How to transform some columns only with SimpleImputer or equivalent

This is methode I use, you can replace low_cardinality_cols by cols you want to encode. But this works also justt set value unique to max(df.columns.nunique()).

#check cardinalité des cols a encoder
low_cardinality_cols = [cname for cname in df.columns if df[cname].nunique() < 16 and 
                        df[cname].dtype == "object"]

Why thes columns, it's recommanded, to encode only cols with cardinality near 10.

# Replace NaN, if not you'll stuck
from sklearn.impute import SimpleImputer
imp = SimpleImputer(missing_values=np.nan, strategy='most_frequent') # feel free to use others strategy
df[low_cardinality_cols]  = imp.fit_transform(df[low_cardinality_cols])

# Apply label encoder 
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
for col in low_cardinality_cols:
    df[col] = label_encoder.fit_transform(df[col])
    ```

Following Dan's advice, an example of using ColumnTransformer and SimpleImputer to backfill the columns is:

import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer

A = [[7,2,3],[4,np.nan,6],[10,5,np.nan]]

column_trans = ColumnTransformer(
[('imp_col1', SimpleImputer(strategy='mean'), [1]),
 ('imp_col2', SimpleImputer(strategy='constant', fill_value=29), [2])],
remainder='passthrough')

print(column_trans.fit_transform(A)[:, [2,0,1]])
# [[7 2.0 3]
#  [4 3.5 6]
#  [10 5.0 29]]

This approach helps with constructing pipelines which are more suitable for larger applications.

There is no need to use the SimpleImputer.
DataFrame.fillna() can do the work as well

• For the second column, use

  column.fillna(column.mean(), inplace=True)

• For the third column, use

  column.fillna(constant, inplace=True)

Of course, you will need to replace column with your DataFrame's column you want to change and constant with your desired constant.

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Edit
Since the use of inplace is discouraged and will be deprecated, the syntax should be

column = column.fillna(column.mean())