Is there a way to create a new column for values that are in the original column either using Excel or Python?

I have the below Pandas Series with values that look like this:

There are almost 1000 rows with this data so the problem I am having is:

Question

Can I run a code in python or Excel to extract the values from these rows in such a way that it would place ID#s on a separate column, Location in a another column …etc

So it would look something like this:

Some items wont have a Name and in that case, could it just leave it blank? or write No name found?

I tried creating separate lists (this data came from a text file) but that method is not working for me, and I do not have any code to share at the moment.so I copy pasted all the values into an excel sheet.

Note (I do not care about the second Address line, so if it is easier to ignore that is fine).

>Solution :

Based on the specific series and for the sake of easiness I suggest you can try:

df['Index'] = df['Info'].replace("# ","",regex=True).str.split().str[0]
df['Values'] = [' '.join(x) for x in df['Info'].replace("# ","",regex=True).str.split().str[1:]]
output = df.set_index('Index').drop(columns='Info').T

Returning:

Index          ID Location:           Address:   Name: Address:
Values  3.22.33.2     Texas  1321 madeupstreet  mike b    6.3.1