Pandas Example – Write a Pandas program to split a given dataset, group by one column and remove those groups if all the values of a specific columns are not available

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(Python Example for Beginners)

 

Write a Pandas program to split a given dataset, group by one column and remove those groups if all the values of a specific columns are not available.

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)

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],
    'weight': [173, 192, 186, 167, 151, 159],
    'height': [35, None, 33, 30, None, 32]},
    index=['S1', 'S2', 'S3', 'S4', 'S5', 'S6'])

print("Original DataFrame:")
print(df)

print("nGroup by one column and remove those groups if all the values of a specific columns are not available:")
result = df[(~df['height'].isna()).groupby(df['school_code']).transform('any')]
print(result)

Sample Output:

Original DataFrame:
   school_code class            name date_Of_Birth   age  weight  height
S1        s001     V  Alberto Franco     15/05/2002   12     173    35.0
S2        s002     V    Gino Mcneill     17/05/2002   12     192     NaN
S3        s003    VI     Ryan Parkes     16/02/1999   13     186    33.0
S4        s001    VI    Eesha Hinton     25/09/1998   13     167    30.0
S5        s002     V    Gino Mcneill     11/05/2002   14     151     NaN
S6        s004    VI    David Parkes     15/09/1997   12     159    32.0

Group by one column and remove those groups if all the values of a specific columns are not available:
   school_code class            name date_Of_Birth   age  weight  height
S1        s001     V  Alberto Franco     15/05/2002   12     173    35.0
S3        s003    VI     Ryan Parkes     16/02/1999   13     186    33.0
S4        s001    VI    Eesha Hinton     25/09/1998   13     167    30.0
S6        s004    VI    David Parkes     15/09/1997   12     159    32.0

 

 

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