Merge pull request #164 from tompollard/tp/bump_pandas_2_2_2

Fix deprecated Pandas syntax (up to v2.2.2)

tableone/tableone.py

@@ -889,8 +889,8 @@ def _create_cont_describe(self,
             df_cont : pandas DataFrame
                 Summarise the continuous variables.
         """
-        aggfuncs = [pd.Series.count, np.mean, np.median, self._std,
-                    self._q25, self._q75, min, max, self._t1_summary]
+        aggfuncs = ['count', 'mean', 'median', self._std,
+                    self._q25, self._q75, 'min', 'max', self._t1_summary]
 
         if self._dip_test:
             aggfuncs.append(self._hartigan_dip)
@@ -1173,7 +1173,7 @@ def _create_smd_table(self, data: pd.DataFrame) -> pd.DataFrame:
                                 n1=self.cont_describe['count'][p[0]].loc[v],
                                 n2=self.cont_describe['count'][p[1]].loc[v],
                                 unbiased=False)
-                    df[colname.format(p[0], p[1])].loc[v] = smd
+                    df.loc[v, colname.format(p[0], p[1])] = smd
             except AttributeError:
                 pass
 
@@ -1187,7 +1187,7 @@ def _create_smd_table(self, data: pd.DataFrame) -> pd.DataFrame:
                         n1=self.cat_describe.loc[[v]]['freq'][p[0]].sum(),
                         n2=self.cat_describe.loc[[v]]['freq'][p[1]].sum(),
                         unbiased=False)
-                    df[colname.format(p[0], p[1])].loc[v] = smd  # type: ignore
+                    df.loc[v, colname.format(p[0], p[1])] = smd  # type: ignore
             except AttributeError:
                 pass
 

tests/unit/test_tableone.py

@@ -119,7 +119,11 @@ def data_mixed(n=20):
 
     mu, sigma = 50, 5
     data_mixed['mixed numeric data'] = np.random.normal(mu, sigma, n)
+
+    # FutureWarning: Setting an item of incompatible dtype is deprecated
+    # and will raise an error in a future version of pandas.
     data_mixed.loc[1, 'mixed numeric data'] = 'could not measure'
+
     return data_mixed
 
 
@@ -483,15 +487,15 @@ def test_tableone_row_sort_pn(self, data_pn):
 
         # a call to .index.levels[0] automatically sorts the levels
         # instead, call values and use pd.unique as it preserves order
-        tableone_rows = pd.unique([x[0] for x in table.tableone.index.values])
+        tableone_rows = pd.Series([x[0] for x in table.tableone.index.values]).unique()
 
         # default should not sort
         for i, c in enumerate(columns):
             # i+1 because we skip the first row, 'n'
             assert tableone_rows[i+1] == c
 
         table = TableOne(df, columns=columns, sort=True, label_suffix=False)
-        tableone_rows = pd.unique([x[0] for x in table.tableone.index.values])
+        tableone_rows = pd.Series([x[0] for x in table.tableone.index.values]).unique()
         for i, c in enumerate(sorted(columns, key=lambda s: s.lower())):
             # i+1 because we skip the first row, 'n'
             assert tableone_rows[i+1] == c
@@ -967,7 +971,7 @@ def test_compute_standardized_mean_difference_continuous(self, data_pn):
                    'LOS': '0.121'}
 
         for k in exp_smd:
-            smd = t.tableone.loc[k, 'Grouped by MechVent']['SMD (0,1)'][0]
+            smd = t.tableone.loc[k, 'Grouped by MechVent']['SMD (0,1)'].iloc[0]
             assert smd == exp_smd[k]
 
     def test_compute_standardized_mean_difference_categorical(self, data_pn):
@@ -995,7 +999,7 @@ def test_compute_standardized_mean_difference_categorical(self, data_pn):
                    'death': '0.017'}
 
         for k in exp_smd:
-            smd = t.tableone.loc[k, 'Grouped by MechVent']['SMD (0,1)'][0]
+            smd = t.tableone.loc[k, 'Grouped by MechVent']['SMD (0,1)'].iloc[0]
             assert smd == exp_smd[k]
 
     def test_order_of_order_categorical_columns(self):