Book Image

Hands-On Data Analysis with Pandas

By : Stefanie Molin
Book Image

Hands-On Data Analysis with Pandas

By: Stefanie Molin

Overview of this book

Data analysis has become a necessary skill in a variety of domains where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with 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 powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will be able 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. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets.
Table of Contents (21 chapters)
Free Chapter
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

Summary

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. 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 have come together and how these libraries can work harmoniously with other packages...