diff --git a/exploration/expl_proximity.py b/exploration/expl_proximity.py
index f3be825..fe85124 100644
--- a/exploration/expl_proximity.py
+++ b/exploration/expl_proximity.py
@@ -16,6 +16,7 @@
 # %%
 # %matplotlib inline
 import datetime
+import importlib
 import os
 import sys
 
@@ -32,13 +33,16 @@ import participants.query_db
 TZ_LJ = timezone("Europe/Ljubljana")
 
 # %%
-from features.proximity import *
+from features import helper, proximity
+
+# %%
+importlib.reload(proximity)
 
 # %% [markdown]
 # # Basic characteristics
 
 # %%
-df_proximity_nokia = get_proximity_data(["nokia_0000003"])
+df_proximity_nokia = proximity.get_proximity_data(["nokia_0000003"])
 print(df_proximity_nokia)
 
 # %%
@@ -53,7 +57,7 @@ df_proximity_nokia.double_proximity.value_counts()
 
 # %%
 participants_inactive_usernames = participants.query_db.get_usernames()
-df_proximity_inactive = get_proximity_data(participants_inactive_usernames)
+df_proximity_inactive = proximity.get_proximity_data(participants_inactive_usernames)
 
 # %%
 df_proximity_inactive.double_proximity.describe()
@@ -110,3 +114,13 @@ df_proximity_combinations[
     (df_proximity_combinations[5.0] != 0)
     & (df_proximity_combinations[5.00030517578125] != 0)
 ]
+
+# %% [markdown]
+# # Features
+
+# %%
+df_proximity_inactive = helper.get_date_from_timestamp(df_proximity_inactive)
+
+# %%
+df_proximity_features = proximity.count_proximity(df_proximity_inactive, ["date_lj"])
+display(df_proximity_features)
diff --git a/features/proximity.py b/features/proximity.py
index aabef74..1884f29 100644
--- a/features/proximity.py
+++ b/features/proximity.py
@@ -78,11 +78,11 @@ def count_proximity(
         A dataframe with the count of "near" proximity values and their relative count.
     """
     if group_by is None:
-        group_by = ["participant_id"]
+        group_by = []
     if "bool_prox_near" not in df_proximity:
         df_proximity = recode_proximity(df_proximity)
     df_proximity["bool_prox_far"] = ~df_proximity["bool_prox_near"]
-    df_proximity_features = df_proximity.groupby(group_by).sum()[
+    df_proximity_features = df_proximity.groupby(["participant_id"] + group_by).sum()[
         ["bool_prox_near", "bool_prox_far"]
     ]
     df_proximity_features = df_proximity_features.assign(