Can we apply pandas.map() where the function mapping takes more than 1 argument?

Question

Suppose I have a dataframe containing a column of probability. Now I create a map function which returns 1 if the probability is >a threshold value other wise returns 0. Now the catch is that I want to specify the threshold by giving it as an argument to function, and then mapping it on the pandas dataframe.

Take the code example below:

def partition(x,threshold):
    if x<threshold:
        return 0
    else:
        return 1

df=pd.DataFrame({'probability':[0.2,0.8,0.4,0.95]})
df2=df.map(partition) #how would this line work - is my doubt to be exact

I.e. how do I pass the threshold value inside my map function now?

score 2 · Accepted Answer · answered Jul 01 '20 at 17:18

2

We can use Dataframe.applymap

df2 = df.applymap(lambda x: partition(x, threshold=0.5))

Or if only one column:

df['probability']=df['probability'].apply(lambda x: partition(x, threshold=0.5))

but it is not neccesary here. You can do:

df2 = df.ge(threshold).astype(int)

I recommend you see it

answered Jul 01 '20 at 17:18

ansev

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Wow, the third one worked like a charm. Thanks a lot. – Rishabh Rao Jul 01 '20 at 17:24

score 0 · Answer 2 · answered Jul 01 '20 at 17:15

0

You can use lambda for that purpose:

def partition(x,threshold):
    if x<threshold:
        return 0
    else:
        return 1

df=pd.DataFrame({'probability':[0.2,0.8,0.4,0.95]})
df['probability']=df['probability'].map(lambda x: partition(x, threshold=0.5))

answered Jul 01 '20 at 17:15

Grzegorz Skibinski

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he has a Dataframe – ansev Jul 01 '20 at 17:20
True, and from the example it does seem like taking the whole data frame is not the way to go @ansev – Grzegorz Skibinski Jul 01 '20 at 17:32

Can we apply pandas.map() where the function mapping takes more than 1 argument?

2 Answers2