From 312f88230ec108eea0b35175bb8ce19303aa2b5e Mon Sep 17 00:00:00 2001
From: Tom Pollard <tpollard@mit.edu>
Date: Fri, 7 Jun 2024 15:53:43 -0400
Subject: [PATCH] Move create_cat_table function to tables module.

---
 tableone/tableone.py | 54 +++++++++++---------------------------------
 tableone/tables.py   | 50 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 63 insertions(+), 41 deletions(-)

diff --git a/tableone/tableone.py b/tableone/tableone.py
index 4874644..f6c1d86 100644
--- a/tableone/tableone.py
+++ b/tableone/tableone.py
@@ -337,7 +337,19 @@ def __init__(self, data: pd.DataFrame,
 
         # create continuous and categorical tables
         if self._categorical:
-            self.cat_table = self._create_cat_table(data, self._overall)
+            self.cat_table = self.tables.create_cat_table(data,
+                                                          self._overall,
+                                                          self.cat_describe,
+                                                          self._categorical,
+                                                          self._pval,
+                                                          self._pval_adjust,
+                                                          self._htest_table,
+                                                          self._smd,
+                                                          self.smd_table,
+                                                          self._groupby,
+                                                          self.cat_describe_all)
+
+
 
         if self._continuous:
             self.cont_table = self.tables.create_cont_table(data,
@@ -522,46 +534,6 @@ def _t1_summary(self, x: pd.Series) -> str:
                 f = '{{:.{}f}} ({{:.{}f}})'.format(n, n)
                 return f.format(np.nanmean(x.values), self.statistics._std(x, self._ddof))  # type: ignore
 
-    def _create_cat_table(self, data, overall):
-        """
-        Create table one for categorical data.
-
-        Returns
-        ----------
-        table : pandas DataFrame
-            A table summarising the categorical variables.
-        """
-        table = self.cat_describe['t1_summary'].copy()
-
-        # add the total count of null values across all levels
-        isnull = data[self._categorical].isnull().sum().to_frame(
-            name='Missing')
-        isnull.index = isnull.index.rename('variable')
-        try:
-            table = table.join(isnull)
-        # if columns form a CategoricalIndex, need to convert to string first
-        except TypeError:
-            table.columns = table.columns.astype(str)
-            table = table.join(isnull)
-
-        # add pval column
-        if self._pval and self._pval_adjust:
-            table = table.join(self._htest_table[['P-Value (adjusted)',
-                                                  'Test']])
-        elif self._pval:
-            table = table.join(self._htest_table[['P-Value', 'Test']])
-
-        # add standardized mean difference (SMD) column/s
-        if self._smd:
-            table = table.join(self.smd_table)
-
-        # join the overall column if needed
-        if self._groupby and overall:
-            table = table.join(pd.concat([self.cat_describe_all['t1_summary'].
-                                          Overall], axis=1, keys=["Overall"]))
-
-        return table
-
     def _create_tableone(self, data):
         """
         Create table 1 by combining the continuous and categorical tables.
diff --git a/tableone/tables.py b/tableone/tables.py
index 608cc4d..7135136 100644
--- a/tableone/tables.py
+++ b/tableone/tables.py
@@ -439,3 +439,53 @@ def create_cont_table(self,
                                           Overall], axis=1, keys=["Overall"]))
 
         return table
+
+    def create_cat_table(self,
+                         data,
+                         overall,
+                         cat_describe,
+                         categorical,
+                         pval,
+                         pval_adjust,
+                         htest_table,
+                         smd,
+                         smd_table,
+                         groupby,
+                         cat_describe_all):
+        """
+        Create table one for categorical data.
+
+        Returns
+        ----------
+        table : pandas DataFrame
+            A table summarising the categorical variables.
+        """
+        table = cat_describe['t1_summary'].copy()
+
+        # add the total count of null values across all levels
+        isnull = data[categorical].isnull().sum().to_frame(name='Missing')
+        isnull.index = isnull.index.rename('variable')
+
+        try:
+            table = table.join(isnull)
+        # if columns form a CategoricalIndex, need to convert to string first
+        except TypeError:
+            table.columns = table.columns.astype(str)
+            table = table.join(isnull)
+
+        # add pval column
+        if pval and pval_adjust:
+            table = table.join(htest_table[['P-Value (adjusted)', 'Test']])
+        elif pval:
+            table = table.join(htest_table[['P-Value', 'Test']])
+
+        # add standardized mean difference (SMD) column/s
+        if smd:
+            table = table.join(smd_table)
+
+        # join the overall column if needed
+        if groupby and overall:
+            table = table.join(pd.concat([cat_describe_all['t1_summary'].Overall],
+                                         axis=1, keys=["Overall"]))
+
+        return table