mutate function

[samukweku]

Brief Description

I would like to propose a mutate function, similar to pandas' assign function, but more flexible - it will also serve as replacement for the transform functions

Example API

df.mutate(y='sum',n=lambda df: df.nth(1))
df.mutate(y='sum',n=lambda df: df.nth(1), by='x')


# replicate dplyr's across
# https://stackoverflow.com/q/63200530/7175713
# select_columns syntax can fit in nicely here
mtcars.mutate(("*t", "mean"), ("*p", "sum"), {"cyl": lambda df: df + 1, "new_col": lambda df: df.select_columns("*t").sum(axis=1))

[samukweku]

• for multiple columns, we use a tuple of three args (cols, func, names) - where cols is the cols we wish to select, func is a function or list/tuple of functions, while col names is how the col will be renamed, either flattened for a MultiIndex, or prefix/suffix added. For the names, we'll use an f-string format sort of. - the idea is borrowed from R's dplyr's across function.
• for single columns, we can pass that as a dictionary, or use pandas named agg for more control of the output renaming

[samukweku]

so far, tests i've conducted show that building a dictionary and passing it to pandas transform/apply/agg/assign deliver faster performance compared to what I came up with for mutate. maybe someone else comes up with a cleaner API, which is fast as well.