Python pandas: convert a column to a string?

Question

I have some columns:

0                                            ['XS', '2X']
1                                      ['A2', '2X', '8W']
2                                                  ['2X']
3                    ['A2', 'FR', 'XS', '8W', '8E', '2X']
4                    ['A5', 'FR', 'XS', 'QF', '2X', '23']
5                                      ['XS', '2X', 'MF']
6                                                    None

that I am trying to iterate over. It I loop over the column and get the type:

for item in data1['business_types']:
    print(type(item))

It shows that the types are of type str

<class 'str'>
<class 'str'>
<class 'str'>
<class 'str'>
<class 'str'>
<class 'str'>
<class 'NoneType'>

I would like to convert the objects in this column to a list object that I can iterate over.

Maybe l = data1['business_types'].tolist() and then l = [ast.literal_eval(i) for i in l] — user2918461
– user2918461, Commented Jun 7, 2017 at 13:04
I think your title should be convert column of strings to column of lists of strings — user2918461
– user2918461, Commented Jun 7, 2017 at 13:05

jezrael · Accepted Answer · 2017-06-07 13:34:43Z

2

I think you need ast.literal_eval with fillna for replace None to []:

data1['business_types'] = data1['business_types'].fillna('[]').apply(ast.literal_eval)
print (data1)
             business_types
0                  [XS, 2X]
1              [A2, 2X, 8W]
2                      [2X]
3  [A2, FR, XS, 8W, 8E, 2X]
4  [A5, FR, XS, QF, 2X, 23]
5              [XS, 2X, MF]
6                        []

If need None (NaNs):

def f(x):
    try:
        return ast.literal_eval(x)
    except ValueError:
        return x

import ast

data1['business_types'] = data1['business_types'].apply(f)
print (data1)
             business_types
0                  [XS, 2X]
1              [A2, 2X, 8W]
2                      [2X]
3  [A2, FR, XS, 8W, 8E, 2X]
4  [A5, FR, XS, QF, 2X, 23]
5              [XS, 2X, MF]
6                       NaN

for item in data1['business_types']:
    print(type(item))

<class 'list'>
<class 'list'>
<class 'list'>
<class 'list'>
<class 'list'>
<class 'list'>
<class 'float'>

edited Jun 7, 2017 at 13:34

answered Jun 7, 2017 at 13:02

jezrael

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6 Comments

spitfiredd Over a year ago

What version are you using? I actually had tried that and I get ValueError: malformed node or string: None under 0.20.2. I actually just updated from 0.19.2 and it's broke some of my code...

jezrael Over a year ago

I use pandas 0.20.1, but if bad data, then it can failed :(

jezrael Over a year ago

i see problem, get my some time

jezrael Over a year ago

Is possible replace None to empty string?

spitfiredd Over a year ago

This worked, I had tried a solution with ast.literal_eval function however, the trick with filling the Nonetypes with [] was clever and worked. Thanks.

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