class for technical analysis of a single asset and the AssetGroupAnalyzer class for technical analysis of a group of assets. These classes are in the stock_analysis/stock_analyzer.py file.

As with the other modules, we will start with our docstring and imports:

"""Classes for technical analysis of assets."""
import math
from .utils import validate_df

The StockAnalyzer class

For analyzing individual assets, we will build the StockAnalyzer class, which calculates metrics for a given asset. The following UML diagram shows all the metrics that it provides:

Figure 7.19 – Structure of the StockAnalyzer...