Pandas: Split a given dataset, group by two columns and convert other columns of the dataframe into a dictionary with column header as key
26. Grouping by Two Columns and Converting to Dictionary
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'}]}]
For more Practice: Solve these Related Problems:
- Write a Pandas program to group a dataframe by two columns and then convert the remaining columns into a dictionary with column headers as keys.
- Write a Pandas program to split the dataframe by two keys and then create a dictionary for each group containing the aggregated values.
- Write a Pandas program to group by school and class, and then aggregate the other columns into a dictionary for each group.
- Write a Pandas program to group the dataframe by multiple columns and then use the apply() method to output each group as a dictionary.
Python Code Editor:
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