Time-series data sources

In data science and engineering, one of the most common challenges is temporal data manipulation. Datasets that hold geospatial or transactional data, which mostly lie in the financial and economics area of an application, are some of the most common examples of data that is indexed by a timestamp. Working in areas such as finance, fraud, or even socio-economic temporal data ultimately leads to the need to join, aggregate, and visualize data points.

This temporal data regularly comes in datetime formats that might vary not only in the format itself but in the information that it holds. One of the examples of this is the difference between the DD/MM/YYYY and MM/DD/YYYY format. Misunderstanding these different datetime formats could lead to failures or wrongly formed results if the formats used don't match up. Moreover, this data doesn't come in numerical format, which—as we have seen in previous sections of the chapter—can lead to...