How To Use Multiple Separators In A Pandas Series And Split Into Multiple Rows

I have a dataframe like this. df = pd.DataFrame({ 'Name' : ['ABC LLC Ram corp', 'IJK Inc'], 'id' : [101, 102] }) Name id 0 ABC LLC Ram corp 101 1

Solution 1:

You can use str.findall to find all the occurrence of matching regex pattern in column Name, then assign these matching occurrences to the column Name and explode the dataframe on Name:

pat = fr"(?i)(.*?(?:{'|'.join(separators)}))"
df.assign(Name=df['Name'].str.findall(pat)).explode('Name')

Regex details:

• (?i) : Case insensitive flag
• ( : Start of capturing group
• .*? : Matches any character except line terminators between zero and unlimited times, as few times as possible (lazy match).
• (?: : start of a non capturing group
• {'|'.join(separators)}: f-string expression which evaluates to inc|corp|llc
• ) : End of non-capturing group
• ) : End of capturing group

---

        Name   id
0    ABC LLC  101
0   Ram corp  101
1    IJK Inc  102

Solution 2:

A bit verbose approach , by replacing the spaces after the words with comma and then split:

d = dict(zip([f'{i} 'for i in separators],[f'{i},'for i in separators]))
#{'inc ': 'inc,', 'corp ': 'corp,', 'llc ': 'llc,'}

out = (df.assign(Name=df['Name'].str.lower()
       .replace(d,regex=True).str.title().str.split(",")).explode("Name"))

---

print(out)

       Name   id
0   Abc Llc  101
0  Ram Corp  101
1   Ijk Inc  102