From 2e8683d602dff49520f8dba575db9e4dd1b351cd Mon Sep 17 00:00:00 2001
From: Colin Ho <colin.ho99@gmail.com>
Date: Mon, 13 Nov 2023 12:50:17 -0800
Subject: [PATCH] use warnings.warn

---
 daft/dataframe/dataframe.py | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/daft/dataframe/dataframe.py b/daft/dataframe/dataframe.py
index e701640b76..36b70c5774 100644
--- a/daft/dataframe/dataframe.py
+++ b/daft/dataframe/dataframe.py
@@ -4,7 +4,6 @@
 # in order to support runtime typechecking across different Python versions.
 # For technical details, see https://github.com/Eventual-Inc/Daft/pull/630
 
-import logging
 import pathlib
 from dataclasses import dataclass
 from functools import reduce
@@ -21,6 +20,7 @@
     TypeVar,
     Union,
 )
+import warnings
 
 from daft.api_annotations import DataframePublicAPI
 from daft.context import get_context
@@ -47,8 +47,6 @@
 
 from daft.logical.schema import Schema
 
-logger = logging.getLogger(__name__)
-
 UDFReturnType = TypeVar("UDFReturnType", covariant=True)
 
 ColumnInputType = Union[Expression, str]
@@ -854,7 +852,7 @@ def sum(self, *cols: ColumnInputType) -> "DataFrame":
             DataFrame: Globally aggregated sums. Should be a single row.
         """
         if len(cols) == 0:
-            logger.warning(
+            warnings.warn(
                 "No columns specified; performing sum on all columns. Specify columns using df.sum('col1', 'col2', ...)."
             )
             cols = tuple(self.columns)
@@ -870,7 +868,7 @@ def mean(self, *cols: ColumnInputType) -> "DataFrame":
             DataFrame: Globally aggregated mean. Should be a single row.
         """
         if len(cols) == 0:
-            logger.warning(
+            warnings.warn(
                 "No columns specified; performing mean on all columns. Specify columns using df.mean('col1', 'col2', ...)."
             )
             cols = tuple(self.columns)
@@ -886,7 +884,7 @@ def min(self, *cols: ColumnInputType) -> "DataFrame":
             DataFrame: Globally aggregated min. Should be a single row.
         """
         if len(cols) == 0:
-            logger.warning(
+            warnings.warn(
                 "No columns specified; performing min on all columns. Specify columns using df.min('col1', 'col2', ...)."
             )
             cols = tuple(self.columns)
@@ -902,7 +900,7 @@ def max(self, *cols: ColumnInputType) -> "DataFrame":
             DataFrame: Globally aggregated max. Should be a single row.
         """
         if len(cols) == 0:
-            logger.warning(
+            warnings.warn(
                 "No columns specified; performing max on all columns. Specify columns using df.max('col1', 'col2', ...)."
             )
             cols = tuple(self.columns)
@@ -918,7 +916,7 @@ def count(self, *cols: ColumnInputType) -> "DataFrame":
             DataFrame: Globally aggregated count. Should be a single row.
         """
         if len(cols) == 0:
-            logger.warning(
+            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)
@@ -934,7 +932,7 @@ def agg_list(self, *cols: ColumnInputType) -> "DataFrame":
             DataFrame: Globally aggregated list. Should be a single row.
         """
         if len(cols) == 0:
-            logger.warning(
+            warnings.warn(
                 "No columns specified; performing agg_list on all columns. Specify columns using df.agg_list('col1', 'col2', ...)."
             )
             cols = tuple(self.columns)
@@ -950,7 +948,7 @@ def agg_concat(self, *cols: ColumnInputType) -> "DataFrame":
             DataFrame: Globally aggregated list. Should be a single row.
         """
         if len(cols) == 0:
-            logger.warning(
+            warnings.warn(
                 "No columns specified; performing agg_concat on all columns. Specify columns using df.agg_concat('col1', 'col2', ...)."
             )
             cols = tuple(self.columns)