Completed pandas 10

Actors_and_directors_cooperated3times.py

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+import pandas as pd
+
+def actors_and_directors(actor_director: pd.DataFrame) -> pd.DataFrame:
+  # DF = actor_director.groupby(['actor_id', 'director_id']).count().reset_index()
+
+  # return DF[['actor_id', 'director_id']][(DF['timestamp'] >= 3)]
+  dict = {}
+  res =[]
+  for i in range(len(actor_director)):
+    actor_id = actor_director['actor_id'][i]
+    director_id = actor_director['director_id'][i]
+
+    if (actor_id, director_id) not in dict: 
+      dict[(actor_id, director_id)] = 0
+    dict[(actor_id, director_id)] = dict[(actor_id, director_id)] + 1
+
+  for key, values in dict.items():
+    if values >= 3:
+      res.append([key[0], key[1]])
+
+
+  return pd.DataFrame(res, columns= ['actor_id', 'director_id'])
+

Daily_lead_and_partners.py

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+import pandas as pd
+
+def daily_leads_and_partners(daily_sales: pd.DataFrame) -> pd.DataFrame:
+    # DF = daily_sales.groupby(['date_id', 'make_name']).agg(
+    #   unique_leads=('lead_id', 'nunique'),
+    #   unique_partners=  ('partner_id', 'nunique')
+    #   ).reset_index()
+
+
+    # return DF
+    dict = {}
+    res=[]
+    for i in range(len(daily_sales)):
+      date_id = daily_sales['date_id'][i]
+      make_name = daily_sales['make_name'][i]
+      lead_id = daily_sales['lead_id'][i]
+      partner_id = daily_sales['partner_id'][i]
+      key = (date_id, make_name)
+
+      if key not in dict:
+        dict[key] = [set(),set()]
+      dict[key][0].add(lead_id)
+      dict[key][1].add(partner_id)    
+
+
+
+    for key, values in dict.items():
+      res.append([key[0], key[1],len(values[0]), len(values[1])])
+
+    DF = pd.DataFrame(res, columns= ('date_id', 'make_name', 'unique_leads', 'unique_partners'))
+
+
+    return DF

Sold_product_by_the_date.py

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+import pandas as pd
+
+def categorize_products(activities: pd.DataFrame) -> pd.DataFrame:
+  Dict = {}
+  res= []
+  for i in range(len(activities)):
+    product = activities['product'][i]
+    date = activities['sell_date'][i]
+    if date not in Dict:
+      Dict[date] = [] 
+    if product not in Dict[date]:
+      Dict[date].append(product)
+
+  for key, values in Dict.items():
+    res.append([key, len(values), values])
+
+  DF = pd.DataFrame(res, columns=['sell_date', 'num_sold', 'products'])
+  DF['products'] = DF['products'].apply(lambda x: ','.join(sorted(x)))
+
+  return DF.sort_values('sell_date')