Pandas: Difference between two series with different length (unaligned index)

Consider two following series:

sri = inp.groupby(inp.index.date)['value'].count()

2009-01-12    7
2009-01-14    3

and

sro = out.groupby(out.index.date)['value'].count()

2009-01-03      1
2009-01-09     14
2009-01-10     61
2009-01-11     93
2009-01-12    106
2009-01-13    123
2009-01-14    130

When we subtract one from another, sro-sri, we have:

2009-01-03      NaN
2009-01-09      NaN
2009-01-10      NaN
2009-01-11      NaN
2009-01-12     99.0
2009-01-13      NaN
2009-01-14    127.0

However my desired output is:

2009-01-03      1.0
2009-01-04      0.0
2009-01-05      0.0
2009-01-06      0.0
2009-01-07      0.0
2009-01-08      0.0
2009-01-09     14.0
2009-01-10     61.0
2009-01-11     93.0
2009-01-12     99.0
2009-01-13    123.0
2009-01-14    127.0

and we can use the following workaround to generate the same result:

start_date = '2009-01-03'
end_date = '2009-01-15'
df = pd.DataFrame(
    index=pd.date_range(pd.to_datetime(start_date), pd.to_datetime(end_date) - timedelta(days=1), freq='d').date)
df = df.merge(sro.to_frame(), how='outer', left_index=True, right_index=True) \
    .merge(sri.to_frame(), how='outer', left_index=True, right_index=True).fillna(0)
print(df['value_x'] - df['value_y'])

Is there a much more compact solution to generate the same output?

>Solution :

A simple approach for a simple subtraction is to use sub with fillna=0:

sro.sub(sri, fill_value=0).convert_dtypes()

output:

2009-01-03      1
2009-01-09     14
2009-01-10     61
2009-01-11     93
2009-01-12     99
2009-01-13    123
2009-01-14    127

To add the missing indexes:

idx = sro.index.union(sri.index)
(sro.sub(sri, fill_value=0)
    .reindex(pd.date_range(idx.min(), idx.max()).astype(str), fill_value=0)
    .convert_dtypes()
)

output:

2009-01-03      1
2009-01-04      0
2009-01-05      0
2009-01-06      0
2009-01-07      0
2009-01-08      0
2009-01-09     14
2009-01-10     61
2009-01-11     93
2009-01-12     99
2009-01-13    123
2009-01-14    127

used input:

sri = pd.Series({'2009-01-12': 7, '2009-01-14': 3})
sro = pd.Series({'2009-01-03': 1, '2009-01-09': 14, '2009-01-10': 61, '2009-01-11': 93, '2009-01-12': 106, '2009-01-13': 123, '2009-01-14': 130})