2
Reading Time Series Data from Files
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 they are 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 ...
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