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Pandas: Split the specified given dataframe into groups based on single column and multiple columns and find the size of the grouped data

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-5 with Solution

Write a Pandas program to split the following given dataframe into groups based on single column and multiple columns. Find the size of the grouped data.

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('\nSplit the said data on school_code wise:')
grouped_single = student_data.groupby(['school_code'])
print("Size of the grouped data - single column")
print(grouped_single.size())
print('\nSplit the said data on school_code and class wise:')

grouped_mul = student_data.groupby(['school_code', 'class'])
print("Size of the grouped data - multiple columns:")
print(grouped_mul.size())

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 said data on school_code wise:
Size of the grouped data - single column
school_code
s001    2
s002    2
s003    1
s004    1
dtype: int64

Split the said data on school_code and class wise:
Size of the grouped data - multiple columns:
school_code  class
s001         V        1
             VI       1
s002         V        2
s003         VI       1
s004         VI       1
dtype: int64

Python Code Editor:


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Python: Tips of the Day

Negative Indexing:

In Python you can use negative indexing. While positive index starts with 0, negative index starts with -1.

name="Welcome"
print(name[0])
print(name[-1])
print(name[0:3])
print(name[-1:-4:-1])

Output:

W
e
Wel
emo