How do I Sample each group from a pandas data frame at different rates

Question

I have a data frame containing information about a population that i wish to generate a sample from. I also have a dataframe sample_info that details how many units of each group in the population dataframe I need in my sample. I have developed some code that achieves what i need but it runs slower than i would like given the large datasets i am working with.

Is there a way to group the population frame and apply sampling to the groups rather than looping through them as i have done below?

import pandas as pd

population = pd.DataFrame([[1,True],[1,False],[1,False],[2,True],[2,True],[2,False],[2, True]], columns = ['Group ID','Response'])

    Group ID    Response
0   1           True
1   1           False
2   1           False
3   2           True
4   2           True
5   2           False
6   2           True

sample_info = pd.DataFrame([[1,5],[2,6]], columns = ['Group ID','Sample Size'])

output = pd.DataFrame(columns = ['Group ID','Response'])

    Group ID    Sample Size
0   1           5
1   2           6


for index, row in sample_info.iterrows():    
        output = output.append(population.loc[population['Group ID'] == row['Group ID']].sample(n=row['Sample Size'], replace = True)) 

I couldn't figure out to bring in the sample size information using group-by and apply as suggested in Pandas: sample each group after groupby

Answer 1

Convert sample_info to dictionary. Group population by Group ID. Pass the sample size values to DataFrame.sample using the dictionary.

mapper = sample_info.set_index('Group ID')['Sample Size'].to_dict()

population.groupby('Group ID').apply(lambda x: x.sample(n=mapper.get(x.name))).reset_index(drop = True)

Answer 2

I am not sure about the speed but sample the index looks like save the memory at least

d=population.groupby('Group ID').groups
a=np.concatenate([np.random.choice(d[x],y) for x, y in zip(sample_info['Group ID'],sample_info['Sample Size']) ])
population.loc[a]
Out[83]: 
   Group ID  Response
1         1     False
1         1     False
2         1     False
0         1      True
1         1     False
3         2      True
5         2     False
3         2      True
4         2      True
5         2     False
5         2     False