How come standard functions like abs or count work on a dataframe?
Question:
I realized that for some standard python functions like round or abs, instead of invoking them as a method on a dataframe like:
df['MyCol'].round()
df['MyCol'].abs()
or using apply like:
df['MyCol'].apply(round)
df['MyCol'].apply(abs)
I can also call their std library equivalent directly for the same result:
round(df['MyCol'])
abs(df['MyCol'])
How come this works? Is there something in the round and abs implementations that allows to extend them to new types somehow?
Answers:
Many Python built-in functions are designed to be extended by classes, so they’ll call a dunder method if it exists.
From the documentation of round():
For a general Python object number, round delegates to number.__round__.
So when you call round(df['MyCol']) it’s equivalent to
df['MyCol'].__round__()
I realized that for some standard python functions like round or abs, instead of invoking them as a method on a dataframe like:
df['MyCol'].round()
df['MyCol'].abs()
or using apply like:
df['MyCol'].apply(round)
df['MyCol'].apply(abs)
I can also call their std library equivalent directly for the same result:
round(df['MyCol'])
abs(df['MyCol'])
How come this works? Is there something in the round and abs implementations that allows to extend them to new types somehow?
Many Python built-in functions are designed to be extended by classes, so they’ll call a dunder method if it exists.
From the documentation of round():
For a general Python object
number,rounddelegates tonumber.__round__.
So when you call round(df['MyCol']) it’s equivalent to
df['MyCol'].__round__()