Move detect_categorical function to preprocessors module.

tableone/preprocessors.py

@@ -1,4 +1,4 @@
-
+import numpy as np
 
 def ensure_list(arg, arg_name):
     """
@@ -12,3 +12,37 @@ def ensure_list(arg, arg_name):
         return arg
     else:
         raise TypeError(f"{arg_name} must be a string or a list of strings.")
+
+
+def detect_categorical(data, groupby) -> list:
+    """
+    Detect categorical columns if they are not specified.
+
+    Parameters
+    ----------
+        data : pandas DataFrame
+            The input dataset.
+        groupby : str (optional)
+            The groupby variable.
+
+    Returns
+    ----------
+        likely_cat : list
+            List of variables that appear to be categorical.
+    """
+    # assume all non-numerical and date columns are categorical
+    numeric_cols = set(data._get_numeric_data().columns.values)
+    date_cols = set(data.select_dtypes(include=[np.datetime64]).columns)
+    likely_cat = set(data.columns) - numeric_cols
+    likely_cat = list(likely_cat - date_cols)
+
+    # check proportion of unique values if numerical
+    for var in data._get_numeric_data().columns:
+        likely_flag = 1.0 * data[var].nunique()/data[var].count() < 0.005
+        if likely_flag:
+            likely_cat.append(var)
+
+    if groupby:
+        likely_cat = [x for x in likely_cat if x != groupby]
+
+    return likely_cat

tableone/tableone.py

@@ -10,7 +10,7 @@
 from tabulate import tabulate
 
 from tableone.deprecations import deprecated_parameter
-from tableone.preprocessors import ensure_list
+from tableone.preprocessors import ensure_list, detect_categorical
 from tableone.statistics import Statistics
 from tableone.validators import DataValidator, InputValidator, InputError
 
@@ -236,13 +236,12 @@ def __init__(self, data: pd.DataFrame,
         self.input_validator.validate(groupby, self._nonnormal, min_max, pval_adjust, order,  # type: ignore
                                       pval, self._columns, categorical, continuous)  # type: ignore
 
-        # if categorical not specified, try to identify categorical
+        # if categorical is set to None, try to automatically detect
         # if empty list is provided, assume there are no categorical variables.
-        if not categorical and type(categorical) != list:
-            categorical = self._detect_categorical_columns(data[self._columns])
-            # omit categorical row if it is specified in groupby
-            if groupby:
-                categorical = [x for x in categorical if x != groupby]
+        if categorical is None:
+            self._categorical = detect_categorical(data[self._columns], groupby)
+        else:
+            self._categorical = categorical
 
         # if input df has ordered categorical variables, get the order.
         order_cats = [x for x in data.select_dtypes("category")
@@ -266,8 +265,7 @@ def __init__(self, data: pd.DataFrame,
             self._continuous = continuous
         else:
             self._continuous = [c for c in self._columns  # type: ignore
-                                if c not in categorical + [groupby]]
-        self._categorical = categorical
+                                if c not in self._categorical + [groupby]]
         self._ddof = ddof
         self._decimals = decimals
         self._dip_test = dip_test
@@ -481,32 +479,6 @@ def _generate_remarks(self, newline='\n') -> str:
 
         return msg
 
-    def _detect_categorical_columns(self, data) -> list:
-        """
-        Detect categorical columns if they are not specified.
-
-        Parameters
-        ----------
-            data : pandas DataFrame
-                The input dataset.
-
-        Returns
-        ----------
-            likely_cat : list
-                List of variables that appear to be categorical.
-        """
-        # assume all non-numerical and date columns are categorical
-        numeric_cols = set(data._get_numeric_data().columns.values)
-        date_cols = set(data.select_dtypes(include=[np.datetime64]).columns)
-        likely_cat = set(data.columns) - numeric_cols
-        likely_cat = list(likely_cat - date_cols)
-        # check proportion of unique values if numerical
-        for var in data._get_numeric_data().columns:
-            likely_flag = 1.0 * data[var].nunique()/data[var].count() < 0.005
-            if likely_flag:
-                likely_cat.append(var)
-        return likely_cat
-
     def _t1_summary(self, x: pd.Series) -> str:
         """
         Compute median [IQR] or mean (Std) for the input series.