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Pandas: Split a given dataset, group by one column and remove those groups if all the values of a specific columns are not available

Pandas Grouping and Aggregating: Split-Apply-Combine Exercise-28 with Solution

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

Python Code Editor:


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Previous: Write a Pandas program to split a given dataset, group by one column and apply an aggregate function to few columns and another aggregate function to the rest of the columns of the dataframe.
Next: Write a Pandas program to split a given dataset using group by on specified column into two labels and ranges.

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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