[BUG]: Chp2 - ValueError

[Cryptomamy]

Hi Aurélien,

Describe the bug

Chapter 2, page 75.

When I run a piece of code word for word from the book, I obtain an error.

The code

df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown), 
                         columns=cat_encoder.get_feature_names_out(), 
                         index=df_test_unknown.index)

And if you got an exception, please copy the full stacktrace here:

Output exceeds the size limit. Open the full output data in a text editor
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[183], line 1
----> 1 df_output = pd.DataFrame(cat_encoder.transform(df_test_unknown), 
      2                          columns=cat_encoder.get_feature_names_out(), 
      3                          index=df_test_unknown.index)

File [~/ml/studysession/lib/python3.10/site-packages/pandas/core/frame.py:762](https://file+.vscode-resource.vscode-cdn.net/Users/[user]/ml/~/ml/studysession/lib/python3.10/site-packages/pandas/core/frame.py:762), in DataFrame.__init__(self, data, index, columns, dtype, copy)
    754         mgr = arrays_to_mgr(
    755             arrays,
    756             columns,
   (...)
    759             typ=manager,
    760         )
    761     else:
--> 762         mgr = ndarray_to_mgr(
    763             data,
    764             index,
    765             columns,
    766             dtype=dtype,
    767             copy=copy,
    768             typ=manager,
    769         )
    770 else:
    771     mgr = dict_to_mgr(
...
    418 passed = values.shape
    419 implied = (len(index), len(columns))
--> 420 raise ValueError(f"Shape of passed values is {passed}, indices imply {implied}")

ValueError: Shape of passed values is (2, 1), indices imply (2, 5)

Expected behavior

I am supposed to receive a DataFrame

Screenshots

Versions (please complete the following information):

• OS: MacOSX 12.6
• Python: 3.10
• Scikit-Learn: 1.2.1

Thank you for your time