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

Last update on May 28 2022 13:37:33 (UTC/GMT +8 hours)

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.

Next: 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.

Python: Tips of the Day

Find current directory and file's directory:

To get the full path to the directory a Python file is contained in, write this in that file:

import os dir_path = os.path.dirname(os.path.realpath(__file__))

(Note that the incantation above won't work if you've already used os.chdir() to change your current working directory, since the value of the __file__ constant is relative to the current working directory and is not changed by an os.chdir() call.)

To get the current working directory use

import os cwd = os.getcwd()

Documentation references for the modules, constants and functions used above:

The os and os.path modules.

The __file__ constant

os.path.realpath(path) (returns "the canonical path of the specified filename, eliminating any symbolic links encountered in the path")

os.path.dirname(path) (returns "the directory name of pathname path")

os.getcwd() (returns "a string representing the current working directory")

os.chdir(path) ("change the current working directory to path")

Ref: https://bit.ly/3fy0R6m