Create Pandas10sol.py

Pandas10sol.py

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+#1484. Group Sold Products By The Date
+import pandas as pd
+
+def categorize_products(activities: pd.DataFrame) -> pd.DataFrame:
+    mydictionary = {}
+    for i in range (len(activities)):
+        sell_date = activities['sell_date'][i]
+        product = activities['product'][i]
+        if sell_date not in mydictionary:
+            mydictionary[sell_date] = set()
+        mydictionary[sell_date].add(product)
+    result = []
+    for key,value in mydictionary.items():
+        temp = []
+        for product in value:
+            temp.append(product)
+        temp.sort()
+        s - ""
+        for i in range(len(temp)):
+            s = s + temp[i]
+            if i != len(temp) - 1:
+                s = s + ','
+        result.append([key, len(value),s])
+    df = pd.DataFrame(result, columns =['sell_date', 'num_sold', 'product'])
+    df.sort_values(by='sell_date', inplace=True)  
+    return df
+
+
+
+import pandas as pd
+
+def categorize_products(activities: pd.DataFrame) -> pd.DataFrame:
+    groups = activities.groupby(['sell_date'])
+    result = groups.agg(
+        num_sold=('product', 'nunique'), 
+        products=('product', lambda x: ','.join(sorted(set(x))))
+    ).reset_index()
+    result.sort_values(by=['sell_date'], inplace=True)
+    return result
+
+
+#1693. Daily Leads and Partners
+import pandas as pd
+
+def daily_leads_and_partners(daily_sales: pd.DataFrame) -> pd.DataFrame:
+    mydictionary = {}
+    for i in range (len(daily_sales)):
+        d_id = daily_sales['date_id'][i]
+        m_name = daily_sales['make_name'][i]
+        l_id = daily_sales['lead_id'][i]
+        p_id = daily_sales['partner_id'][i]
+        if(d_id, m_name) not in mydictionary:
+            mydictionary[(d_id, m_name)] = [set(),set()]
+        mydictionary[(d_id, m_name)][0].add(l_id)
+        mydictionary[(d_id, m_name)][1].add(p_id)
+    result = []
+    for key, value in mydictionary.items():
+        result.append([key[0],key[1], len(value[0]), len(value[1])])
+    return pd.DataFrame(result, columns = ['date_id', 'make_name', 'unique_leads','unique_partners'])
+
+
+
+import pandas as pd
+
+def daily_leads_and_partners(daily_sales: pd.DataFrame) -> pd.DataFrame:
+    df = daily_sales.groupby(['date_id','make_name']).agg({
+        'lead_id' : pd.Series.nunique,
+        'partner_id' : pd.Series.nunique
+    }).reset_index()  
+    result = df.rename(columns={'lead_id' : 'unique_leads', 'partner_id' : 'unique_partners'})  
+    return result
+
+
+import pandas as pd
+
+def daily_leads_and_partners(daily_sales: pd.DataFrame) -> pd.DataFrame:
+    # Grouping by date_id and make_name and aggregating the lead_ids and partner_ids into lists
+    grouped = daily_sales.groupby(['date_id', 'make_name']).agg({
+        'lead_id': lambda x: list(x),
+        'partner_id': lambda x: list(x)
+    }).reset_index()
+
+    # Function to count unique elements in a list
+    def count_unique(lst):
+        return len(set(lst))
+
+    # Applying the count_unique function to lead_id and partner_id columns
+    grouped['unique_leads'] = grouped['lead_id'].apply(count_unique)
+    grouped['unique_partners'] = grouped['partner_id'].apply(count_unique)
+
+    # Dropping the lead_id and partner_id columns
+    grouped.drop(['lead_id', 'partner_id'], axis=1, inplace=True)
+
+    return grouped
+
+#1050. Actors and Directors Who Cooperated At Least Three Times
+import pandas as pd
+
+def actors_and_directors(actor_director: pd.DataFrame) -> pd.DataFrame:
+    df = actor_director.groupby(['actor_id','director_id']).size().reset_index(name = 'counts')
+    df = df[df['counts'] >= 3]
+    return df[['actor_id', 'director_id']]
+
+
+import pandas as pd
+
+def actors_and_directors(actor_director: pd.DataFrame) -> pd.DataFrame:
+    mydictionary = {}
+    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 mydictionary:
+            mydictionary[(a_id, d_id)] = 0
+        mydictionary[(a_id, d_id)] += 1
+    result = []
+    for key, value in mydictionary.items():
+        if value >=3:
+            result.append(key)
+    return pd.DataFrame(result, columns = ['actor_id', 'director_id'])
+