How can I separate this conversation into per row using python,

Lets say this string below is a content of a row with a "body" as the column name. Now I want to create a row per speaker out from this string.

Helper:
Hi, I'm Helper, Virtual Assistant, how can I help you today?
Are you inquiring about:
eBooks
Audiobooks
Purchasing
Subscriptions
Movies
etc

Cx said:
Movies

The expected output should be like:

I have tried this but the

Testresult = tempchatdf.body.str.split(":\*\*",expand = True)

You can just take the str and split(":").

split = string.split(":")
result = [split[0], ":".join(split[1:])]

This takes the first split and makes that the speaker (0th index) then combines the rest of the split with ":". This is done to ensure that any extra ":" comes back.

If the string contains several lines of these, you can just wrap it in a loop.

table = []
for line in string:
    split = string.split(":")
    table.append([split[0], ":".join(split[1:])])

Here is an approach using re.findall() to match the body string and creating a new df

row_str = df["body"].values[0]
data = re.findall(r'(.+?):\s*(.+)', row_str)

new_df = pd.DataFrame(data, columns=["Speaker", "Transcript"])
print(new_df)

Or you can use re with pandas.DataFrame.explode in a list comprehension

pattern = r'^([a-zA-Z\s]+):'
rows = [{"Speaker": re.match(pattern, line).group(1).strip(), 
         "Transcript": line.split(":", 1)[1].strip()}
         for line in df["body"].str.split("\n").explode().tolist()
         if re.match(pattern, line)]

new_df = pd.DataFrame(rows)
print(new_df)