fix type error.

tompollard · Jun 7, 2024 · 53a1824 · 53a1824
1 parent 312f882
commit 53a1824
Showing 1 changed file with 38 additions and 37 deletions.
diff --git a/tableone/tableone.py b/tableone/tableone.py
@@ -446,44 +446,45 @@ def _generate_remarks(self, newline='\n') -> str:
         Generate a series of remarks that the user should consider
         when interpreting the summary statistics.
         """
-        # generate warnings for continuous variables
-        if self._continuous and self._tukey_test:
-            # highlight far outliers
-            outlier_mask = self.cont_describe.far_outliers > 1
-            outlier_vars = list(self.cont_describe.far_outliers[outlier_mask].
+        if self.cont_describe is not None:
+            # generate warnings for continuous variables
+            if self._continuous and self._tukey_test:
+                # highlight far outliers
+                outlier_mask = self.cont_describe.far_outliers > 1
+                outlier_vars = list(self.cont_describe.far_outliers[outlier_mask].
+                                    dropna(how='all').index)
+                if outlier_vars:
+                    self._warnings["""Tukey test indicates far outliers
+                                    in"""] = outlier_vars
+
+            if self._continuous and self._dip_test:
+                # highlight possible multimodal distributions using hartigan's dip
+                # test -1 values indicate NaN
+                modal_mask = ((self.cont_describe.hartigan_dip >= 0) &
+                            (self.cont_describe.hartigan_dip <= 0.05))
+                modal_vars = list(self.cont_describe.hartigan_dip[modal_mask].
                                 dropna(how='all').index)
-            if outlier_vars:
-                self._warnings["""Tukey test indicates far outliers
-                                  in"""] = outlier_vars
-
-        if self._continuous and self._dip_test:
-            # highlight possible multimodal distributions using hartigan's dip
-            # test -1 values indicate NaN
-            modal_mask = ((self.cont_describe.hartigan_dip >= 0) &
-                          (self.cont_describe.hartigan_dip <= 0.05))
-            modal_vars = list(self.cont_describe.hartigan_dip[modal_mask].
-                              dropna(how='all').index)
-            if modal_vars:
-                self._warnings["""Hartigan's Dip Test reports possible
-                                  multimodal distributions for"""] = modal_vars
-
-        if self._continuous and self._normal_test:
-            # highlight non normal distributions
-            # -1 values indicate NaN
-            modal_mask = ((self.cont_describe.normality >= 0) &
-                          (self.cont_describe.normality <= 0.001))
-            modal_vars = list(self.cont_describe.normality[modal_mask].
-                              dropna(how='all').index)
-            if modal_vars:
-                self._warnings["""Normality test reports non-normal
-                                  distributions for"""] = modal_vars
-
-        # create the warning string
-        msg = '{}'.format(newline)
-        for n, k in enumerate(sorted(self._warnings)):
-            msg += '[{}] {}: {}.{}'.format(n+1, k,
-                                           ', '.join(self._warnings[k]),
-                                           newline)
+                if modal_vars:
+                    self._warnings["""Hartigan's Dip Test reports possible
+                                    multimodal distributions for"""] = modal_vars
+
+            if self._continuous and self._normal_test:
+                # highlight non normal distributions
+                # -1 values indicate NaN
+                modal_mask = ((self.cont_describe.normality >= 0) &
+                            (self.cont_describe.normality <= 0.001))
+                modal_vars = list(self.cont_describe.normality[modal_mask].
+                                dropna(how='all').index)
+                if modal_vars:
+                    self._warnings["""Normality test reports non-normal
+                                    distributions for"""] = modal_vars
+
+            # create the warning string
+            msg = '{}'.format(newline)
+            for n, k in enumerate(sorted(self._warnings)):
+                msg += '[{}] {}: {}.{}'.format(n+1, k, ', '.join(self._warnings[k]), newline)
+        else:
+            msg = ""
 
         return msg