Can I consolidate identical terms into a single column in a pandas dataframe?

Within this extensive pandas dataframe, there are several terms:

type    name    exp
-------------------
feline  tiger  True
feline  cat    False
rodent  rabbit True
canine  dog    False
feline  puma   True
feline  bobcat False

Are there ways to consolidate all terms in the name column with matching types in the type column into a single cell? For instance:

type    name                  exp
----------------------------------
feline  tiger cat puma bobcat True
rodent  rabbit                True
canine  dog                   False
pythonpython-3.xpandasdata-structures

Answer №1

Utilizing the df.groupby method:

In [201]: df_grouped = df.groupby('type', sort=False, as_index=False)

Initial step, dealing with the name column:

In [204]: df_grouped['name'].apply(lambda x: ' '.join(x))
Out[204]: 
0    tiger cat puma bobcat
1                   rabbit
2                      dog
dtype: object

Next, addressing the exp column:

In [205]: df_grouped['exp'].apply(any)
Out[205]: 
0     True
1     True
2    False
dtype: bool

Putting all of it together:

In [220]: df_grouped = df.groupby('type', sort=False, as_index=False).agg({'name' : ' '.join, 'exp' : any}); df_grouped
Out[220]: 
     type                   name    exp
0  feline  tiger cat puma bobcat   True
1  rodent                 rabbit   True
2  canine                    dog  False

To ensure uniqueness, apply a lambda function to the name column:

df.groupby('type', sort=False, as_index=False)\
       .agg({'name' : lambda x: ' '.join(set(x)), 'exp' : any})

Answer №2

Try this approach.

In [797]: df.group('category', as_index=False).combine({'brand': ' '.unite, 'price': 'max'})
Out[797]:
     category                   brand    price
0  electronic                    Sony   800
1      clothing  Nike Adidas Reebok Puma   300
2        food                 McDonalds   15

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