As quickly sketched in the comment there are a couple of considerations to be done on your example:

• method .fit_transform() generally returns either a sparse matrix or a numpy array. Returning a sparse matrix serves the purpose of saving memory; think to the example where you one-hot-encode a categorical attribute with many categories. You'll end up having a matrix with many columns and a single non-zero entry per row; with a sparse matrix you can store the location of the non-zero element only. In these situation you can call .toarray() on the output of .fit_transform() to get a numpy array back to be passed to the pd.DataFrame constructor.

  Actually, on a five-rows dataset similar to the one you provided

  df = pd.DataFrame({
      'department': ['operations', 'operations', 'support', 'logistics', 'sales'],
      'review': [0.577569, 0.751900, 0.722548, 0.675158, 0.676203],
      'projects': [3, 3, 3, 4, 3],
      'salary': ['low', 'medium', 'medium', 'low', 'high'],
      'satisfaction': [0.626759, 0.751900, 0.722548, 0.675158, 0.676203],
      'bonus': [0, 0, 0, 0, 1],
      'avg_hrs_month': [180.866070, 182.708149, 184.416084, 188.707545, 179.821083],
      'left': [0, 0, 1, 0, 0]
  })
  
  ord_features = ["salary"]
  ordinal_transformer = OrdinalEncoder()
  
  cat_features = ["department"]
  categorical_transformer = OneHotEncoder(handle_unknown="ignore")
  
  ct = ColumnTransformer(transformers=[
      ("ord", ordinal_transformer, ord_features),
      ("cat", categorical_transformer, cat_features),
  ])
  

  I can't reproduce your issue (namely, I directly obtain a numpy array), but basically pd.DataFrame(ct.fit_transform(df).toarray()) should work for your case. This is the output you would get:

• As you can see, with respect to your expected output, this only contains the transformed (ordinally encoded) salary column as first column and the transformed (one-hot-encoded) department column from the second to the last column. That's because, as you can see within the docs, parameter remainder is set to 'drop' by default, which implies that all columns which are not subject to transformation are dropped. To avoid this, you should set it to 'passthrough'; this will help you to transform the columns you need and keep the other untouched.

  ct = ColumnTransformer(transformers=[
      ("ord", ordinal_transformer, ord_features),
      ("cat", categorical_transformer, cat_features )],
      remainder='passthrough'
  )
  

  This would be the output of your pd.DataFrame(ct.fit_transform(df).toarray()) in such a case:

  

• Again, as you can see also column order is not the one you would expect after the transformation. Long story short, that's because in a ColumnTransformer

The order of the columns in the transformed feature matrix follows the order of how the columns are specified in the transformers list. Columns of the original feature matrix that are not specified are dropped from the resulting transformed feature matrix, unless specified in the passthrough keyword. Those columns specified with passthrough are added at the right to the output of the transformers.

I would aggest reading Preserve column order after applying sklearn.compose.ColumnTransformer at this proposal.

• Eventually, for what concerns column names you should probably apply a custom solution passing what you want directly to the columns parameter to be passed to the pd.DataFrame constructor. Indeed, OrdinalEncoder (differently from OneHotEncoder) does not provide a .get_feature_names_out() method that makes it generally easy to pass columns=ct.get_feature_names_out() to the pd.DataFrame constructor. See ColumnTransformer & Pipeline with OHE - Is the OHE encoded field retained or removed after ct is performed? for an example of its usage.

Update 10/2022 - sklearn version 1.2.dev0

With sklearn version 1.2.0 it will be possible to solve the problem of returning a DataFrame when transforming a ColumnTransformer instance much more easily. Such version has not been released yet, but you can test the following in dev (version 1.2.dev0), by installing the nightly builds as such:

pip install --pre --extra-index https://pypi.anaconda.org/scipy-wheels-nightly/simple scikit-learn -U

The ColumnTransformer (and other transformers as well) now exposes a .set_output() method which gives the possibility to configure a transformer to output pandas DataFrames, by passing parameter transform='pandas' to it.

Therefore, the example becomes:

import pandas as pd
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import RandomForestClassifier

df = pd.DataFrame({
    'department': ['operations', 'operations', 'support', 'logistics', 'sales'],
    'review': [0.577569, 0.751900, 0.722548, 0.675158, 0.676203],
    'projects': [3, 3, 3, 4, 3],
    'salary': ['low', 'medium', 'medium', 'low', 'high'],
    'satisfaction': [0.626759, 0.751900, 0.722548, 0.675158, 0.676203],
    'bonus': [0, 0, 0, 0, 1],
    'avg_hrs_month': [180.866070, 182.708149, 184.416084, 188.707545, 179.821083],
    'left': [0, 0, 1, 0, 0]
})

ord_features = ["salary"]
ordinal_transformer = OrdinalEncoder()

cat_features = ["department"]
categorical_transformer = OneHotEncoder(sparse_output=False, handle_unknown="ignore")

ct = ColumnTransformer(transformers=[
    ("ord", ordinal_transformer, ord_features),
    ("cat", categorical_transformer, cat_features )],
    remainder='passthrough'
)

ct.set_output('pandas')
df_pandas = ct.fit_transform(df)
df_pandas

The output also becomes much easier to read as it has proper column names (indeed, at each step, the transformers of which ColumnTransformer is made of do have the attribute feature_names_in_; so you don't lose column names anymore while transforming the input).

Last note. Observe that the example now requires parameter sparse_output=False to be passed to the OneHotEncoder instance in order to work.

Since sklearn Version 1.2, set_output can be configured per estimator by calling the set_output method or globally by setting set_config(transform_output="pandas")

See Release Highlights for scikit-learn 1.2 - Pandas output with set_output API

Example for set_output():

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().set_output(transform="pandas")

Example for set_config():

from sklearn import set_config
set_config(transform_output="pandas")

FuntionTransformer is quite of a flexible beast and by fixing validate=False (that is the default), the input data will not be validated and thus passed to the function directly.

Elsewhere in scikit-learn, input validation always happens and pandas dataframes are always converted into NumPy array because scikit-learn is going to make some numerical operations. This is the reason why ColumnTransformer output a NumPy array or a sparse matrix.

In the future, we will try to improve this part of the user experience by providing feature_names at different steps of a pipeline and maybe at some point, provide other types than NumPy array at the intermediate stages of the preprocessing.

One way to do it would be using a dummy transformer that just returns the transformed column with its original value:

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import PowerTransformer    

np.random.seed(1714)

class NoTransformer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self

    def transform(self, X):
        assert isinstance(X, pd.DataFrame)
        return X

I'm adding an id column to the dataset so I can show the use of the remainder parameter in ColumnTransformer(), which I find very useful.

df = pd.DataFrame(np.hstack((np.arange(10).reshape((10, 1)),
                             np.random.randint(1,100,size=(10, 3)))),
                  columns=["id"] + list('rfm'))

Using remainder with the value passthrough (by default the value is drop) one can retain the columns that are not transformed; from the docs.

And using the NoTransformer() dummy class we can transform the columns 'r', 'f', 'm' to have the same value.

column_trans = ColumnTransformer(
    [('r_original', NoTransformer(), ['r']),
     ('f_original', NoTransformer(), ['f']),
     ('m_original', NoTransformer(), ['m']),
     ('r_std', StandardScaler(), ['r']),
     ('f_std', StandardScaler(), ['f']),
     ('m_std', StandardScaler(), ['m']),
     ('r_boxcox', PowerTransformer(method='box-cox'), ['r']),
     ('f_boxcox', PowerTransformer(method='box-cox'), ['f']),
     ('m_boxcox', PowerTransformer(method='box-cox'), ['m']),
    ], remainder="passthrough")

A tip if you want to transform many more columns: the fitted ColumnTransformer() class (column_trans in your case) has a transformers_ method that lets you access the names ['r_std', 'f_std', 'm_std', 'r_boxcox', 'f_boxcox', 'm_boxcox'] programmatically:

column_trans.transformers_

#[('r_original', NoTransformer(), ['r']),
# ('f_original', NoTransformer(), ['f']),
# ('m_original', NoTransformer(), ['m']),
# ('r_std', StandardScaler(copy=True, with_mean=True, with_std=True), ['r']),
# ('f_std', StandardScaler(copy=True, with_mean=True, with_std=True), ['f']),
# ('m_std', StandardScaler(copy=True, with_mean=True, with_std=True), ['m']),
# ('r_boxcox',
#  PowerTransformer(copy=True, method='box-cox', standardize=True),
#  ['r']),
# ('f_boxcox',
#  PowerTransformer(copy=True, method='box-cox', standardize=True),
#  ['f']),
# ('m_boxcox',
#  PowerTransformer(copy=True, method='box-cox', standardize=True),
#  ['m']),
# ('remainder', 'passthrough', [0])]

Finally, I think your code could be simplified like this:

column_trans_2 = ColumnTransformer(
    ([
     ('original', NoTransformer(), ['r', 'f', 'm']),
     ('std', StandardScaler(), ['r', 'f', 'm']),
     ('boxcox', PowerTransformer(method='box-cox'), ['r', 'f', 'm']),
    ]), remainder="passthrough")

transformed_2 = column_trans_2.fit_transform(df)
column_trans_2.transformers_

#[('std',
#  StandardScaler(copy=True, with_mean=True, with_std=True),
#  ['r', 'f', 'm']),
# ('boxcox',
#  PowerTransformer(copy=True, method='box-cox', standardize=True),
#  ['r', 'f', 'm'])]

And assign the column names programmatically through transformers_:

new_col_names = []
for i in range(len(column_trans_2.transformers)):
    new_col_names += [column_trans_2.transformers[i][0] + '_' + s for s in column_trans_2.transformers[i][2]]
# The non-transformed columns ('id' in this case) will be appended on the right of
# the array and do not show up in the 'transformers_' method.
# Add the id columns to the col_names manually
new_col_names += ['id']

# ['original_r', 'original_f', 'original_m', 'std_r', 'std_f', 'std_m', 'boxcox_r',
#  'boxcox_f', 'boxcox_m', 'id']


pd.DataFrame(transformed_2, columns=new_col_names)