Slice pandas dataframe column into multiple columns using substring

You can approach this by using pandas.Series.split :

df[["Information", "Price", "Place"]]= df.pop("Column B").str.split(r"\\n", expand=True)

df= df.astype(str).apply(lambda x: x.replace(x.name, "", regex=True).str.strip(" - "))

# Output :

print(df.to_string())

   Column A                            Information  Price       Place
0  Item_ID1  information for item that has ID as 1  $7.99  Albany, NY
1  Item_ID2        item's information with ID as 2  $5.99  Ottawa, ON

For a generic method in which you do not need to know the future columns in advance, you can use str.extractall and a pivot:

out = df.drop(columns='Column B').join(
 df['Column B']
 .str.extractall(r'([^-]+) - ([^\n]+)\n?')
 .droplevel('match')
 .pivot(columns=0, values=1)
)

NB. I am assuming that you have real newlines, if you have instead the two characters \ and n, you can convert with df['Column B'] = df['Column B'].str.replace(r'\\n', '\n')

Output:

   Column A                            Information       Place  Price
0  Item_ID1  information for item that has ID as 1  Albany, NY  $7.99
1  Item_ID2        item's information with ID as 2  Ottawa, ON  $5.99

Another possible solution, based on the following ideas:

1. Split Column B by \s-\s|\\n, using pandas.Series.str.split.

2. Reshape the result, using numpy.reshape.

3. Apply pandas.pivot_table.

(pd.concat([df['Column A'], pd.DataFrame(
    df['Column B'].str.split(r'\s-\s|\\n', expand=True, regex=True).values
    .reshape((-1,2)))
 .pivot_table(columns=0, values=1, aggfunc=list)
 .pipe(lambda d: d.explode(d.columns.tolist(), ignore_index=True))], axis=1))

Output:

   Column A                            Information       Place  Price
0  Item_ID1  information for item that has ID as 1  Albany, NY  $7.99
1  Item_ID2        item's information with ID as 2  Ottawa, ON  $5.99