Python: How to One Hot Encode a Feature with multiple values?

I have the following dataframe df with names of the travelling cities in route column of an aircraft with it’s ticket_price.

I want to obtain individual city names from route and one hot encode them.

Dataframe (df)

id|         route               | ticket_price
1 | Mumbai - Pune - Bangalore   |   10000
2 | Pune - Delhi                |    7000
3 | Delhi - Pune                |    6500

Required Dataframe (df_encoded)

id | route_mumbai | route_pune | route_bangalore | route_delhi | ticket_price
1  |      1       |      1     |      1          |     0       |   10000
2  |      0       |      1     |      0          |     1       |    7000
3  |      0       |      1     |      0          |     1       |    6500

Code
I have performed some preprocessing on the route column using the following code but am unable to understand how to one hot encode it.

def location_preprocessing(text):

  """
  Function to Preprocess the features having location names.
  """

  text = text.replace(" ", "")    # Remove whitespaces
  text = text.split("|")          # Obtain individual cities

  lst_text = [x.lower() for x in text]    # Lowercase city names

  text = " ".join(lst_text)               # Convert to string from list

  return text

df['route'] = df['route'].apply(lambda x: location_preprocessing(x))

If I apply one hot encoding directly using the below code then all routes are considered unique and are one hot encoded individually which is not desired. I want individual cities to be one hot encoded not the route.

df = pd.get_dummies(df, columns = ['route'])    # One-hot Encoding `route`

How to obtain the required dataframe?

Answer

If you have dataframe:

   id                      route  ticket_price
0   1  Mumbai - Pune - Bangalore         10000
1   2               Pune - Delhi          7000
2   3               Delhi - Pune          6500

Then:

df.route = df.route.str.split(" - ")
df_out = pd.concat(
    [
        df.explode("route")
        .pivot_table(index="id", columns="route", aggfunc="size", fill_value=0)
        .add_prefix("Route_"),
        df.set_index("id").ticket_price,
    ],
    axis=1,
)
print(df_out)

Prints:

    Route_Bangalore  Route_Delhi  Route_Mumbai  Route_Pune  ticket_price
id                                                                      
1                 1            0             1           1         10000
2                 0            1             0           1          7000
3                 0            1             0           1          6500