diff --git a/doc/conf.py b/doc/conf.py
index b42f4a5ccd0..d7dc68cf703 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -1327,11 +1327,16 @@ def reset_warnings(gallery_conf, fname):
         category=RuntimeWarning,
     )
     # pandas, via seaborn (examples/time_frequency/time_frequency_erds.py)
-    warnings.filterwarnings(
-        "ignore",
-        message=r"iteritems is deprecated.*Use \.items instead\.",
-        category=FutureWarning,
-    )
+    for message in (
+        "use_inf_as_na option is deprecated.*",
+        r"iteritems is deprecated.*Use \.items instead\.",
+        "is_categorical_dtype is deprecated.*",
+    ):
+        warnings.filterwarnings(
+            "ignore",
+            message=message,
+            category=FutureWarning,
+        )
     # pandas in 50_epochs_to_data_frame.py
     warnings.filterwarnings(
         "ignore", message=r"invalid value encountered in cast", category=RuntimeWarning
diff --git a/tutorials/epochs/40_autogenerate_metadata.py b/tutorials/epochs/40_autogenerate_metadata.py
index df7b59df489..01b9ed0d7ed 100644
--- a/tutorials/epochs/40_autogenerate_metadata.py
+++ b/tutorials/epochs/40_autogenerate_metadata.py
@@ -48,7 +48,6 @@
 import matplotlib.pyplot as plt
 import mne
 
-
 data_dir = Path(mne.datasets.erp_core.data_path())
 infile = data_dir / "ERP-CORE_Subject-001_Task-Flankers_eeg.fif"
 
@@ -240,6 +239,8 @@
 # check whether the response matches the stimulation side, and add this result
 # to another column.
 
+metadata.loc[:, "stimulus_side"] = ""  # initialize column
+
 # left-side stimulation
 metadata.loc[
     metadata["first_stimulus"].isin(
@@ -356,6 +357,8 @@
 # Exactly like in the previous example, we create new columns ``stimulus_side``
 # and ``response_correct``.
 
+metadata.loc[:, "stimulus_side"] = ""  # initialize column
+
 # left-side stimulation
 metadata.loc[
     metadata["last_stimulus"].isin(
diff --git a/tutorials/epochs/50_epochs_to_data_frame.py b/tutorials/epochs/50_epochs_to_data_frame.py
index 164c9120581..66c4443e4a9 100644
--- a/tutorials/epochs/50_epochs_to_data_frame.py
+++ b/tutorials/epochs/50_epochs_to_data_frame.py
@@ -169,7 +169,6 @@
         id_vars=["condition", "epoch"], var_name="channel", value_name="latency of peak"
     )
 )
-
 ax = sns.violinplot(
     x="channel",
     y="latency of peak",