I come across this a lot. I optimize the importing of a file, but I'm stuck parsing timestamps. I have to map `DateObject` across these timestamps. Mapping is slower than WL functions optimized with a `Listable` attribute. Is there a better way to handle timestamps? I thought I could speed it up by specifying `$DefaultStringFormat` in `Block`, but that didn't help. Any other ideas? Block[{$DateStringFormat = {"Month", "/", "Day", "/", "Year", " ", "Hour12", ":", "Minute", ":", "Second", " ", "AMPM"}}, DateObject /@ listOfTimestamps ]; This is taking 10s when the import takes 0.1s.

Thanks for the correction. I also found that StringReplace works well too, about the same performance: convertString[dateTime_]:=With[{ns=NumberString}, StringReplace[{m:ns~~"/"~~d:ns~~"/"~~y:ns~~" "~~h:ns~~":"~~min:ns~~":"~~s:ns~~" "~~ampm:WordCharacter..:> "{"<>y<>","<>m<>","<>d<>","<>h<>Switch[ampm,"AM","","PM","+12"]<>","<>min<>","<>s<>"}"}] [dateTime]//ToExpression//DateObject ] I optimized this some, instead of using `If`, `Switch` is a tiny bit faster. And instead of using `StringRiffle`, inserting the commas as strings with `StringJoin` gets the 20,000 timestamps converted in less than 0.6s

Genius! So not having the string in DateList form is the slowdown. It's only taking 0.8 seconds to do 20,000. Thank you!

Thanks for replying @Neil Singer and @Rohit Namjoshi, These are large files from our lab equipment. The one I'm looking at has 20,000 rows of data. Just for an apples-to-apples comparison, I converted the first 5000 lines. It takes 2.5s on a 2017 MacBook Pro (15" i7), so comparable to your timings. Is there an easy way to compile a mapped DateObject function? Passing a list of strings to the new compiler isn't obvious to me. And it feels like this could be faster considering the import times.

DateObject isn't Listable, is there a faster way?