Pandas: Split a given dataframe into groups and list all the keys from the GroupBy object

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-16 with Solution

Write a Pandas program to split a given dataframe into groups and list all the keys from the GroupBy object.

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 

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)
print("\nSplit the data on school_code:");
gp = df.groupby('school_code')
print("\nList of all the keys:")
print(gp.groups.keys())

Sample Output:

Original DataFrame:
   school_code class            name   ...    height  weight  address
S1        s001     V  Alberto Franco   ...       173      35  street1
S2        s002     V    Gino Mcneill   ...       192      32  street2
S3        s003    VI     Ryan Parkes   ...       186      33  street3
S4        s001    VI    Eesha Hinton   ...       167      30  street1
S5        s002     V    Gino Mcneill   ...       151      31  street2
S6        s004    VI    David Parkes   ...       159      32  street4

[6 rows x 8 columns]

Split the data on school_code:

List of all the keys:
dict_keys(['s001', 's002', 's003', 's004'])

Python Code Editor:

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