Python pandas: dynamic concatenation from get_dummies

Question

having the following dataframe:

import pandas as pd

cars = ["BMV", "Mercedes", "Audi"]
customer = ["Juan", "Pepe", "Luis"]
price = [100, 200, 300]
year = [2022, 2021, 2020]


df_raw = pd.DataFrame(list(zip(cars, customer, price, year)),\
                      columns=["cars", "customer", "price", 'year'])

I need to do one-hot encoding from the categorical variables cars and customer, for this I use the get_dummies method for these two columns.

numerical = ["price", "year"]
df_final = pd.concat([df_raw[numerical], pd.get_dummies(df_raw.cars),\
                      pd.get_dummies(df_raw.customer)], axis=1)

Is there a way to generate these dummies in a dynamic way, like putting them in a list and loop through them with a for.In this case it may seem simple because I only have 2, but if I had 30 or 60 attributes, would I have to go one by one?

Answer 1

pd.get_dummies

pd.get_dummies(df_raw, columns=['cars', 'customer'])

---

   price  year  cars_Audi  cars_BMV  cars_Mercedes  customer_Juan  customer_Luis  customer_Pepe
0    100  2022          0         1              0              1              0              0
1    200  2021          0         0              1              0              0              1
2    300  2020          1         0              0              0              1              0

Answer 2

One simple way is to concatenate the columns and use str.get_dummies:

cols = ['cars', 'customer']
out = df_raw.join(df_raw[cols].agg('|'.join, axis=1).str.get_dummies())

output:

       cars customer  price  year  Audi  BMV  Juan  Luis  Mercedes  Pepe
0       BMV     Juan    100  2022     0    1     1     0         0     0
1  Mercedes     Pepe    200  2021     0    0     0     0         1     1
2      Audi     Luis    300  2020     1    0     0     1         0     0

Another option is to melt and use crosstab:

df2 = df_raw[cols].reset_index().melt('index')
out = df_raw.join(pd.crosstab(df2['index'], df2['value']))