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Pandas: Split the specified given dataframe into groups based on school code and call a specific group with the name of the group

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-6 with Solution

Write a Pandas program to split the following given dataframe into groups based on school code and call a specific group with the name of the group.

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 = student_data.groupby(['school_code'])
print("Call school code 's001':")
print(grouped.get_group('s001'))
print("\nCall school code 's004':")
print(grouped.get_group('s004'))

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:
Call school code 's001':
   school_code class            name   ...    height  weight  address
S1        s001     V  Alberto Franco   ...      173      35  street1
S4        s001    VI    Eesha Hinton   ...      167      30  street1

[2 rows x 8 columns]

Call school code 's004':
   school_code class          name   ...    height  weight  address
S6        s004    VI  David Parkes   ...      159      32  street4

[1 rows x 8 columns]

Python Code Editor:


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Previous: 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.
Next: Write a Pandas program to split a dataset, group by one column and get mean, min, and max values by group. Using the following dataset find the mean, min, and max values of purchase amount (purch_amt) group by customer id (customer_id).

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Python: Tips of the Day

Python: Time library

Time library provides lots of time related functions and methods and is good to know whether you're developing a website or apps and games or working with data science or trading financial markets. Time is essential in most development pursuits and Python's standard time library comes very handy for that.

Let's check out a few simple examples:

moment=time.strftime("%Y-%b-%d__%H_%M_%S",time.localtime())

import time
time_now=time.strftime("%H:%M:%S",time.localtime())
print(time_now)
date_now=time.strftime("%Y-%b-%d",time.localtime())
print(date_now)

Output:

11:36:34
2020-Nov-30