Binary-vectorize pandas DataFrame column

Question

In a fictional patients dataset one might encounter the following table:

pd.DataFrame({
    "Patients": ["Luke", "Nigel", "Sarah"],
    "Disease": ["Cooties", "Dragon Pox", "Greycale & Cooties"]
})

Which renders the following dataset:

Now, assuming that the rows with multiple illnesses use the same pattern (separation with a character, in this context a &) and that there exists a complete list diseases of the illnesses, I've yet to find a simple solution to applying to these situations pandas.get_dummies one-hot encoder to obtain a binary vector for each patient.

How can I obtain, in the simplest possible manner, the following binary vectorization from the initial DataFrame?

pd.DataFrame({
    "Patients": ["Luke", "Nigel", "Sarah"],
    "Cooties":[1, 0, 1],
    "Dragon Pox":[0, 1, 0],
    "Greyscale":[0, 0, 1]
})

Answer 1

You can use Series.str.get_dummies with right separator,

df.set_index('Patients')['Disease'].str.get_dummies(' & ').reset_index()

    Patients    Cooties Dragon Pox  Greycale
0   Luke        1       0           0
1   Nigel       0       1           0
2   Sarah       1       0           1

Answer 2

We can unnest your string to rows using this function.

After that we use pivot_table with aggfunc=len:

df = explode_str(df, 'Disease', ' & ')

print(df)
  Patients     Disease
0     Luke     Cooties
1    Nigel  Dragon Pox
2    Sarah    Greycale
2    Sarah     Cooties

---

df.pivot_table(index='Patients', columns='Disease', aggfunc=len)\
  .fillna(0).reset_index()

Disease Patients  Cooties  Dragon Pox  Greycale
0           Luke      1.0         0.0       0.0
1          Nigel      0.0         1.0       0.0
2          Sarah      1.0         0.0       1.0

---

Function used from linked answer:

def explode_str(df, col, sep):
    s = df[col]
    i = np.arange(len(s)).repeat(s.str.count(sep) + 1)
    return df.iloc[i].assign(**{col: sep.join(s).split(sep)})