StandardScaler fit_transform() does not work with list as input data when output is configured to 'pandas'

### Describe the bug

I have a `StandardScaler` configured to output pandas dataframes using the `set_output` api. When `fit_transform` is called with data of type `list`, it throws an error as shown below.

I found that this issue does not occur when the line `my_stand_scale.set_output(transform='pandas')` in the MWE below is commented out.

On further digging and looking at the traceback, this bug occurs because the `index` argument in the line `return pd.DataFrame(data_to_wrap, index=index, columns=columns, copy=False)` of the traceback is actually the bound method `index` of list object X (which was passed as input to `fit_transform`), instead of a pandas `DataFrame.index`.

### Steps/Code to Reproduce

```
import pandas as pd
from sklearn import preprocessing
from sklearn.datasets import make_blobs

my_stand_scale = preprocessing.StandardScaler()
my_stand_scale.set_output(transform='pandas')

X, y = make_blobs(
        n_samples=30,
        centers=[[0, 0, 0], [1, 1, 1]],
        random_state=0,
        n_features=2,
        cluster_std=0.1,
    )
    
my_stand_scale.fit_transform(X=X.tolist())
```

### Expected Results

no error is thrown when `fit_transform` is called.

### Actual Results

```
Traceback (most recent call last):
  File "/home/nihal/issue_check.py", line 16, in <module>
    my_stand_scale.fit_transform(X=X.tolist())
  File "/home/nihal/scikit-learn/sklearn/utils/_set_output.py", line 140, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
  File "/home/nihal/scikit-learn/sklearn/base.py", line 948, in fit_transform
    return self.fit(X, **fit_params).transform(X)
  File "/home/nihal/scikit-learn/sklearn/utils/_set_output.py", line 153, in wrapped
    return _wrap_data_with_container(method, data_to_wrap, X, self)
  File "/home/nihal/scikit-learn/sklearn/utils/_set_output.py", line 128, in _wrap_data_with_container
    return _wrap_in_pandas_container(
  File "/home/nihal/scikit-learn/sklearn/utils/_set_output.py", line 60, in _wrap_in_pandas_container
    return pd.DataFrame(data_to_wrap, index=index, columns=columns, copy=False)
  File "/home/nihal/miniconda3/envs/sklearn-env/lib/python3.9/site-packages/pandas/core/frame.py", line 722, in __init__
    mgr = ndarray_to_mgr(
  File "/home/nihal/miniconda3/envs/sklearn-env/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 345, in ndarray_to_mgr
    index, columns = _get_axes(
  File "/home/nihal/miniconda3/envs/sklearn-env/lib/python3.9/site-packages/pandas/core/internals/construction.py", line 748, in _get_axes
    index = ensure_index(index)
  File "/home/nihal/miniconda3/envs/sklearn-env/lib/python3.9/site-packages/pandas/core/indexes/base.py", line 7333, in ensure_index
    return Index._with_infer(index_like, copy=copy)
  File "/home/nihal/miniconda3/envs/sklearn-env/lib/python3.9/site-packages/pandas/core/indexes/base.py", line 716, in _with_infer
    result = cls(*args, **kwargs)
  File "/home/nihal/miniconda3/envs/sklearn-env/lib/python3.9/site-packages/pandas/core/indexes/base.py", line 565, in __new__
    subarr = com.asarray_tuplesafe(data, dtype=_dtype_obj)
  File "/home/nihal/miniconda3/envs/sklearn-env/lib/python3.9/site-packages/pandas/core/common.py", line 238, in asarray_tuplesafe
    values = list(values)
TypeError: 'builtin_function_or_method' object is not iterable
```

### Versions

```shell
System:
    python: 3.9.16 | packaged by conda-forge | (main, Feb  1 2023, 21:39:03)  [GCC 11.3.0]
executable: /home/nihal/miniconda3/envs/sklearn-env/bin/python
   machine: Linux-5.15.0-76-generic-x86_64-with-glibc2.31

Python dependencies:
      sklearn: 1.4.dev0
          pip: 23.2.1
   setuptools: 68.0.0
        numpy: 1.25.2
        scipy: 1.11.1
       Cython: 3.0.0
       pandas: 1.5.3
   matplotlib: None
       joblib: 1.3.1
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 8
         prefix: libopenblas
       filepath: /home/nihal/miniconda3/envs/sklearn-env/lib/libopenblasp-r0.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 8
         prefix: libgomp
       filepath: /home/nihal/miniconda3/envs/sklearn-env/lib/libgomp.so.1.0.0
        version: None
```

COMMENT:
StandardScaler's fit_transform() method does not work with a list as input data when the output is configured to 'pandas,' to solve this , i have created a custom transformer class that converts the input list to a Pandas DataFrame before applying the scaling

import pandas as pd
from sklearn.base import TransformerMixin
from sklearn.preprocessing import StandardScaler
class ListToDataFrameScaler(TransformerMixin):
    def __init__(self):
        self.scaler = StandardScaler()

    def fit(self, X, y=None):
        # Converting the input list to a Pandas DataFrame
        self.X_df = pd.DataFrame(X)
        self.scaler.fit(self.X_df)
        return self

    def transform(self, X, y=None):
        # Converting the input list to a Pandas DataFrame and apply scaling
        X_df = pd.DataFrame(X)
        scaled_data = self.scaler.transform(X_df)
        return scaled_data
This custom transformer class wraping the StandardScaler and handles the conversion of the input list to a Pandas DataFrame.

-Use the Custom Transformer
from sklearn.pipeline import Pipeline
data_list = [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]]
list_to_df_scaler = ListToDataFrameScaler()
pipeline = Pipeline([
    ('list_to_df_scaler', list_to_df_scaler),
])
scaled_data = pipeline.fit_transform(data_list)
This pipeline uses the custom transformer to first convert the list data to a Pandas DataFrame and then apply the standard scaling.
This approach allows me to maintain compatibility with both list and Pandas DataFrame inputs while applying the necessary transformations.

Labels: Bug