In pandas is there something like a GroupBy.get_group, but with an optional default value?

Question

I've got a DataFrame df, which I've 'groupby'ed. I'm looking for a function which is similar to get_group(name) except that rather than throwing a KeyError if the name doesn't exist, returns an empty DataFrame (or some other value), similar to how dict.get works:

g = df.groupby('x')

# doesn't work, but would be nice:
i = g.get_group(1, default=[])

# does work, but is hard to read:
i = g.obj.take(g.indices.get(1, []), g.axis)

Is there already a function which provides this?

Edit:

In many ways, the GroupBy object is represented by a dict (.indicies, .groups), and this 'get with default' functionality was core enough to the concept of a dict that it is included in the Python language itself. It seemed that if a dict-like thing doesn't have a get with default, maybe I'm not understanding it correctly? Why would a dict like thing not have a 'get with default'?

An abbreviated example of what I want to do is:

df1_bymid = df1.groupby('mid')
df2_bymid = df2.groupby('mid')

for mid in set(df1_bymid.groups) | set(df2_bymid.groups) :
    rows1 = df1_bymid.get_group(mid, [])
    rows2 = df1_bymid.get_group(mid, [])
    for row1, row2 in itertools.product(rows1, rows2) :
        yield row1, row2

Of course I could creating a function, and I might, it just seemed that if I have to go this far out of my way, maybe I'm not using the GroupBy object the way it was intended:

def get_group(df, name, obj=None, default=None) :
    if obj is None :
        obj = df.obj

    try :
        inds = df.indices[name]
    except KeyError, e :
        if default is None :
            raise e

        inds = default

    return df.obj.take(inds, df.axis)

Answer 1

I might define my own get_group() as following

In [55]: def get_group(g, key):
   ....:     if key in g.groups: return g.get_group(key)
   ....:     return pd.DataFrame()
   ....: 

In [52]: get_group(g, 's1')
Out[52]: 
   Mt Sp  Value  count
0  s1  a      1      3
1  s1  b      2      2

In [54]: get_group(g, 's4')
Out[54]: 
Empty DataFrame
Columns: []
Index: []