迭代pandas数据帧中的所有列以拆分分隔符

Question

I have a dataframe that looks like this:

    name   val
0   cat    ['Furry: yes', 'Fast: yes', 'Slimy: no', 'Living: yes']
1   dog    ['Furry: yes', 'Fast: yes', 'Slimy: no', 'Living: yes']
2   snail  ['Furry: no', 'Fast: no', 'Slimy: yes', 'Living: yes']
3   paper  ['Furry: no', 'Fast: no', 'Slimy: no', 'Living: no']

For each item in list in the val column, I want to split the item on the ':' delimiter. Then I want to make item[0] be the column name, and item[1] be the value for that specific column. Like so:

    name   Furry  Fast  Slimy  Living
0   cat    yes    yes   no     yes
1   dog    yes    yes   no     yes
2   snail  no     no    yes    yes
3   paper  no     no    no     no

I've tried using apply(pd.Series) to the val column, but that still leaves me with many columns that I'd have to either manually do splits on, or figure out how to iteratively go through all the columns and do splits. I prefer to split from ground zero and create the column names. Any idea how I can achieve this?

Answer 1

pd.DataFrame accepts a list of dictionaries directly. Therefore, you can construct a dataframe via a list comprehension and then join.

L = [dict(i.split(': ') for i in x) for x in df['val']]

df = df[['name']].join(pd.DataFrame(L))

print(df)

    name Fast Furry Living Slimy
0    cat  yes   yes    yes    no
1    dog  yes   yes    yes    no
2  snail   no    no    yes   yes
3  paper   no    no     no    no

Answer 2

apply with split to create dictionary:

df.val = df.val.apply(lambda x: dict([i.split(': ') for i in x]))

apply with pd.Series to create columns:

df.join(df.val.apply(pd.Series)).drop('val', 1)

    name Furry  Fast Slimy Living
0    cat   yes   yes    no    yes
1    dog   yes   yes    no    yes
2  snail    no    no   yes    yes
3  paper    no    no    no     no