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