From 5e9a70636b29c55c25603b86a69f697a490e60bf Mon Sep 17 00:00:00 2001
From: biwano <biwano@gmail.com>
Date: Tue, 10 Oct 2023 16:40:37 +0200
Subject: [PATCH] Explicitly do not include group keys

---
 src/apps/services/cache.py       |  8 ++++----
 src/apps/services/credits.py     | 10 +++++-----
 src/apps/services/retirements.py |  4 ++--
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/src/apps/services/cache.py b/src/apps/services/cache.py
index a70e5d6..97eeb39 100644
--- a/src/apps/services/cache.py
+++ b/src/apps/services/cache.py
@@ -188,7 +188,7 @@ def daily_agg(self, df, columns):
         date_column = columns[0]
         """Adds an aggregation by day"""
         df = self.date_manipulations(df, date_column, "daily")
-        df = df.groupby(columns)
+        df = df.groupby(columns, group_keys=False)
         return df
 
     def monthly_agg(self, df, columns):
@@ -197,7 +197,7 @@ def monthly_agg(self, df, columns):
             columns = [columns]
         date_column = columns[0]
         df = self.date_manipulations(df, date_column, "monthly")
-        df = df.groupby(columns)
+        df = df.groupby(columns, group_keys=False)
         return df
 
     @final_cached_command()
@@ -226,7 +226,7 @@ def sum(self, df, column):
     def sum_over_time(self, df, date_column, column, freq):
         df = self.date_manipulations(df, date_column, freq)
         df = df.sort_values(by=date_column, ascending=True)
-        df = df.groupby(date_column)[column].sum().to_frame().reset_index()
+        df = df.groupby(date_column, group_keys=False)[column].sum().to_frame().reset_index()
         df[column] = df[column].cumsum()
         return df
 
@@ -238,7 +238,7 @@ def cumsum(self, df, column):
     @chained_cached_command()
     def monthly_sample(self, df, date_column):
         """Samples daily data into monthly data"""
-        return df.groupby(pd.DatetimeIndex(df[date_column]).to_period('M')).nth(-1).reset_index(drop=True)
+        return df.groupby(pd.DatetimeIndex(df[date_column]).to_period('M'), group_keys=False).nth(-1).reset_index(drop=True)
 
     def date_manipulations(self, df, date_column, freq):
         if date_column not in df:
diff --git a/src/apps/services/credits.py b/src/apps/services/credits.py
index 07d0463..5231208 100644
--- a/src/apps/services/credits.py
+++ b/src/apps/services/credits.py
@@ -92,22 +92,22 @@ def filter(self, df, bridge, pool, status):
     @chained_cached_command()
     def vintage_agg(self, df):
         """Adds an aggregation on vintage"""
-        df = df.groupby("vintage")
+        df = df.groupby("vintage", group_keys=False)
         return df
 
     @chained_cached_command()
     def countries_agg(self, df):
-        df = df.groupby(["country", "country_code"])
+        df = df.groupby(["country", "country_code"], group_keys=False)
         return df
 
     @chained_cached_command()
     def projects_agg(self, df):
-        df = df.groupby("project_type")
+        df = df.groupby("project_type", group_keys=False)
         return df
 
     @chained_cached_command()
     def methodologies_agg(self, df):
-        df = df.groupby("methodology")
+        df = df.groupby("methodology", group_keys=False)
         return df
 
 
@@ -123,7 +123,7 @@ def pool_summary(self, df, kept_fields=[]):
             "mco2_quantity"
         ]
         if isinstance(df, pd.DataFrame):
-            df = df.groupby(lambda x: True)
+            df = df.groupby(lambda x: True, group_keys=False)
 
         if not isinstance(kept_fields, list):
             kept_fields = [kept_fields]
diff --git a/src/apps/services/retirements.py b/src/apps/services/retirements.py
index 5f36c41..d70229d 100644
--- a/src/apps/services/retirements.py
+++ b/src/apps/services/retirements.py
@@ -77,10 +77,10 @@ def summary(df):
 
     @chained_cached_command()
     def beneficiaries_agg(self, df):
-        df = df.groupby("beneficiary")
+        df = df.groupby("beneficiary", group_keys=False)
         return df
 
     @chained_cached_command()
     def tokens_agg(self, df):
-        df = df.groupby("token")
+        df = df.groupby("token", group_keys=False)
         return df