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I use pandas.to_datetime to parse the dates in my data. Pandas by default represent the dates with  datetime64[ns] even though the dates are all daily only. I wonder whether there is an elegant/clever way to convert the dates to or datetime64[D] so that, when I write the data to CSV, the dates are not appended with 00:00:00. I know I can convert the type manually element-by-element:

[dt.to_datetime().date() for dt in df.dates]

But this is really slow since I have many rows and it sort of defeats the purpose of using pandas.to_datetime. Is there a way to convert the dtype of the entire column at once? Or alternatively, does pandas.to_datetime support a precision specification so that I can get rid of the time part while working with daily data?

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by (33.1k points)

In your problem, you are not using any function or parameter which can directly help the Android app to maintain the size of the image by itself.

So I prefer you to use the YOLO detection function in your code to solve this problem. There is a parameter in YOLO detection named ’MAINTAIN_ASPECT’. You need to set this parameter at TRUE to handle image size by itself. 

I hope this solution will debug your code.

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