What I have

I have a pandas frame like this:

df1 = pd.DataFrame({
    'date': ['31-05-2017', '31-05-2017', '31-05-2017', '31-05-2017', '01-06-2017', '01-06-2017'],
    'tag':     ['A', 'B', 'B', 'B', 'A', 'A'],
    'metric1': [0, 0, 0, 1, 1, 1],
    'metric2': [0, 1, 1, 0, 1, 0]
})


df2 = pd.DataFrame({
    'date': ['31-05-2017', '31-05-2017', '01-06-2017'],
    'tag':     ['A', 'B', 'A'],
    'metric3': [25, 3, 7,]
})

What I want

1) I want to sum metricand metric_2 per each combination of date and tag

2) compute the percentage of entries being 1 in metric_2

3) merge grouped df1 with df2 so that I have metric_3 for each dateand tag

date       | tag | metric1_sum | metric2_sum | metric2_percentage| metric 3
-----------|-----|-------------|-------------|-------------------|---------
31-05-2017 | A   | 0           | 0           | 0                 | 25
31-05-2017 | B   | 1           | 2           | 0.667             | 3
01-06-2017 | A   | 1           | 0           | 0.5               | 7

Attempts

(1) group and sum works

>>> g = df1.groupby(['date', 'tag']).agg(sum)
>>> g
                metric1  metric2
date       tag                  
01-06-2017 A          2        1
31-05-2017 A          0        0
           B          1        2

(2) calculating the percentage works but adding it as a column does not

I used the method posted here to calculate the percentage.

>>> g2 = df1.groupby(['date', 'tag']).agg({'metric2': 'sum'})
>>> g2.groupby(level=0).apply(lambda x: x/float(x.sum()))
                metric2
date       tag         
01-06-2017 A        1.0
31-05-2017 A        0.0
           B        1.0

But, how can I now assign this grouped metric2 to a column metric2_percentage in my groups g or my df1?

(3) unsuccessful merge

Merging with the group apparently does not work:

>>> pd.merge(g, df2, how='left', on=['date', 'tag'])
KeyError: 'date'

How can I then reduce df1 to one row per group so that I can merge it with df2?