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Pandas: Split a given dataset, group by two columns and convert other columns of the dataframe into a dictionary with column header as key

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-26 with Solution

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.

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

Sample Solution:

Python Code :

import pandas as pd
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
df = pd.DataFrame({
    'school_code': ['s001','s002','s003','s001','s002','s004'],
    'class': ['V', 'V', 'VI', 'VI', 'V', 'VI'],
    'name': ['Alberto Franco','Gino Mcneill','Ryan Parkes', 'Eesha Hinton', 'Gino Mcneill', 'David Parkes'],
    'date_Of_Birth ': ['15/05/2002','17/05/2002','16/02/1999','25/09/1998','11/05/2002','15/09/1997'],
    'age': [12, 12, 13, 13, 14, 12],
    'height': [173, 192, 186, 167, 151, 159],
    'weight': [35, 32, 33, 30, 31, 32],
    'address': ['street1', 'street2', 'street3', 'street1', 'street2', 'street4']},
    index=['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])
print("Original DataFrame:")
print(df)
dict_data_list = list()

for gg, dd in df.groupby(['school_code','class']):
    group = dict(zip(['school_code','class'], gg))
    ocolumns_list = list()
    for _, data in dd.iterrows():
        data = data.drop(labels=['school_code','class'])
        ocolumns_list.append(data.to_dict())
    group['other_columns'] = ocolumns_list
    dict_data_list.append(group)

print(dict_data_list) 

Sample Output:

Original DataFrame:
   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   

    address  
S1  street1  
S2  street2  
S3  street3  
S4  street1  
S5  street2  
S6  street4  
[{'school_code': 's001', 'class': 'V', 'other_columns': [{'name': 'Alberto Franco', 'date_Of_Birth ': '15/05/2002', 'age': 12, 'height': 173, 'weight': 35, 'address': 'street1'}]}, 
{'school_code': 's001', 'class': 'VI', 'other_columns': [{'name': 'Eesha Hinton', 'date_Of_Birth ': '25/09/1998', 'age': 13, 'height': 167, 'weight': 30, 'address': 'street1'}]},
{'school_code': 's002', 'class': 'V', 'other_columns': [{'name': 'Gino Mcneill', 'date_Of_Birth ': '17/05/2002', 'age': 12, 'height': 192, 'weight': 32, 'address': 'street2'}, {'name': 'Gino Mcneill', 'date_Of_Birth ': '11/05/2002', 'age': 14, 'height': 151, 'weight': 31, 'address': 'street2'}]},
{'school_code': 's003', 'class': 'VI', 'other_columns': [{'name': 'Ryan Parkes', 'date_Of_Birth ': '16/02/1999', 'age': 13, 'height': 186, 'weight': 33, 'address': 'street3'}]},
{'school_code': 's004', 'class': 'VI', 'other_columns': [{'name': 'David Parkes', 'date_Of_Birth ': '15/09/1997', 'age': 12, 'height': 159, 'weight': 32, 'address': 'street4'}]}]

Python Code Editor:


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Previous: 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).
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2020-Nov-30