Early on, pandas made some unfortunate design decisions that are still biting hard. For example, the choice of datetime (pandas.Timestamp) represented by a 64-bit int with a fixed nanosecond resolution. This choice gives dynamic range of +- 292 years around 1970-01-01 (the epoch). This range is too small to represent the works of William Shakespeare, never mind human history. Using pandas in these areas becomes a royal pain in the neck, for one constantly needs to work around pandas datetime limitations.
OTOH, in numpy one can choose time resolution units (anything from attosecond to a year) tailoring time resolution to your task (from high energy physics all way to astronomy). Panda's choice is only good for high-frequency stock traders, though.
OTOH, in numpy one can choose time resolution units (anything from attosecond to a year) tailoring time resolution to your task (from high energy physics all way to astronomy). Panda's choice is only good for high-frequency stock traders, though.