[ENH] Allow pandas.Series as valid input for second_level_input

nilearn/_utils/docs.py

@@ -795,8 +795,10 @@ def custom_function(vertices):
     "second_level_input"
 ] = """
 second_level_input : :obj:`list` of \
-:class:`~nilearn.glm.first_level.FirstLevelModel` objects \
-or :class:`pandas.DataFrame` or :obj:`list` of Niimg-like objects.
+    :class:`~nilearn.glm.first_level.FirstLevelModel` objects or \
+    :class:`pandas.DataFrame` or \
+    :obj:`list` of Niimg-like objects or \
+    :obj:`pandas.Series` of Niimg-like objects.
 
     - Giving :class:`~nilearn.glm.first_level.FirstLevelModel` objects
       will allow to easily compute the second level contrast of arbitrary first

nilearn/glm/second_level/second_level.py

@@ -48,6 +48,8 @@ def _check_input_type(second_level_input):
     """Determine the type of input provided."""
     if isinstance(second_level_input, pd.DataFrame):
         return "df_object"
+    if isinstance(second_level_input, pd.Series):
+        return "pd_series"
     if isinstance(second_level_input, (str, Nifti1Image)):
         return "nii_object"
     if isinstance(second_level_input, list):
@@ -56,6 +58,7 @@ def _check_input_type(second_level_input):
         "second_level_input must be "
         "either a pandas DataFrame, "
         "a Niimg-like object, "
+        "a pandas Series of Niimg-like object, "
         "a list of Niimg-like object or "
         "a list of FirstLevelModel objects. "
         f"Got {_return_type(second_level_input)} instead"
@@ -105,6 +108,9 @@ def _check_input_as_type(
 ):
     if input_type == "flm_object":
         _check_input_as_first_level_model(second_level_input, none_confounds)
+    elif input_type == "pd_series":
+        second_level_input = second_level_input.to_list()
+        _check_input_as_nifti_images(second_level_input, none_design_matrix)
     elif input_type == "nii_object":
         _check_input_as_nifti_images(second_level_input, none_design_matrix)
     else:

nilearn/glm/tests/test_second_level.py

@@ -624,6 +624,26 @@ def test_fmri_inputs(tmp_path, rng):
     SecondLevelModel().fit(niimg_4d, None, sdes)
 
 
+def test_fmri_pand_series_as_inputs(tmp_path, rng):
+    # prepare correct input dataframe and lists
+    p, q = 80, 10
+    X = rng.standard_normal(size=(p, q))
+    shapes = (SHAPE,)
+    _, FUNCFILE, _ = write_fake_fmri_data_and_design(
+        shapes, file_path=tmp_path
+    )
+    FUNCFILE = FUNCFILE[0]
+
+    # dataframes as input
+    sdes = pd.DataFrame(X[:3, :3], columns=["intercept", "b", "c"])
+    niidf = pd.DataFrame({"filepaths": [FUNCFILE, FUNCFILE, FUNCFILE]})
+    SecondLevelModel().fit(
+        second_level_input=niidf["filepaths"],
+        confounds=None,
+        design_matrix=sdes,
+    )
+
+
 def test_fmri_inputs_errors(tmp_path):
     # Test processing of FMRI inputs
     # prepare fake data