分解两个不同 DataFrame 中的列

Question

I have two DataFrames and in each of them I have a categorical column col . I want to replace all the categories with numbers, so I decided to do it this fashion:

df1['col'] = pd.factorize(df1['col'])[0]

Now the question is how can I code df2[col] in the same way? And how can I also code categories that are present in df2[col] but not in df1[col] ?

Answer 1

You need a LabelEncoder

from sklearn.preprocessing import LabelEncoder

enc = LabelEncoder()
df1['col'] = enc.fit_transform(df1['col'])
df2['col'] = enc.transform(df2['col'])

---

for unseen label, this may be a solution:

enc = LabelEncoder()
enc.fit(df1['col'])
diz_map = dict(zip(enc.classes_, enc.transform(enc.classes_)+1))

for i in set(df2['col']).difference(df1['col']):
    diz_map[i] = 0

df1['col'] = [diz_map[i] for i in df1['col'].values]
df2['col'] = [diz_map[i] for i in df2['col'].values]

you map as 0 all the unseen values in df2['col']