pandas - convert string into list of strings

Question

I have this \'file.csv\' file to read with pandas:

Title|Tags
T1|\"[Tag1,Tag2]\"
T1|\"[Tag1,Tag2,Tag3]\"
T2|\"[Tag3,Tag1]\"

using

Answer 1

Your df['Tags'] appears to be a list of strings. If you print that list you should get ["[tag1,tag2]","[Tag1,Tag2,Tag3]","[Tag3,Tag1]"] this is why when you call the first element of the first element you're actually getting the first single character of the string, rather than what you want.

You either need to parse that string afterward. Performing something like

df['Tags'][0] = df['Tags'][0].split(',')

But as you saw in your cited example this will give you a list that looks like

in: df['Tags'][0][0] 
out: '[tag1'`

What you need is a way to parse the string editing out multiple characters. You can use a simple regex expression to do this. Something like:

 import re
 df['Tags'][0] = re.findall(r"[\w']+", df['Tags'][0])
 print(df['Tags'][0][0])

will print:

 'tag1'

Using the other answer involving Pandas converters you might write a converter like this:

 def clean(seq_string):
      return re.findall(r"[\w']+", seq_string)

If you don't know regex, they can be quite powerful, but also unpredictable if you're not sure on the content of your input strings. The expression used here r"[\w']+" will match any common word character alpha-numeric and underscores and treat everything else as a point for re.findall to split the list at.

Answer 2

You can split the string manually:

>>> df['Tags'] = df.Tags.apply(lambda x: x[1:-1].split(','))
>>> df.Tags[0]
['Tag1', 'Tag2']

Answer 3

You can convert the string to a list using strip and split.

df_out = df.assign(Tags=df.Tags.str.strip('[]').str.split(','))

df_out.Tags[0][0]

Output:

'Tag1'

Answer 4

Here's a simple yet performant operation:

df['Tags'].str.split(',')

Answer 5

Or

df.Tags=df.Tags.str[1:-1].str.split(',').tolist()

Answer 6

I think you could use the json module.

import json
import pandas

df = pd.read_csv('file.csv', sep='|')
df['Tags'] = df['Tags'].apply(lambda x: json.loads(x))

So this will load your dataframe as before, then apply a lambda function to each of the items in the Tags column. The lambda function calls json.loads() which converts the string representation of the list to an actual list.