Added brute force functions

Author: MomIsBestFriend

pandas/core/nanops.py

@@ -428,10 +428,37 @@ def nanany(values, axis=None, skipna: bool = True, mask=None):
     # doesn't handle ``any`` and ``all`` on
     # object arrays correclty. see
     # https://github.com/numpy/numpy/issues/4352
+
+    # TODO: Find a less code-smelly way of doing this
     if is_object_dtype(dtype) and axis is None:
-        return np.any(values)
+        output = values.any()
+    else:
+        output = values.any(axis)
+
+    if isinstance(output, bool):
+        return output
+
+    # Bruteforce way
+    def is_any(values: np.ndarray) -> bool:
+        """
+        Check if any elements along an axis evaluate to True.
+
+        Parameters
+        ----------
+        values : np.ndarray
+            Array to iterate over it's values.
+
+        Returns
+        -------
+        bool
+            Indicating if any of the values in the array, evaluates to True.
+        """
+        for value in values.tolist():
+            if bool(value):
+                return True
+        return False
 
-    return values.any(axis)
+    return is_any(values)
 
 
 def nanall(values, axis=None, skipna: bool = True, mask=None):
@@ -469,11 +496,37 @@ def nanall(values, axis=None, skipna: bool = True, mask=None):
     # doesn't handle ``any`` and ``all`` on
     # object arrays correclty. see
     # https://github.com/numpy/numpy/issues/4352
-    if is_object_dtype(dtype) and axis is None:
-        return np.all(values)
 
-    return values.all(axis)
+    # TODO: Find a less code-smelly way of doing this
+    if is_object_dtype(dtype) and axis is None:
+        output = values.all()
+    else:
+        output = values.all(axis)
+
+    if isinstance(output, bool):
+        return output
+
+    # Bruteforce way
+    def is_all(values: np.ndarray) -> bool:
+        """
+        Check if all elements along an axis evaluate to True.
+
+        Parameters
+        ----------
+        values : np.ndarray
+            Array to iterate over it's values.
+
+        Returns
+        -------
+        bool
+            Indicating if all of the values in the array, evaluates to True.
+        """
+        for value in values.tolist():
+            if not bool(value):
+                return False
+        return True
 
+    return is_all(values)
 
 @disallow("M8")
 def nansum(values, axis=None, skipna=True, min_count=0, mask=None):