Hi, When im working with some big dataframes and I need to create some columns based on functions. So i have some code like this
Def function(row): function
And then I run the function on the df as
df[‘new column’] = df.apply(function, axis=1)
But I do this with 10 or more columns/functions at time. I don’t think this is efficient because each time a column is created it had to parce the entire data frame. There’s a way to create all the columns at the same time while parsing the rows only once?
Thanks for any help.
Whatever you do, usually as long as the data frame fits in memory it should be pretty fast. Depending on functions you’re using applymap on splices of columns might be faster but code readability will suffer.
How big is your dataset? If it’s huge or your need are complex you’ll get way more performance by switching from Pandas to Polars dataframes rather than trying to optimize Pandas operations.
6M rows (it grows by 35K rows at month aprox), 6 columns, after the function it’s go to 17 columns and then finally to 9 where I starts to processes. It currently took 8min the pd.read_cvs() and 20min the creation of the columns. I would like to reduce that 20 min process.