diff --git a/daft/dataframe/dataframe.py b/daft/dataframe/dataframe.py
index 467e9dfa8a..9c99652e78 100644
--- a/daft/dataframe/dataframe.py
+++ b/daft/dataframe/dataframe.py
@@ -5,6 +5,7 @@
 # For technical details, see https://github.com/Eventual-Inc/Daft/pull/630
 
 import pathlib
+import warnings
 from dataclasses import dataclass
 from functools import reduce
 from typing import (
@@ -858,7 +859,11 @@ def sum(self, *cols: ColumnInputType) -> "DataFrame":
         Returns:
             DataFrame: Globally aggregated sums. Should be a single row.
         """
-        assert len(cols) > 0, "no columns were passed in"
+        if len(cols) == 0:
+            warnings.warn(
+                "No columns specified; performing sum on all columns. Specify columns using df.sum('col1', 'col2', ...)."
+            )
+            cols = tuple(self.columns)
         return self._agg([(c, "sum") for c in cols])
 
     @DataframePublicAPI
@@ -870,7 +875,11 @@ def mean(self, *cols: ColumnInputType) -> "DataFrame":
         Returns:
             DataFrame: Globally aggregated mean. Should be a single row.
         """
-        assert len(cols) > 0, "no columns were passed in"
+        if len(cols) == 0:
+            warnings.warn(
+                "No columns specified; performing mean on all columns. Specify columns using df.mean('col1', 'col2', ...)."
+            )
+            cols = tuple(self.columns)
         return self._agg([(c, "mean") for c in cols])
 
     @DataframePublicAPI
@@ -882,7 +891,11 @@ def min(self, *cols: ColumnInputType) -> "DataFrame":
         Returns:
             DataFrame: Globally aggregated min. Should be a single row.
         """
-        assert len(cols) > 0, "no columns were passed in"
+        if len(cols) == 0:
+            warnings.warn(
+                "No columns specified; performing min on all columns. Specify columns using df.min('col1', 'col2', ...)."
+            )
+            cols = tuple(self.columns)
         return self._agg([(c, "min") for c in cols])
 
     @DataframePublicAPI
@@ -894,7 +907,11 @@ def max(self, *cols: ColumnInputType) -> "DataFrame":
         Returns:
             DataFrame: Globally aggregated max. Should be a single row.
         """
-        assert len(cols) > 0, "no columns were passed in"
+        if len(cols) == 0:
+            warnings.warn(
+                "No columns specified; performing max on all columns. Specify columns using df.max('col1', 'col2', ...)."
+            )
+            cols = tuple(self.columns)
         return self._agg([(c, "max") for c in cols])
 
     @DataframePublicAPI
@@ -906,7 +923,11 @@ def count(self, *cols: ColumnInputType) -> "DataFrame":
         Returns:
             DataFrame: Globally aggregated count. Should be a single row.
         """
-        assert len(cols) > 0, "no columns were passed in"
+        if len(cols) == 0:
+            warnings.warn(
+                "No columns specified; performing count on all columns. Specify columns using df.count('col1', 'col2', ...) or use df.count_rows() for row counts."
+            )
+            cols = tuple(self.columns)
         return self._agg([(c, "count") for c in cols])
 
     @DataframePublicAPI
@@ -918,7 +939,11 @@ def agg_list(self, *cols: ColumnInputType) -> "DataFrame":
         Returns:
             DataFrame: Globally aggregated list. Should be a single row.
         """
-        assert len(cols) > 0, "no columns were passed in"
+        if len(cols) == 0:
+            warnings.warn(
+                "No columns specified; performing agg_list on all columns. Specify columns using df.agg_list('col1', 'col2', ...)."
+            )
+            cols = tuple(self.columns)
         return self._agg([(c, "list") for c in cols])
 
     @DataframePublicAPI
@@ -930,7 +955,11 @@ def agg_concat(self, *cols: ColumnInputType) -> "DataFrame":
         Returns:
             DataFrame: Globally aggregated list. Should be a single row.
         """
-        assert len(cols) > 0, "no columns were passed in"
+        if len(cols) == 0:
+            warnings.warn(
+                "No columns specified; performing agg_concat on all columns. Specify columns using df.agg_concat('col1', 'col2', ...)."
+            )
+            cols = tuple(self.columns)
         return self._agg([(c, "concat") for c in cols])
 
     @DataframePublicAPI