Best way(run-time) to aggregate (calculate ratio o

问题:

I'm trying to identify ratio of approved applications(identified by flag '1' and if not then '0') to total applications for each person(Cust_ID). I have achieved this logic by the following code but it takes about 10 mins to compute this for 1.6 M records. Is there a faster to perform the same operation?

# Finding ratio of approved out of total applications
df_approved_ratio = df.groupby('Cust_ID').apply(lambda x:x['STATUS_Approved'].sum()/len(x))

回答1:

I think need aggregate by mean:

df = pd.DataFrame({'STATUS_Approved':[0,1,0,0,1,1],
                   'Cust_ID':list('aaabbb')})

print (df)
   STATUS_Approved Cust_ID
0                0       a
1                1       a
2                0       a
3                0       b
4                1       b
5                1       b

df_approved_ratio = df.groupby('Cust_ID')['STATUS_Approved'].mean()
print (df_approved_ratio)
Cust_ID
a    0.333333
b    0.666667
Name: STATUS_Approved, dtype: float64

print (df.groupby('Cust_ID').apply(lambda x:x['STATUS_Approved'].sum()/len(x)))
Cust_ID
a    0.333333
b    0.666667
Name: STATUS_Approved, dtype: float64