7
how to optimize this kind of process (lemmy.eco.br)
submitted 11 months ago by driving_crooner@lemmy.eco.br to c/python@programming.dev
5 comments fedilink hide all child comments

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.

you are viewing a single comment's thread
view the rest of the comments
[-] driving_crooner@lemmy.eco.br 2 points 11 months ago

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.

this post was submitted on 11 Dec 2023
7 points (88.9% liked)

Python

6347 readers
1 users here now

Welcome to the Python community on the programming.dev Lemmy instance!

📅 Events

PastNovember 2023

October 2023

July 2023

August 2023

September 2023

🐍 Python project:
💓 Python Community:
✨ Python Ecosystem:
🌌 Fediverse
Communities
Projects
Feeds

founded 1 year ago
MODERATORS
erlingur@programming.dev
jnovinger@programming.dev
norambna@programming.dev