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

Exploratory data analysis

Now that we have our data, we want to get familiar with it. As we saw in Chapter 5, Visualizing Data with Pandas and Matplotlib and Chapter 6, Plotting with Seaborn and Customization Techniques, creating good visualizations requires knowledge of matplotlib, and—depending on the data format and the end goal for the visualization—seaborn. Just as we did with the StockReader class, we want to make it easier to visualize both individual assets and groups of assets, so rather than expecting users of our package (and, perhaps, our collaborators) to be proficient with matplotlib and seaborn, we will create wrappers around this functionality. This means that users of this package only have to be able to use the stock_analysis package to visualize their financial data. In addition, we are able to set a standard for how the visualizations look and avoid copying and pasting large amounts of code for each new analysis we want to conduct, which brings consistency...