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

Collecting financial data

Back in Chapter 2, Working with Pandas DataFrames, and Chapter 3, Data Wrangling with Pandas, we worked with APIs to gather data; however, there are other ways to collect data from the Internet. We can use web scraping to extract data from the HTML page itself, which pandas offers with the pd.read_html() function—it returns a dataframe for each of the HTML tables it finds on the page. For economic and financial data, an alternative is the pandas_datareader package, which the StockReader class in the stock_analysis package uses to collect financial data.

Important note

In case anything has changed with the data sources that are used in this chapter or you encounter errors when using the StockReader class to collect data, the CSV files in the data/ folder can be read in as a replacement in order to follow along with the text; for example:

pd.read_csv('data/bitcoin.csv', index_col='date', parse_dates=True...