CODEWITHSUNDEEP
pythonpandasdataframepandas-apply
aabujamra
aabujamra

Reputation: 4636

Apply function to create string with multiple columns as argument

I have a dataframe like this:

     name .  size . type    .  av_size_type
0    John .   23  . Qapra'  .            22
1     Dan .   21  . nuk'neH .            12
2  Monica .   12  . kahless .            15

I want to create a new column with a sentence, like this:

    name .  size . type    .  av_size_type  .   sentence
0    John .   23 . Qapra'  .            22  .   "John has size 23, above the average of Qapra' type (22)"
1     Dan .   21 . nuk'neH .            12  .   "Dan has size 21, above the average of nuk'neH type (21)"
2  Monica .   12 . kahless .            15  .   "Monica has size 12l, above the average of kahless type (12)

It would be something like this:

def func(x):
    string="{0} has size {1}, above the average of {2} type ({3})".format(x[0],x[1],x[2],x[3])
    return string

df['sentence']=df[['name','size','type','av_size_type']].apply(func)

However, apparently this sort of synthax doesn't work.

Would anyone have a suggestion for that?

Upvotes: 7

Views: 1860

Answers (3)

piRSquared
piRSquared

Reputation: 294488

Use a splat and unpack

string = lambda x: "{} has size {}, above the average of {} type ({})".format(*x)

df.assign(sentence=df.apply(string, 1))

     name  size     type  av_size_type                                           sentence
0    John    23   Qapra'            22  John has size 23, above the average of Qapra' ...
1     Dan    21  nuk'neH            12  Dan has size 21, above the average of nuk'neH ...
2  Monica    12  kahless            15  Monica has size 12, above the average of kahle...

If you want, you can use dictionary unpacking

string = lambda x: "{name} has size {size}, above the average of {type} type ({av_size_type})".format(**x)

df.assign(sentence=df.apply(string, 1))

     name  size     type  av_size_type                                           sentence
0    John    23   Qapra'            22  John has size 23, above the average of Qapra' ...
1     Dan    21  nuk'neH            12  Dan has size 21, above the average of nuk'neH ...
2  Monica    12  kahless            15  Monica has size 12, above the average of kahle...

Upvotes: 8

Allen Qin
Allen Qin

Reputation: 19957

You can use apply to build the sentence directly.

df['sentence'] = (
    df.apply(lambda x: "{} has size {}, above the average of {} type ({})"
                       .format(*x), axis=1)
)

If you would like to reference the columns explicitly, you can do:

df['sentence'] = (
    df.apply(lambda x: "{} has size {}, above the average of {} type ({})"
                       .format(x.name, x.size, x.type, x.av_size_type), axis=1)
)

Upvotes: 4

cs95
cs95

Reputation: 402844

Use a list comprehension as a fast alternative since you're forced to iterate:

string = "{0} has size {1}, above the average of {2} type ({3})"
df['sentence'] = [string.format(*r) for r in df.values.tolist()]


df

     name  size     type  av_size_type  \
0    John    23   Qapra'            22   
1     Dan    21  nuk'neH            12   
2  Monica    12  kahless            15   

                                            sentence  
0  John has size 23, above the average of Qapra' ...  
1  Dan has size 21, above the average of nuk'neH ...  
2  Monica has size 12, above the average of kahle...

Upvotes: 4

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