What this book covers

Chapter 1 , pandas and Data Analysis, is a hands-on introduction to the key features of pandas.  The idea of this chapter is to provide some context for using pandas in the context of statistics and data science. The chapter will get into several concepts in data science and show how they are supported by pandas.  This will set a context for each of the subsequent chapters, mentioning each chapter relates to both data science and data science processes.

Chapter 2, Up and Running with pandas, instructs the reader on obtain and install pandas, and to get introduce a few of the basic concepts in pandas.  We will also look at how the examples are presented using iPython and Juypter notebook.

Chapter 3, Representing Univariate Data with the Series, walks the reader through the use of the pandas Series, which provides 1-dimensional, indexed data representations.  The reader will learn about how to create Series objects and how to manipulate data held within.  They will also learn about indexes and alignment of data, and about how the Series can be used to slice data.

Chapter 4, Representing Tabular and Multivariate Data with the DataFrame, walks the reader through the basic use of the pandas DataFrame, which provides and indexes multivariate data representations. This chapter will instruct the reader to be able to create DataFrame objects using various sets of static data, and how to perform selection of specific columns and rows within. Complex queries, manipulation, and indexing will be now handled in the following chapter.

Chapter 5, Manipulation and Indexing of DataFrame objects, expands on the previous chapter and instructs you on how to perform more complex manipulations of a DataFrame.  We start by learning how to add, remove, and delete columns and rows; modify data within a DataFrame (or created a modified copy); perform calculations on data within; create hierarchical indexes; and also calculate common statistical results upon DataFrame contents.

Chapter 6, Indexing Data, shows how data can be loaded and saved from external sources into both Series and DataFrame objects. The chapter also covers data access from multiple sources such as files, http servers, database systems, and web services.  Also covered is the processing of data in CSV, HTML, and JSON formats.

Chapter 7, Categorical Data, instructs the reader on how to use the various tools provided by pandas for managing dirty and missing data.

Chapter 8, Numerical and Statistical Methods, covers various techniques for combining, splitting, joining, and merging of data located in multiple pandas objects, and then demonstrates on how to reshape data using concepts such as pivots, stacking, and melting.

Chapter 9, Accessing Data, talks about grouping and performing aggregate data analysis.  In pandas, this is often referred to as the split-apply-combine pattern.  The reader will learn about using this pattern to group data in various different configurations and also apply aggregate functions to calculate results upon each group of data.

Chapter 10, Tidying Up Your Data, explains how to organize data in a tidy form, that is usable for data analysis.

Chapter 11, Combining, Relating and Reshaping Data, tells the readers how they can take data in multiple pandas objects and combine them, through concepts such as joins, merges and concatenation.

Chapter 12, Data Aggregation, dives into the integration of pandas with matplotlib to visualize pandas data. The chapter will demonstrate how to present many common statistical and financial data visualizations including bar charts, histograms, scatter plots, area plots, density plots, and heat maps.

Chapter 13, Time-Series Modeling, covers representing time series data in pandas. This chapter will cover the extensive capabilities provided by pandas for facilitating analysis of time series data.

Chapter 14, Visualization, teaches you how to create data visualizations based upon data stored in pandas data structures.  We start with the basics learning, how to create a simple chart from data and control several of the attributes of the chart (such as legends, labels, and colors).  We examine the creation of several common types of plot used to represent different types of data that are use those plot types to convey meaning in the underlying data.  We also learn how to integrate pandas with D3.js so that we can create rich web-based visualizations.

Chapter 15, Historical Stock Price Analysis, shows you how to apply pandas to basic financial problems. It will focus on data obtained from Yahoo! Finance, and will demonstrate a number of financial concepts in financial data such as calculating returns, moving averages, volatility, and several other concepts. The student will also learns how to apply data visualization to these financial concepts.