根据多个条件从 Pandas DataFrame 中随机选择行

Question

I am trying to use Python to sample data to QA. My criteria is to audit 2 individuals and then a random sample of their respective vendors based on a risk level. So I need a script that basically says:

If or While the PM Owner is Alex, then randomly select 1 (as long as 1 exists) each of Critical Risk, High Risk, Medium Risk and Low Risk.

WHILE df['PM Owner'] == 'Alex':
    IF df['Risk Tier'] == 'Critical':
        df['Risk Tier'].sample()

I get this error:

The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()`

Then I need to repeat the loop for the other individual.

I have tried if and while loops without the success I need.

My columns for this are 'PM Owner' and 'Risk Tier' .

Answer 1

I am not sure did I get the question right or not, but at least this answer will help other to give you a answer If this is not what you are looking for, please give me shot

import pandas as pd
#your dataframe  
maindf = {'PM Owner': ['A', 'B','C','A','E','F'], 'Risk Tier': [1,3,1,1,1,2],'sam' :['A0','B0','C0','D0','E0','F0']}
Maindf = pd.DataFrame(data=maindf)
 

#what you are looking for
filterdf = {'PM Owner': ['A'  ], 'Risk Tier': [ 1 ]}
Filterdf = pd.DataFrame(data=filterdf)

 
#Filtering
NewMaindf= (Maindf[Maindf[['PM Owner','Risk Tier']].astype(str).sum(axis = 1).isin(
                Filterdf[['PM Owner','Risk Tier']].astype(str).sum(axis = 1))])
#Just one sample
print( (NewMaindf).sample())
#whole dataset after filtering
print( (NewMaindf) )

Result :

 PM Owner  Risk Tier sam
3        A          1  D0
  PM Owner  Risk Tier sam
0        A          1  A0
3        A          1  D0

Answer 2

The conditions you specified can match many rows, that is why you got the error also suggesting you to use one of the functions that reduces the results to a single value. However the conditions in their present form can actually be used as masks, so it may be possible to draw the samples matching the criteria simply by narrowing down the scope, ie.:

df.loc[(df['PM Owner'] == 'Alex') & (df['Risk Tier'] == 'Critical'), 'Risk Tier'].sample()

If you need to loop through every PM Owner you can do so:

for pm_owner in df['PM Owner']:
    sample = df.loc[(df['PM Owner'] == pm_owner) & (df['Risk Tier'] == 'Critical'), 'Risk Tier'].sample()