diff --git a/Actors and Directors who Cooperated At Least Three Times b/Actors and Directors who Cooperated At Least Three Times
new file mode 100644
index 0000000..684515a
--- /dev/null
+++ b/Actors and Directors who Cooperated At Least Three Times	
@@ -0,0 +1,24 @@
+import pandas as pd
+
+def actors_and_directors(actor_director: pd.DataFrame) -> pd.DataFrame:
+    #long version - have a dcitionary in place where actor id and director id are stored in form of tuple
+
+    # dict = {}
+    # for i in range(len(actor_director)):
+    #     a_id = actor_director['actor_id'][i]
+    #     d_id = actor_director['director_id'][i]
+    #     if (a_id,d_id) not in dict:
+    #         dict[(a_id,d_id)] =0
+    #     dict[(a_id,d_id)] +=1
+
+    # result =[]
+    # for key,value in dict.items():
+    #     if value >=3:
+    #         result.append(key)
+    # return pd.DataFrame(result, columns = ['actor_id','director_id'])
+
+    #pandas solution 
+     df = actor_director.groupby(['actor_id','director_id']).size().reset_index(name='count')
+     return df[df['count'] >= 3][['actor_id', 'director_id']]
+
+
diff --git a/Daily Leads and Partners b/Daily Leads and Partners
new file mode 100644
index 0000000..e9ca9de
--- /dev/null
+++ b/Daily Leads and Partners	
@@ -0,0 +1,13 @@
+import pandas as pd
+
+def daily_leads_and_partners(daily_sales: pd.DataFrame) -> pd.DataFrame:
+    if daily_sales.empty:  # Handle case where there's no data
+        return pd.DataFrame(columns=["date_id", "make_name", "unique_leads", "unique_partners"])
+
+    # Group by date_id and make_name, and count unique leads and partners
+    result = daily_sales.groupby(["date_id", "make_name"]).agg(
+        unique_leads=("lead_id", "nunique"),
+        unique_partners=("partner_id", "nunique")
+    ).reset_index()
+
+    return result
diff --git a/Group Sold Products by the Date b/Group Sold Products by the Date
new file mode 100644
index 0000000..70c368f
--- /dev/null
+++ b/Group Sold Products by the Date	
@@ -0,0 +1,13 @@
+import pandas as pd
+
+def categorize_products(activities: pd.DataFrame) -> pd.DataFrame:
+    if activities.empty:  # Handle case where there's no data
+        return pd.DataFrame(columns=["sell_date", "num_sold", "products"])
+
+    # Group by sell_date and compute required fields
+    result = activities.groupby("sell_date").agg(
+        num_sold=("product", "nunique"),  # Count unique products
+        products=("product", lambda x: ",".join(sorted(x.unique())))  # Sort and join product names
+    ).reset_index()
+
+    return result