best practise apply pint function on pandas dataframe

[SchabiDesigns]

Hi guys,

I am quite sure I am not the first who noticed the bad performance when applying a pint function on a large dataframe. :)
So when I have an column with values and want to convert them to another unit with a given function that takes forever.
I am aware of the workaround with pint-pandas to add the needed values first as separate columns to the dataframe and then calculate the desired result column, but I would like to prevent to code another method to adress data input from dataframes. How do you address that issue?

the function itself is not a big deal...

@ureg.wraps("gravity", ("volt", None, "gravity", "volt", "volt"))
def v_2_g(x: ureg.Quantity, gain: float, sensor_range: ureg.Quantity, volt_range: ureg.Quantity = Q_("10 V"), sensor_volt_range: ureg.Quantity = Q_("5 V")) -> ureg.Quantity:
...

Thanks for your input in advance! 👍
Regards Schabi

[andrewgsavage]

I think using apply will always be slower as it is applied row by row.

Can your function operate on an array?

v_2_g(df['voltage']...)

May just work

[SchabiDesigns]

@andrewgsavage thanks for your answer. 👍
I checked that and I got an TypeError: