Using index:

>>> string = "Username: How are you today?"
>>> string[:string.index(":")]
'Username'

The index will give you the position of : in string, then you can slice it.

If you want to use regex:

>>> import re
>>> re.match("(.*?):",string).group()
'Username'                       

match matches from the start of the string.

you can also use itertools.takewhile

>>> import itertools
>>> "".join(itertools.takewhile(lambda x: x!=":", string))
'Username'

You don't need regular expressions here. Just type

string = "My City | August 5"
string1, string2 = string.split("|")

If you want to crop the trailing space in the results, you can use

string1 = string1.strip(" ")
string2 = string2.strip(" ")

Difference between split and partition is split returns the list without delimiter and will split where ever it gets delimiter in string i.e.

x = 'http://test.com/lalala-134-431'

a,b,c = x.split(-)
print(a)
"http://test.com/lalala"
print(b)
"134"
print(c)
"431"

and partition will divide the string with only first delimiter and will only return 3 values in list

x = 'http://test.com/lalala-134-431'
a,b,c = x.partition('-')
print(a)
"http://test.com/lalala"
print(b)
"-"
print(c)
"134-431"

so as you want last value you can use rpartition it works in same way but it will find delimiter from end of string

x = 'http://test.com/lalala-134-431'
a,b,c = x.rpartition('-')
print(a)
"http://test.com/lalala-134"
print(b)
"-"
print(c)
"431"

Hope I got it right, but you could use a regex. For your example:

import re
r = re.compile('(.+)_(\d+\.jpg)')
m = r.match('Egyptian_Mau_214.jpg')
print(m.groups()) # -> ('Egyptian_Mau', '214.jpg')

Regex explanation:

1. (.+) - Group 1, 1 or more of any character.
2. _ - Just an underscore.
3. (\d+\.jpg) - Group 2, one or more digits, and .jpg suffix.

The idea is to:

• concatenate your 2 columns (inserting a '_' between them),
• call str.extract to extract the parts of interest, specified with a regex pattern with proper named capturing groups,
• for each row, merge these parts, adding the required number of zeroes.

To implement it, start with creating of an intermediate DataFrame:

df2 = (df.Strain + '_' + df.Region).str.extract(
    r'(?:[^_]+_){2}(?P<QL>[^_]+)_[^_]+_(?P<Rg>[^&]+)\D+(?P<D1>\d)\.(?P<D2>\d)')

The result, for your data, is:

     QL      Rg D1 D2
0  QL40  Region  1  1
1  QL40  Region  2  2
2  QL40  Region  2  1

Then define a merging function, to be applied for each row from df2:

def mrg(row):
    rg = row.Rg + '0'
    if len(rg) < 11:
        rg += '0'
    return row.QL + '_' + row.D1 + '.' + rg + row.D2

And to get the final result, run:

region_list = df2.apply(mrg, axis=1).tolist()

The result is:

['QL40_1.Region001', 'QL40_2.Region002', 'QL40_2.Region001']