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In this chapter, we will use pandas, a popular Python library with a rich set of I/O tools, data wrangling, and date/time functionality to streamline working with time series data. In addition, you will explore several reader functions available in pandas to ingest data from different file types, such as Comma-Separated Value (CSV), Excel, and SAS. You will explore reading from files, whether stored locally on your drive or remotely on the cloud, such as an AWS S3 bucket.
Time series data is complex and can be in different shapes and formats. Conveniently, the pandas reader functions offer a vast number of arguments (parameters) to help handle such variety in the data.
The pandas library provides two fundamental data structures, Series and DataFrame, implemented as classes. The DataFrame class is a distinct data structure for working with tabular data (think rows and columns in a spreadsheet). The main difference between the...