Sampling one record per unique value (pandas, python)

Question

I work with python-pandas dataframes, and I have a large dataframe containing users and their data. Each user can have multiple rows. I want to sample 1-row per user. My current solution seems not efficient:

df1 = pd.DataFrame({'User': ['user1', 'user1', 'user2', 'user3', 'user2', 'user3'],
                 'B': ['B', 'B1', 'B2', 'B3','B4','B5'],
                 'C': ['C', 'C1', 'C2', 'C3','C4','C5'],
                 'D': ['D', 'D1', 'D2', 'D3','D4','D5'],
                 'E': ['E', 'E1', 'E2', 'E3','E4','E5']},
                 index=[0, 1, 2, 3,4,5])

df1
>>  B   C   D   E   User
0   B   C   D   E   user1
1   B1  C1  D1  E1  user1
2   B2  C2  D2  E2  user2
3   B3  C3  D3  E3  user3
4   B4  C4  D4  E4  user2
5   B5  C5  D5  E5  user3

userList = list(df1.User.unique())
userList
> ['user1', 'user2', 'user3']

The I loop over unique users list and sample one row per user, saving them to a different dataframe

usersSample = pd.DataFrame() # empty dataframe, to save samples
for i in userList:
    usersSample=usersSample.append(df1[df1.User == i].sample(1)) 

> usersSample   
B   C   D   E   User
0   B   C   D   E   user1
4   B4  C4  D4  E4  user2
3   B3  C3  D3  E3  user3

Is there a more efficient way of achieving that? I'd really like to: 1) avoid appending to dataframe usersSample. This is gradually growing object and it seriously kills performance. And 2) avoid looping over users one at a time. Is there a way to sample 1-per-user more efficiently?

Answer 1

This is what you want:

df1.groupby('User').apply(lambda df: df.sample(1))

Without the extra index:

df1.groupby('User', group_keys=False).apply(lambda df: df.sample(1))