How to split a column based on several string indices using pandas

Question

I would like to split each row into new columns based on several indices:

6ABCDE0218594STRING

to

6 ABCDE 021 8594 STRING

This seems like it'd have been asked at least once before, but I keep finding only variations on the question (separating by a delimiter as in pandas: How do I split text in a column into multiple rows? , separating into new rows using rather than new columns, again with a delimiter: Split pandas dataframe string entry to separate rows ).

I apologize in advance if this is a duplicate!

Answer 1

One way is to use a regex and str.extract to pull out the columns:

In [11]: df = pd.DataFrame([['6ABCDE0218594STRING']])

You could just do it with index, so something like this:

In [12]: df[0].str.extract('(.)(.{5})(.{3})(.{4})(.*)')
Out[12]:
   0      1    2     3       4
0  6  ABCDE  021  8594  STRING

Or you could be a bit more cautious and ensure each column is the correct form:

In [13]: df[0].str.extract('(\d)(.{5})(\d{3})(\d{4})(.*)')
Out[13]:
   0      1    2     3       4
0  6  ABCDE  021  8594  STRING

Note: You can also use named groups (see the docs ).

Answer 2

Try this:

string = '6ABCDE0218594STRING'
indices = [1,5,3,4]
myList = []

for index in indices:
    token, string = string[:index],string[index:]
    myList.append(token)

myList.append(string)

>>> Output: ['6', 'ABCDE', '021', '8594', 'STRING']

Answer 3

Or in case you don't know the number of digits, letters etc.:

import re

m = re.match('(\d*)([A-Z]*)(\d*)([A-Z]*)', '6ABCDE0218594STRING').groups()
print m[0], m[1], m[2], m[3]

Output:

6 ABCDE 0218594 STRING