在 Pandas groupby object 中获取比率

Question

I have a dataframe that looks like this:

I want to create another column called "engaged_percent" for each state which is basically the number of unique engaged_count divided by the user_count of each particular state.

I tried doing the following:

def f(x):
    engaged_percent = x['engaged_count'].nunique()/x['user_count']
    return pd.Series({'engaged_percent': engaged_percent})

by = df3.groupby(['user_state']).apply(f)
by

But it gave me the following result:

What I want is something like this:

user_state        engaged_percent
---------------------------------
California           2/21 = 0.09
Florida              2/7 =  0.28

I think my approach is correct, however I am not sure why my result shows up like the one seen in the second picture.

Any help would be much appreciated! Thanks in advance!

Answer 1

How about:

user_count=df3.groupby('user_state')['user_count'].mean()
#(or however you think a value for each state should be calculated)

engaged_unique=df3.groupby('user_state')['engaged_count'].nunique()

engaged_pct=engaged_unique/user_count

(you could also do this in one line in a bunch of different ways)

Your original solution was almost fine except that you were dividing a value by the entire user count series. So you were getting a Series instead of a value. You could try this slight variation:

def f(x):
    engaged_percent = x['engaged_count'].nunique()/x['user_count'].mean()
    return engaged_percent

by = df3.groupby(['user_state']).apply(f)
by

Answer 2

I would just use groupby and apply directly

df3['engaged_percent'] = df3.groupby('user_state')
                            .apply(lambda s: s.engaged_count.nunique()/s.user_count).values

Demo

>>> df3
    engaged_count  user_count  user_state
0               3          21  California
1               3          21  California
2               3          21  California
...
19              4           7     Florida
20              4           7     Florida
21              4           7     Florida

>>> df3['engaged_percent'] = df3.groupby('user_state').apply(lambda s: s.engaged_count.nunique()/s.user_count).values

>>> df3
    engaged_count  user_count  user_state  engaged_percent
0               3          21  California         0.095238
1               3          21  California         0.095238
2               3          21  California         0.095238
...
19              4           7     Florida         0.285714
20              4           7     Florida         0.285714
21              4           7     Florida         0.285714

Answer 3

titanic.groupby('Sex')['Fare'].mean()

you can try this example just put your example in that