split data such that a a categorical value is either in train or test python

I have a data set (df) as follows

Company Col1 Col2 Output     
AB       10    20   1
AB       20    22   1
AB       14    12   0
XZ       33    22   1
XZ       43    62   0

I want to train_test_split the data such that if a company is in the test set, it should not be in the training set at all. By which I mean if the first row ( AB, 10, 20,1) is in the test set, the second row ( AB, 20,22,1) should also be in the test set. I know stratify would stratify=df[["Name"] would do the exact opposite of what I want. Is there any built in function to do as such?

P.S. Company column is string

>Solution :

This might be a little verbose and not a generic function, but this approach might work for you:

counts = df.groupby("Company").count()["Output"]
frac = 0.8 # Fraction of the training table, will only be approximated
train_companies = []
i = 0 
c = 0 
total_count = counts.values.sum()
train_count = total_count * frac 
while(c < train_count): 
  train_companies.append(counts.index[i])
  c = c + counts.values[i]
  i = i + 1 
  c = c + counts.values[i]

df_train = df[df['Company'].isin(train_companies)]
df_test = df[~df['Company'].isin(train_companies)]