What this book covers
Chapter 1, Pandas Foundations, covers the anatomy and vocabulary used to identify the components of the two main pandas data structures, the Series and the DataFrame. Each column must have exactly one type of data, and each of these data types is covered. You will learn how to unleash the power of the Series and the DataFrame by calling and chaining together their methods.
Chapter 2, Essential DataFrame Operations, focuses on the most crucial and typical operations that you will perform during data analysis.
Chapter 3, Creating and Persisting DataFrames, discusses the various ways to ingest data and create DataFrames.
Chapter 4, Beginning Data Analysis, helps you develop a routine to get started after reading in your data.
Chapter 5, Exploratory Data Analysis, covers basic analysis techniques for comparing numeric and categorical data. This chapter will also demonstrate common visualization techniques.
Chapter 6, Selecting Subsets of Data, covers the many varied and potentially confusing ways of selecting different subsets of data.
Chapter 7, Filtering Rows, covers the process of querying your data to select subsets of it based on Boolean conditions.
Chapter 8, Index Alignment, targets the very important and often misunderstood index object. Misuse of the Index is responsible for lots of erroneous results, and these recipes show you how to use it correctly to deliver powerful results.
Chapter 9, Grouping for Aggregation, Filtration, and Transformation, covers the powerful grouping capabilities that are almost always necessary during data analysis. You will build customized functions to apply to your groups.
Chapter 10, Restructuring Data into a Tidy Form, explains what tidy data is and why it's so important, and then it shows you how to transform many different forms of messy datasets into tidy ones.
Chapter 11, Combining Pandas Objects, covers the many available methods to combine DataFrames and Series vertically or horizontally. We will also do some web-scraping and connect to a SQL relational database.
Chapter 12, Time Series Analysis, covers advanced and powerful time series capabilities to dissect by any dimension of time possible.
Chapter 13, Visualization with Matplotlib, Pandas, and Seaborn, introduces the matplotlib library, which is responsible for all of the plotting in pandas. We will then shift focus to the pandas plot method and, finally, to the seaborn library, which is capable of producing aesthetically pleasing visualizations not directly available in pandas.
Chapter 14, Debugging and Testing Pandas, explores mechanisms of testing our DataFrames and pandas code. If you are planning on deploying pandas in production, this chapter will help you have confidence in your code.
