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


In this chapter, we saw how building Python packages for our analysis applications can make it very easy for others to carry out their own analyses and reproduce ours, as well as for us to create repeatable workflows for future analyses.

The stock_analysis package we created in this chapter contained classes for gathering stock data from the Internet (StockReader); visualizing individual assets or groups of them (Visualizer family); calculating metrics for single assets or groups of them for comparisons (StockAnalyzer and AssetGroupAnalyzer, respectively); and time series modeling with decomposition, ARIMA, and linear regression (StockModeler). We also got our first look at using the statsmodels package in the StockModeler class. This chapter showed us how the pandas, matplotlib, seaborn, and numpy functionality that we've covered so far in this book has come together and how these libraries can work harmoniously with other packages for custom applications. I strongly...