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Pandas Grouping and Aggregating: Split-Apply-Combine Exercises, Practice, Solution

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Pandas Grouping and Aggregating [ 32 exercises with solution]

Pandas GroupBy Split-apply-combine Image

1. Write a Pandas program to split the following dataframe into groups based on school code. Also check the type of GroupBy object. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height  weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

2. Write a Pandas program to split the following given dataframe by school code and get mean, min, and max value of age for each school. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height  weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

3. Write a Pandas program to split the following given dataframe into groups based on school code and class. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height  weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

4. Write a Pandas program to split the following given dataframe into groups based on school code and cast grouping as a list. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height  weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

5. Write a Pandas program to split the following given dataframe into groups based on single column and multiple columns. Find the size of the grouped data. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height  weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

6. Write a Pandas program to split the following given dataframe into groups based on school code and call a specific group with the name of the group. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height  weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

7. Write a Pandas program to split a dataset, group by one column and get mean, min, and max values by group. Using the following dataset find the mean, min, and max values of purchase amount (purch_amt) group by customer id (customer_id). Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3005         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3002         5001
3    70004     110.50  2012-08-17         3009         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3007         5001
6    70008    5760.00  2012-09-10         3002         5001
7    70010    1983.43  2012-10-10         3004         5006
8    70003    2480.40  2012-10-10         3009         5003
9    70012     250.45  2012-06-27         3008         5002
10   70011      75.29  2012-08-17         3003         5007
11   70013    3045.60  2012-04-25         3002         5001
Click me to see the sample solution

8. Write a Pandas program to split a dataset to group by two columns and count by each row. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3005         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3002         5001
3    70004     110.50  2012-08-17         3009         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3007         5001
6    70008    5760.00  2012-09-10         3002         5001
7    70010    1983.43  2012-10-10         3004         5006
8    70003    2480.40  2012-10-10         3009         5003
9    70012     250.45  2012-06-27         3008         5002
10   70011      75.29  2012-08-17         3003         5007
11   70013    3045.60  2012-04-25         3002         5001
Click me to see the sample solution

9. Write a Pandas program to split a dataset to group by two columns and then sort the aggregated results within the groups. Go to the editor
In the following dataset group on 'customer_id', 'salesman_id' and then sort sum of purch_amt within the groups.
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3005         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3002         5001
3    70004     110.50  2012-08-17         3009         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3007         5001
6    70008    5760.00  2012-09-10         3002         5001
7    70010    1983.43  2012-10-10         3004         5006
8    70003    2480.40  2012-10-10         3009         5003
9    70012     250.45  2012-06-27         3008         5002
10   70011      75.29  2012-08-17         3003         5007
11   70013    3045.60  2012-04-25         3002         5001
Click me to see the sample solution

10. Write a Pandas program to split the following dataframe into groups based on customer id and create a list of order date for each group. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3005         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3002         5001
3    70004     110.50  2012-08-17         3009         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3007         5001
6    70008    5760.00  2012-09-10         3002         5001
7    70010    1983.43  2012-10-10         3004         5006
8    70003    2480.40  2012-10-10         3009         5003
9    70012     250.45  2012-06-27         3008         5002
10   70011      75.29  2012-08-17         3003         5007
11   70013    3045.60  2012-04-25         3002         5001
Click me to see the sample solution

11. Write a Pandas program to split the following dataframe into groups and calculate monthly purchase amount. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  05-10-2012         3001         5002
1    70009     270.65  09-10-2012         3001         5005
2    70002      65.26  05-10-2012         3005         5001
3    70004     110.50  08-17-2012         3001         5003
4    70007     948.50  10-09-2012         3005         5002
5    70005    2400.60  07-27-2012         3001         5001
6    70008    5760.00  10-09-2012         3005         5001
7    70010    1983.43  10-10-2012         3001         5006
8    70003    2480.40  10-10-2012         3005         5003
9    70012     250.45  06-17-2012         3001         5002
10   70011      75.29  07-08-2012         3005         5007
11   70013    3045.60  04-25-2012         3005         5001
Click me to see the sample solution

12. Write a Pandas program to split the following dataframe into groups, group by month and year based on order date and find the total purchase amount year wise, month wise. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  05-10-2012         3001         5002
1    70009     270.65  09-10-2012         3001         5005
2    70002      65.26  05-10-2012         3005         5001
3    70004     110.50  08-17-2012         3001         5003
4    70007     948.50  10-09-2012         3005         5002
5    70005    2400.60  07-27-2012         3001         5001
6    70008    5760.00  10-09-2012         3005         5001
7    70010    1983.43  10-10-2012         3001         5006
8    70003    2480.40  10-10-2012         3005         5003
9    70012     250.45  06-17-2012         3001         5002
10   70011      75.29  07-08-2012         3005         5007
11   70013    3045.60  04-25-2012         3005         5001
Click me to see the sample solution

13. Write a Pandas program to split the following dataframe into groups based on first column and set other column values into a list of values. Go to the editor
Test Data:

    X   Y   Z
0  10  10  22
1  10  15  20
2  10  11  18
3  20  20  20
4  30  21  13
5  30  12  10
6  10  14   0
Click me to see the sample solution

14. Write a Pandas program to split the following dataframe into groups based on all columns and calculate Groupby value counts on the dataframe. Go to the editor
Test Data:

   id  type     book
0   1    10     Math
1   2    15  English
2   1    11  Physics
3   1    20     Math
4   2    21  English
5   1    12  Physics
6   2    14  English
Output:
book     English  Math  Physics id type                      
1  10          0     1        0
   11          0     0        1
   12          0     0        1
   20          0     1        0
2  14          1     0        0
   15          1     0        0
   21          1     0        0
Click me to see the sample solution

15. Write a Pandas program to split the following dataframe into groups and count unique values of 'value' column. Go to the editor
Test Data:

   id value
0   1     a
1   1     a
2   2     b
3   3  None
4   3     a
5   4     a
6   4  None
7   4     b
Output:
value
a 3
b 2
Click me to see the sample solution

16. Write a Pandas program to split a given dataframe into groups and list all the keys from the GroupBy object. Go to the editor
Test Data:

   school_code class            name date_Of_Birth   age  height  weight 
S1        s001     V  Alberto Franco     15/05/2002   12     173      35   
S2        s002     V    Gino Mcneill     17/05/2002   12     192      32   
S3        s003    VI     Ryan Parkes     16/02/1999   13     186      33   
S4        s001    VI    Eesha Hinton     25/09/1998   13     167      30   
S5        s002     V    Gino Mcneill     11/05/2002   14     151      31   
S6        s004    VI    David Parkes     15/09/1997   12     159      32 
Click me to see the sample solution

17. Write a Pandas program to split a given dataframe into groups and create a new column with count from GroupBy. Go to the editor
Test Data:

  book_name book_type  book_id
0     Book1      Math        1
1     Book2   Physics        2
2     Book3  Computer        3
3     Book4   Science        4
4     Book1      Math        1
5     Book2   Physics        2
6     Book3  Computer        3
7     Book5   English        5 
Click me to see the sample solution

18. Write a Pandas program to split a given dataframe into groups with bin counts. Go to the editor
Test Data:

    ord_no  purch_amt  customer_id  sales_id
0    70001     150.50         3005      5002
1    70009     270.65         3001      5003
2    70002      65.26         3002      5004
3    70004     110.50         3009      5003
4    70007     948.50         3005      5002
5    70005    2400.60         3007      5001
6    70008    5760.00         3002      5005
7    70010    1983.43         3004      5007
8    70003    2480.40         3009      5008
9    70012     250.45         3008      5004
10   70011      75.29         3003      5005
11   70013    3045.60         3002      5001 
Click me to see the sample solution

19. Write a Pandas program to split a given dataframe into groups with multiple aggregations. Go to the editor
Split the following given dataframe by school code, class and get mean, min, and max value of height and age for each value of the school.
Test Data:

   school class            name date_Of_Birth   age  height   weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

20. Write a Pandas program to split a given dataframe into groups and display target column as a list of unique values. Go to the editor
Test Data:

   id  type     book
0   A     1     Math
1   A     1     Math
2   A     1  English
3   A     1  Physics
4   A     2     Math
5   A     2  English
6   B     1  Physics
7   B     1  English
8   B     1  Physics
9   B     2  English
10  B     2  English
Output:
List all unique values in a group:
  id  type                  book
0  A     1  Math,English,Physics
1  A     2          Math,English
2  B     1       Physics,English
3  B     2               English
Click me to see the sample solution

21. Write a Pandas program to split the following dataframe into groups and calculate quarterly purchase amount. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  05-10-2012         3001         5002
1    70009     270.65  09-10-2012         3001         5005
2    70002      65.26  05-10-2012         3005         5001
3    70004     110.50  08-17-2012         3001         5003
4    70007     948.50  10-09-2012         3005         5002
5    70005    2400.60  07-27-2012         3001         5001
6    70008    5760.00  10-09-2012         3005         5001
7    70010    1983.43  10-10-2012         3001         5006
8    70003    2480.40  10-10-2012         3005         5003
9    70012     250.45  06-17-2012         3001         5002
10   70011      75.29  07-08-2012         3005         5007
11   70013    3045.60  04-25-2012         3005         5001
Click me to see the sample solution

22. Write a Pandas program to split the following given dataframe into groups by school code and get mean, min, and max value of age with customized column name for each school. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height   weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

23. Write a Pandas program to split the following given datasets into groups on customer id and calculate the number of customers starting with 'C', the list of all products and the difference of maximum purchase amount and minimum purchase amount. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date customer_id  salesman_id
0    70001     150.50  05-10-2012       C3001         5002
1    70009     270.65  09-10-2012       C3001         5005
2    70002      65.26  05-10-2012       D3005         5001
3    70004     110.50  08-17-2012       D3001         5003
4    70007     948.50  10-09-2012       C3005         5002
5    70005    2400.60  07-27-2012       D3001         5001
6    70008    5760.00  10-09-2012       C3005         5001
7    70010    1983.43  10-10-2012       D3001         5006
8    70003    2480.40  10-10-2012       D3005         5003
9    70012     250.45  06-17-2012       C3001         5002
10   70011      75.29  07-08-2012       D3005         5007
11   70013    3045.60  04-25-2012       D3005         5001
Click me to see the sample solution

24. Write a Pandas program to split the following given datasets into groups on customer_id to summarize purch_amt and calculate percentage of purch_amt in each group. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  05-10-2012         3001         5002
1    70009     270.65  09-10-2012         3001         5005
2    70002      65.26  05-10-2012         3005         5001
3    70004     110.50  08-17-2012         3001         5003
4    70007     948.50  10-09-2012         3005         5002
5    70005    2400.60  07-27-2012         3001         5001
6    70008    5760.00  10-09-2012         3005         5001
7    70010    1983.43  10-10-2012         3001         5006
8    70003    2480.40  10-10-2012         3005         5003
9    70012     250.45  06-17-2012         3001         5002
10   70011      75.29  07-08-2012         3005         5007
11   70013    3045.60  04-25-2012         3005         5001
Click me to see the sample solution

25. Write a Pandas program to split a dataset, group by one column and get mean, min, and max values by group, also change the column name of the aggregated metric. Using the following dataset find the mean, min, and max values of purchase amount (purch_amt) group by customer id (customer_id). Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height   weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

26. Write a Pandas program to split a given dataset, group by two columns and convert other columns of the dataframe into a dictionary with column header as key. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height   weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

27. Write a Pandas program to split a given dataset, group by one column and apply an aggregate function to few columns and another aggregate function to the rest of the columns of the dataframe. Go to the editor
Test Data:

    salesman_id  sale_jan  sale_feb  sale_mar  sale_apr  sale_may  sale_jun  \
0          5002    150.50    250.50    150.50    150.50    130.50    150.50   
1          5005    270.65    170.65    270.65    270.65    270.65    270.65   
2          5001     65.26     15.26     65.26     95.26     65.26     45.26   
3          5003    110.50    110.50    110.50    210.50    310.50    110.50   
4          5002    948.50    598.50    948.50    948.50    948.50    948.50   
5          5001   2400.60   1400.60   2400.60   2400.60   2400.60   3400.60   
6          5001   1760.00   2760.00   5760.00    760.00    760.00   5760.00   
7          5006   2983.43   1983.43   1983.43   1983.43   1983.43    983.43   
8          5003    480.40   2480.40   2480.40   2480.40   2480.40   2480.40   
9          5002   1250.45    250.45    250.45    250.45    250.45    250.45   
10         5007     75.29     75.29     75.29     75.29     75.29     75.29   
11         5001   1045.60   3045.60   3045.60   3045.60   3045.60   3045.60   
    sale_jul  sale_aug  sale_sep  sale_oct  sale_nov  sale_dec  
0     950.50    150.50    150.50    150.50    150.50    150.50  
1     270.65     70.65    270.65    270.65    270.65     70.65  
2      65.26     65.26     65.26     65.26     95.26     65.26  
3     210.50    110.50    110.50    110.50    110.50    110.50  
4     948.50    948.50    948.50    948.50    948.50    948.50  
5    2400.60    400.60    200.60   2400.60   2400.60   2400.60  
6    5760.00   5760.00   5760.00   5760.00   5760.00   5760.00  
7     983.43   1983.43   1983.43   1983.43   1983.43   1983.43  
8    2480.40   2480.40   2480.40   2480.40   2480.40   2480.40  
9     250.45    250.45    250.45    250.45    250.45    250.45  
10     75.29     75.29     75.29     75.29     75.29     75.29  
11   3045.60   3045.60   3045.60   3045.60   3045.60   3045.60  
Click me to see the sample solution

28. Write a Pandas program to split a given dataset, group by one column and remove those groups if all the values of a specific columns are not available. Go to the editor
Test Data:

   school class            name date_Of_Birth   age  height   weight  address
S1   s001     V  Alberto Franco     15/05/2002   12    173      35  street1
S2   s002     V    Gino Mcneill     17/05/2002   12    192      32  street2
S3   s003    VI     Ryan Parkes     16/02/1999   13    186      33  street3
S4   s001    VI    Eesha Hinton     25/09/1998   13    167      30  street1
S5   s002     V    Gino Mcneill     11/05/2002   14    151      31  street2
S6   s004    VI    David Parkes     15/09/1997   12    159      32  street4
Click me to see the sample solution

29. Write a Pandas program to split a given dataset using group by on specified column into two labels and ranges. Go to the editor
Split the group on 'salesman_id',
Ranges:
1) (5001...5006)
2) (5007..5012)
Test Data:

    salesman_id  sale_jan
0          5001    150.50
1          5002    270.65
2          5003     65.26
3          5004    110.50
4          5005    948.50
5          5006   2400.60
6          5007   1760.00
7          5008   2983.43
8          5009    480.40
9          5010   1250.45
10         5011     75.29
11         5012   1045.60

Click me to see the sample solution

30. Write a Pandas program to split the following dataset using group by on first column and aggregate over multiple lists on second column. Go to the editor
Test Data:

  student_id         marks
0       S001  [88, 89, 90]
1       S001  [78, 81, 60]
2       S002  [84, 83, 91]
3       S002  [84, 88, 91]
4       S003  [90, 89, 92]
5       S003  [88, 59, 90]
Output:
student_id
S001 [83.0, 85.0, 75.0]
S002 [84.0, 85.5, 91.0]
S003 [89.0, 74.0, 91.0]
Click me to see the sample solution

31. Write a Pandas program to split the following dataset using group by on 'salesman_id' and find the first order date for each group. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3005         5002
1    70009     270.65  2012-09-10         3001         5005
2    70002      65.26  2012-10-05         3002         5001
3    70004     110.50  2012-08-17         3009         5003
4    70007     948.50  2012-09-10         3005         5002
5    70005    2400.60  2012-07-27         3007         5001
6    70008    5760.00  2012-09-10         3002         5001
7    70010    1983.43  2012-10-10         3004         5004
8    70003    2480.40  2012-10-10         3009         5003
9    70012     250.45  2012-06-27         3008         5002
10   70011      75.29  2012-08-17         3003         5004
11   70013    3045.60  2012-04-25         3002         5001
Click me to see the sample solution

32. Write a Pandas program to split a given dataset using group by on multiple columns and drop last n rows of from each group. Go to the editor
Test Data:

    ord_no  purch_amt    ord_date  customer_id  salesman_id
0    70001     150.50  2012-10-05         3002         5002
1    70009     270.65  2012-09-10         3001         5003
2    70002      65.26  2012-10-05         3001         5001
3    70004     110.50  2012-08-17         3003         5003
4    70007     948.50  2012-09-10         3002         5002
5    70005    2400.60  2012-07-27         3002         5001
6    70008    5760.00  2012-09-10         3001         5001
7    70010    1983.43  2012-10-10         3004         5003
8    70003    2480.40  2012-10-10         3003         5003
9    70012     250.45  2012-06-27         3002         5002
10   70011      75.29  2012-08-17         3003         5003
11   70013    3045.60  2012-04-25         3001         5001
Click me to see the sample solution

Python Code Editor:


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Python: Tips of the Day

Python: Dictionary As Arguments Using **arguments

It allows you to pass varying number of keyword arguments to a function.
You can also pass in dictionary values as keyword arguments:

def myfunc(arguments):
  return arguments['key']