coding can be implemented in all four of the above methods. What"s the difference between them? Of course, the length of the script is different. I can already see it.

< H1 > get_dummies method < / H1 >

df7 = DataFrame({"key":list("bbacab"),
                "data1":range(6)})

dummies = pd.get_dummies(df7.key,prefix = "key")

dummies
dummies
0    0    1    0
1    0    1    0
2    1    0    0
3    0    0    1
4    1    0    0
5    0    1    0

< H1 > LabelEncoder plus get_dummies method < / H1 >

this method is mainly get_dummies

.

le = LabelEncoder()
-sharp le.fit(df7["key"])
df7["key2"] = le.fit_transform(df7["key"])

pd.get_dummies(df7.key2)
0    0    1    0
1    0    1    0
2    1    0    0
3    0    0    1
4    1    0    0
5    0    1    0

< H1 > label_binarize method < / H1 >

lab = label_binarize(df7["key"],classes = ["a","b","c"])
lab
array([[0, 1, 0],
       [0, 1, 0],
       [1, 0, 0],
       [0, 0, 1],
       [1, 0, 0],
       [0, 1, 0]])

columns = 
df7.join(pd.DataFrame(lab)).rename(columns = {0:"key_a",1:"key_b",2:"key_c"})

0    0    b    1    0    1    0
1    1    b    1    0    1    0
2    2    a    0    1    0    0
3    3    c    2    0    0    1
4    4    a    0    1    0    0
5    5    b    1    0    1    0

< H1 > OneHotEncoder < / H1 >

onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = df7["key2"].values.reshape(len(df7["key2"]), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
onehot_encoded 
-sharp arrylabel_binarize
array([[ 0.,  1.,  0.],
       [ 0.,  1.,  0.],
       [ 1.,  0.,  0.],
       [ 0.,  0.,  1.],
       [ 1.,  0.,  0.],
       [ 0.,  1.,  0.]])

pd.DataFrame(onehot_encoded)
-sharp join
0    0.0    1.0    0.0
1    0.0    1.0    0.0
2    1.0    0.0    0.0
3    0.0    0.0    1.0
4    1.0    0.0    0.0
5    0.0    1.0    0.0