Pandas combine two group by's, filter and merge the groups(counts)

Question

I have a dataframe that I need to combine two different groupbys with one of them filtered.

 ID     EVENT      SUCCESS
 1       PUT          Y
 2       POST         Y
 2       PUT          N
 1       DELETE       Y 

This table below is how I would like the data to look like. Firstly grouping the 'EVENT' counts, the second is to count the amount of Successes ('Y') per ID

ID  PUT   POST  DELETE SUCCESS
 1   1     0       1      2
 2   1     1       0      1

I've tried a few techniques and the closet I've found is two separate methods which yield the following

group_df = df.groupby(['ID', 'EVENT']) count_group_df = group_df.size().unstack()

Which yields the following for the 'EVENT' counts

ID  PUT   POST  DELETE
 1   1     0       1      
 2   1     1       0      

For the Successes with filters, i dont know whether I can join this to the first set on 'ID'

 df_success = df.loc[df['SUCCESS'] == 'Y', ['ID', 'SUCCESS']]
 count_group_df_2 = df_success.groupby(['ID', 'SUCCESS'])


ID  SUCCESS
1      2
2      1

I need to combine these somehow?

Additionally I'd also like to merge the counts two of the 'EVENT''s for example PUT's and POST's into one column.

Solution 1

Use concat for merge them together:

df1 = df.groupby(['ID', 'EVENT']).size().unstack(fill_value=0)
df_success = (df['SUCCESS'] == 'Y').groupby(df['ID']).sum().astype(int)
df = pd.concat([df1, df_success],axis=1)
print (df)
    DELETE  POST  PUT  SUCCESS
ID                            
1        1     0    1        2
2        0     1    1        1

Another solution with value_counts:

df1 = df.groupby(['ID', 'EVENT']).size().unstack(fill_value=0)
df_success = df.loc[df['SUCCESS'] == 'Y', 'ID'].value_counts().rename('SUCCESS')
df = pd.concat([df1, df_success],axis=1)
print (df)
    DELETE  POST  PUT  SUCCESS
ID                            
1        1     0    1        2
2        0     1    1        1

Last is possible convert index to column and remove columns name ID by reset_index + rename_axis:

df = df.reset_index().rename_axis(None, axis=1)
print (df)
   ID  DELETE  POST  PUT  SUCCESS
0   1       1     0    1        2
1   2       0     1    1        1

Solution 2

pandas

pd.get_dummies(df.EVENT) \ 
  .assign(SUCCESS=df.SUCCESS.eq('Y').astype(int)) \
  .groupby(df.ID).sum().reset_index()

   ID  DELETE  POST  PUT  SUCCESS
0   1       1     0    1        2
1   2       0     1    1        1

numpy and pandas

f, u = pd.factorize(df.EVENT.values)
n = u.size
d = np.eye(n)[f]
s = (df.SUCCESS.values == 'Y').astype(int)
d1 = pd.DataFrame(
    np.column_stack([d, s]),
    df.index, np.append(u, 'SUCCESS')
)
d1.groupby(df.ID).sum().reset_index()

   ID  DELETE  POST  PUT  SUCCESS
0   1       1     0    1        2
1   2       0     1    1        1

---

Timing
small data

%%timeit
f, u = pd.factorize(df.EVENT.values)
n = u.size
d = np.eye(n)[f]
s = (df.SUCCESS.values == 'Y').astype(int)
d1 = pd.DataFrame(
    np.column_stack([d, s]),
    df.index, np.append(u, 'SUCCESS')
)
d1.groupby(df.ID).sum().reset_index()
1000 loops, best of 3: 1.32 ms per loop

%%timeit
df1 = df.groupby(['ID', 'EVENT']).size().unstack(fill_value=0)
df_success = (df['SUCCESS'] == 'Y').groupby(df['ID']).sum().astype(int)
pd.concat([df1, df_success],axis=1).reset_index()
100 loops, best of 3: 3.3 ms per loop

%%timeit
df1 = df.groupby(['ID', 'EVENT']).size().unstack(fill_value=0)
df_success = df.loc[df['SUCCESS'] == 'Y', 'ID'].value_counts().rename('SUCCESS')
pd.concat([df1, df_success],axis=1).reset_index()
100 loops, best of 3: 3.28 ms per loop

%timeit pd.get_dummies(df.EVENT).assign(SUCCESS=df.SUCCESS.eq('Y').astype(int)).groupby(df.ID).sum().reset_index()
100 loops, best of 3: 2.62 ms per loop

large data

df = pd.DataFrame(dict(
        ID=np.random.randint(100, size=100000),
        EVENT=np.random.choice('PUT POST DELETE'.split(), size=100000),
        SUCCESS=np.random.choice(list('YN'), size=100000)
    ))

%%timeit
f, u = pd.factorize(df.EVENT.values)
n = u.size
d = np.eye(n)[f]
s = (df.SUCCESS.values == 'Y').astype(int)
d1 = pd.DataFrame(
    np.column_stack([d, s]),
    df.index, np.append(u, 'SUCCESS')
)
d1.groupby(df.ID).sum().reset_index()
100 loops, best of 3: 10.8 ms per loop

%%timeit
df1 = df.groupby(['ID', 'EVENT']).size().unstack(fill_value=0)
df_success = (df['SUCCESS'] == 'Y').groupby(df['ID']).sum().astype(int)
pd.concat([df1, df_success],axis=1).reset_index()
100 loops, best of 3: 17.7 ms per loop

%%timeit
df1 = df.groupby(['ID', 'EVENT']).size().unstack(fill_value=0)
df_success = df.loc[df['SUCCESS'] == 'Y', 'ID'].value_counts().rename('SUCCESS')
pd.concat([df1, df_success],axis=1).reset_index()
100 loops, best of 3: 17.4 ms per loop

%timeit pd.get_dummies(df.EVENT).assign(SUCCESS=df.SUCCESS.eq('Y').astype(int)).groupby(df.ID).sum().reset_index()
100 loops, best of 3: 16.8 ms per loop