How to apply two different functions to one column if meets the condition?

Question

I need to apply 2 different functions to 1 columns if it meets a condition. My dataframe looks like this. I want to apply the functions to the column produce: veg_pro if the category is a vegetable and fruit_pro if it’s a fruit.

Produce            Category
apple is good      fruit
corn is bad        vegetable
beans is good      vegetable
grape if good      fruit

My functions look like this:

def veg_pro(text):
    reg_tokenizer = RegexpTokenizer('s+', gaps = True)
    terms = reg_tokenizer.tokenize(text) 
    return terms

def fruit_pro(text):
    reg_tokenizer = RegexpTokenizer(“[w+.]+“)
    terms = reg_tokenizer.tokenize(text) 
    return terms

 df['produce']= df['produce'].apply(lambda x: 
 veg_pro(x) if df['Category'] =='vegetable’ else 
 fruit_pro(x))
    
 ValueError: The truth value of a Series is ambiguous. Use 
 a.empty, a.bool(), a.item(), a.any() or a.all().

Asked By: CPDatascience

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Source

Answer 1

instead of using apply on one column use it on the dataframe like this:

 df['produce']= df.apply(lambda x: 
 veg_pro(x["produce"]) if x["Category"] =="vegetable" else fruit_pro(x["produce"]),axis=1)

Answered By: to_data

How to apply two different functions to one column if meets the condition?

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