Pandas: Split a specified dataframe into groups by school code and get mean, min, and max value of age with customized column name for each school

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-22 with Solution

Write a Pandas program to split the following dataframe into groups by school code and get mean, min, and max value of age with customized column name for each 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

Sample Solution:

Python Code :

import pandas as pd
pd.set_option('display.max_rows', None)
#pd.set_option('display.max_columns', None)
student_data = 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(student_data)
print('\nMean, min, and max value of age for each school with customized column names:')
grouped_single = student_data.groupby('school_code').agg(Age_Mean = ('age','mean'),Age_Max=('age',max),Age_Min=('age',min))
print(grouped_single)

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]

Mean, min, and max value of age for each school with customized column names:
             Age_Mean  Age_Max  Age_Min
school_code                            
s001             12.5       13       12
s002             13.0       14       12
s003             13.0       13       13
s004             12.0       12       12

Note: Run on Spyder Python 3.7.1

Python Code Editor:

Have another way to solve this solution? Contribute your code (and comments) through Disqus.

Previous: Write a Pandas program to split the following dataframe into groups and calculate quarterly purchase amount.