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sample a dataframe based on priority

.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty height:90px;width:728px;box-sizing:border-box;

0

I have a dataframe like

import random
import pandas as pd
col1 = random.choices([1,2,3,4,5],k=50)
col2 = random.choices(['A','B','C'],k=50)
df = pd.DataFrame('values':col1,'priority':col2)

I want to pull a sample of 25, but if 25 have priority 'A', I want those 25. if 30 have priority 'A', I want 25 chosen from that 30. if 20 have priority 'A', I want those 20, and a random 5 from the 30 with priority 'B'. If 10 have priority 'A' and 10 have priority 'B', I want all 10 'A' and all 10 'B', and 5 random 'C'.

The only way I can think to do this is split the dataset into 3, and then use if statements like

if len(df_A) ==25:
 output = df_A
elif len(df_A) >25:
 output = df_A.sample(n=25)
elif len(df_A) + len(df_B) == 25:
 output = pd.concat([df_A,df_b])

and so on.
Is there a better way to do this? possibly something that scales up to a larger number of priority groups?

python pandas

share|improve this question

asked Mar 8 at 21:52

PhilPhil

274

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I just had the idea to randomize the dataframe, then use sort and take the top 25 of the dataframe. I think this will work.
  
  – Phil
  Mar 8 at 21:57
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  df.sample(frac=1).sort_values(by='priority').head(25)
  
  – user3483203
  Mar 8 at 21:57
  

  
  
  
  

add a comment | 

0

I have a dataframe like

import random
import pandas as pd
col1 = random.choices([1,2,3,4,5],k=50)
col2 = random.choices(['A','B','C'],k=50)
df = pd.DataFrame('values':col1,'priority':col2)

I want to pull a sample of 25, but if 25 have priority 'A', I want those 25. if 30 have priority 'A', I want 25 chosen from that 30. if 20 have priority 'A', I want those 20, and a random 5 from the 30 with priority 'B'. If 10 have priority 'A' and 10 have priority 'B', I want all 10 'A' and all 10 'B', and 5 random 'C'.

The only way I can think to do this is split the dataset into 3, and then use if statements like

if len(df_A) ==25:
 output = df_A
elif len(df_A) >25:
 output = df_A.sample(n=25)
elif len(df_A) + len(df_B) == 25:
 output = pd.concat([df_A,df_b])

and so on.
Is there a better way to do this? possibly something that scales up to a larger number of priority groups?

python pandas

share|improve this question

asked Mar 8 at 21:52

PhilPhil

274

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  I just had the idea to randomize the dataframe, then use sort and take the top 25 of the dataframe. I think this will work.
  
  – Phil
  Mar 8 at 21:57
  

  
  
  
  


• 
  
  
  

  
  1
  

  
  
  
  
  
  

  
  
  df.sample(frac=1).sort_values(by='priority').head(25)
  
  – user3483203
  Mar 8 at 21:57
  

  
  
  
  

add a comment | 

I have a dataframe like

import random
import pandas as pd
col1 = random.choices([1,2,3,4,5],k=50)
col2 = random.choices(['A','B','C'],k=50)
df = pd.DataFrame('values':col1,'priority':col2)

I want to pull a sample of 25, but if 25 have priority 'A', I want those 25. if 30 have priority 'A', I want 25 chosen from that 30. if 20 have priority 'A', I want those 20, and a random 5 from the 30 with priority 'B'. If 10 have priority 'A' and 10 have priority 'B', I want all 10 'A' and all 10 'B', and 5 random 'C'.

The only way I can think to do this is split the dataset into 3, and then use if statements like

if len(df_A) ==25:
 output = df_A
elif len(df_A) >25:
 output = df_A.sample(n=25)
elif len(df_A) + len(df_B) == 25:
 output = pd.concat([df_A,df_b])

and so on.
Is there a better way to do this? possibly something that scales up to a larger number of priority groups?

python pandas

share|improve this question

asked Mar 8 at 21:52

PhilPhil

274

import random
import pandas as pd
col1 = random.choices([1,2,3,4,5],k=50)
col2 = random.choices(['A','B','C'],k=50)
df = pd.DataFrame('values':col1,'priority':col2)

if len(df_A) ==25:
 output = df_A
elif len(df_A) >25:
 output = df_A.sample(n=25)
elif len(df_A) + len(df_B) == 25:
 output = pd.concat([df_A,df_b])

share|improve this question

asked Mar 8 at 21:52

PhilPhil

274

share|improve this question

asked Mar 8 at 21:52

PhilPhil

274

asked Mar 8 at 21:52

PhilPhil

274

asked Mar 8 at 21:52

PhilPhil

274