diff --git a/xdem/spatialstats.py b/xdem/spatialstats.py
index d6fb7f31..85257f81 100644
--- a/xdem/spatialstats.py
+++ b/xdem/spatialstats.py
@@ -3299,6 +3299,17 @@ def plot_1d_binning(
     if statistic_name not in df.columns.values:
         raise ValueError(f'The statistic "{statistic_name}" is not part of the provided dataframe column names.')
 
+    # Re-format pandas interval if read from CSV as string
+    if all(isinstance(x, pd.Interval) for x in df[var_name].values):
+        pass
+    # Check for any unformatted interval (saving and reading a pd.DataFrame without MultiIndexing transforms
+    # pd.Interval into strings)
+    elif any(isinstance(_pandas_str_to_interval(x), pd.Interval) for x in df[var_name].values):
+        intervalindex_vals = [_pandas_str_to_interval(x) for x in df[var_name].values]
+        df[var_name] = pd.IntervalIndex(intervalindex_vals)
+    else:
+        raise ValueError("The variable columns must be provided as string or pd.Interval values.")
+
     # Hide axes for the main subplot (which will be subdivded)
     ax.axis("off")
 
@@ -3421,6 +3432,18 @@ def plot_2d_binning(
     if statistic_name not in df.columns.values:
         raise ValueError(f'The statistic "{statistic_name}" is not part of the provided dataframe column names.')
 
+    # Re-format pandas interval if read from CSV as string
+    for var in [var_name_1, var_name_2]:
+        if all(isinstance(x, pd.Interval) for x in df[var].values):
+            pass
+        # Check for any unformatted interval (saving and reading a pd.DataFrame without MultiIndexing transforms
+        # pd.Interval into strings)
+        elif any(isinstance(_pandas_str_to_interval(x), pd.Interval) for x in df[var].values):
+            intervalindex_vals = [_pandas_str_to_interval(x) for x in df[var].values]
+            df[var] = pd.IntervalIndex(intervalindex_vals)
+        else:
+            raise ValueError("The variable columns must be provided as string or pd.Interval values.")
+
     # Hide axes for the main subplot (which will be subdivded)
     ax.axis("off")