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Using pandas to skip colums and rows
published 14 Sep 2020, 09:25

Hi guys, i am trying to iterate columns on my csv file before training the dataset .Can anyone help out with how to use iteration or any other method to skip columns

I don't get your point well. Do you mean having the name of columns in a list which can probably be used in for loop?

df.columns would solve that

Or you mean selecting or dropping some particular columns before fitting it into a model

Yes exactly , dropping some columns before calling the fit on the dataset.

to_drop = ['form_field1', 'form_field10', 'form_field50', 'default_status'] #a list of columns you want to drop

X = df.drop(to_drop, axis=1) #the axis=1 specify that it should delete columns not row. so very important

If you don't want a new df and want to continue with normal df, add inplace=True e.g

df.drop(to_drop, axis=1, inplace=True)

Goodluck

You could use some list index slicing or just do a counter like df.columns which returns a list

for i in df.columns:

If condition is true:

Do some operation on the columns

I will try this now. Thanks