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Filter Df_orders By Sum Of… · Python: Aggregations And…

Home / Practice / Python: Aggregations And Group Operations

Filter Df_orders By Sum Of Total_amount Per Customer_id Over 1000

#6 · 6 of 40

Python: Aggregations And Group Operations

40 exercises

01Group Df_orders By 'Status' And Compute Mean Total_amountFree
02Sum 'Quantity' Per Product In Df_order_itemsFree
03Multiple Aggregations On Quantity In Df_order_itemsFree
04Group Df_products By (Category_id, Product_name) To Find Mean Unit_priceLocked
05Transform Df_order_items To Center Quantity Around Group MeanLocked
06Filter Df_orders By Sum Of Total_amount Per Customer_id Over 1000Locked
07Group Df_products By 'Category_id' And Use A Custom Median Aggregator On 'Unit_price'Locked
08Group Df_customers By 'City', Count Rows, Then Reset IndexLocked
09Count Rows Per 'Status' In Df_ordersLocked
10Within Df_order_items, Group By Product_id And Create 'Cumulative_quantity' Plus A 'Quantity_centered'Locked
11Average 'Unit_price' By Order_id In Df_order_itemsLocked
12Median 'Unit_price' By Category In Df_productsLocked
13Sum Of Total_amount By Month In Df_ordersLocked
14Max Total_amount Per Customer_id In Df_ordersLocked
15Compute Standard Deviation Of Quantity Per Product_id In Df_order_itemsLocked
16Distinct Customer_id Count Per Order Status In Df_ordersLocked
17Count How Many Products In Each Category_id Within Df_productsLocked
18Cumulative Order Count Per Customer_id In Df_ordersLocked
19Mean Of Customer_id By City In Df_customers (For Demonstration)Locked
20Quantity Ratio Per Product_id In Df_order_itemsLocked
21Weighted Average 'Unit_price' By Category_id In Df_productsLocked
22Rolling Sum Of Quantity Per Product_id In Df_order_items, After Sorting By Order_idLocked
23Within Df_orders, Compute Expanding Sum Of Total_amount For Each Customer_idLocked
24Quantile Aggregator: Compute The 0.75 Quantile Of Unit_price Per Category_id In Df_productsLocked
25Create A Cross-Tab Of City Vs Status By Merging Df_orders & Df_customersLocked
26Pivot Table With 'Qty_bin' Index And 'Product_id' Columns, Counting Order_idLocked
27Group Df_orders By 'Status', Aggregator On Total_amount With Mean & Max, Then Rename ColumnsLocked
28Rank 'Total_amount' Within Each Status Group In Df_ordersLocked
29Find Top 3 Orders By Total_amount For Each City After Merging Df_orders & Df_customersLocked
30Compute Difference In Total_amount Within Each Status Group In Df_ordersLocked
31Shift Total_amount By 1 Row Within Each Customer_id Group In Df_ordersLocked
32Sample N=2 Rows From Each Status Group In Df_ordersLocked
33Fill Missing Unit_price With Group Median In Df_order_items, Grouping By Product_idLocked
34Group Df_orders By (Employee_id, Status) And Sum Total_amountLocked
35Group Df_products By Category_id, Custom Aggregator For Mean_price & Sum_stockLocked
36Count Df_customers By City, Then Rename The Group Index To 'City_name'Locked
37Merge Df_orders & Df_customers, Pivot On City Vs Status Summing Total_amount, Fill Missing With 0Locked
38Group Df_orders By (Status, Customer_id), Sum Total_amount, Then Unstack So Status Becomes ColumnsLocked
39Re-Stack That Grouped Result From #38 Back To Row FormLocked
40Within Df_order_items, Group By Order_id And Normalize Quantity Using A Custom Transform FunctionLocked

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All exercises in Python: Aggregations And Group Operations40

Python: Aggregations And Group Operations

40 exercises

01Group Df_orders By 'Status' And Compute Mean Total_amountFree
02Sum 'Quantity' Per Product In Df_order_itemsFree
03Multiple Aggregations On Quantity In Df_order_itemsFree
04Group Df_products By (Category_id, Product_name) To Find Mean Unit_priceLocked
05Transform Df_order_items To Center Quantity Around Group MeanLocked
06Filter Df_orders By Sum Of Total_amount Per Customer_id Over 1000Locked
07Group Df_products By 'Category_id' And Use A Custom Median Aggregator On 'Unit_price'Locked
08Group Df_customers By 'City', Count Rows, Then Reset IndexLocked
09Count Rows Per 'Status' In Df_ordersLocked
10Within Df_order_items, Group By Product_id And Create 'Cumulative_quantity' Plus A 'Quantity_centered'Locked
11Average 'Unit_price' By Order_id In Df_order_itemsLocked
12Median 'Unit_price' By Category In Df_productsLocked
13Sum Of Total_amount By Month In Df_ordersLocked
14Max Total_amount Per Customer_id In Df_ordersLocked
15Compute Standard Deviation Of Quantity Per Product_id In Df_order_itemsLocked
16Distinct Customer_id Count Per Order Status In Df_ordersLocked
17Count How Many Products In Each Category_id Within Df_productsLocked
18Cumulative Order Count Per Customer_id In Df_ordersLocked
19Mean Of Customer_id By City In Df_customers (For Demonstration)Locked
20Quantity Ratio Per Product_id In Df_order_itemsLocked
21Weighted Average 'Unit_price' By Category_id In Df_productsLocked
22Rolling Sum Of Quantity Per Product_id In Df_order_items, After Sorting By Order_idLocked
23Within Df_orders, Compute Expanding Sum Of Total_amount For Each Customer_idLocked
24Quantile Aggregator: Compute The 0.75 Quantile Of Unit_price Per Category_id In Df_productsLocked
25Create A Cross-Tab Of City Vs Status By Merging Df_orders & Df_customersLocked
26Pivot Table With 'Qty_bin' Index And 'Product_id' Columns, Counting Order_idLocked
27Group Df_orders By 'Status', Aggregator On Total_amount With Mean & Max, Then Rename ColumnsLocked
28Rank 'Total_amount' Within Each Status Group In Df_ordersLocked
29Find Top 3 Orders By Total_amount For Each City After Merging Df_orders & Df_customersLocked
30Compute Difference In Total_amount Within Each Status Group In Df_ordersLocked
31Shift Total_amount By 1 Row Within Each Customer_id Group In Df_ordersLocked
32Sample N=2 Rows From Each Status Group In Df_ordersLocked
33Fill Missing Unit_price With Group Median In Df_order_items, Grouping By Product_idLocked
34Group Df_orders By (Employee_id, Status) And Sum Total_amountLocked
35Group Df_products By Category_id, Custom Aggregator For Mean_price & Sum_stockLocked
36Count Df_customers By City, Then Rename The Group Index To 'City_name'Locked
37Merge Df_orders & Df_customers, Pivot On City Vs Status Summing Total_amount, Fill Missing With 0Locked
38Group Df_orders By (Status, Customer_id), Sum Total_amount, Then Unstack So Status Becomes ColumnsLocked
39Re-Stack That Grouped Result From #38 Back To Row FormLocked
40Within Df_order_items, Group By Order_id And Normalize Quantity Using A Custom Transform FunctionLocked