pandas concat two dataframe while one column concat and another one still keeps?

Question

Input

df1

id      date      v1
a    2020-1-1     1
a    2020-1-2     2
b    2020-1-4     10
b    2020-1-22    30
c    2020-2-4     10
c    2020-2-22    30


df2

id      date      v1
a    2020-1-3     1
b    2020-1-7     12
b    2020-1-22    13
c    2020-2-10    15
c    2020-2-22    60

Goal

id      date      v1   v2
a    2020-1-1     1    0
a    2020-1-2     2    0
a    2020-1-3     0    1
b    2020-1-4     10   0
b    2020-1-7     0    12
b    2020-1-22    30   13
c    2020-2-4     10   0
c    2020-2-10     0   15
c    2020-2-22    30   60

The details:

• Only two dataframes, for each id, the date is unique.
• Concat two dataframes into df based on id, each id contains all date values from two dataframe
• new merge dataframe contains v1 and v2 columns, while the date in df1 and df2, it returns original values, while the date only in one of df1 and df2, it returns original value and 0 if there is no value on the date.

Try
I have searched merge, concat document but I could not find the answers.

Answer 1

First convert columns to datetimes for correct ordering by to_datetime, then DataFrame.merge with outer join and rename column v1 for df2 for avoid v1_x and v1_y columns in output, replace missing values by DataFrame.fillna, sorting output by DataFrame.sort_values:

df1['date'] = pd.to_datetime(df1['date'])
df2['date'] = pd.to_datetime(df2['date'])

df = (df1.merge(df2.rename(columns={'v1':'v2'}), on=['id','date'], how='outer')
         .fillna(0)
         .sort_values(['id','date']))
print (df)
  id       date    v1    v2
0  a 2020-01-01   1.0   0.0
1  a 2020-01-02   2.0   0.0
6  a 2020-01-03   0.0   1.0
2  b 2020-01-04  10.0   0.0
7  b 2020-01-07   0.0  12.0
3  b 2020-01-22  30.0  13.0
4  c 2020-02-04  10.0   0.0
8  c 2020-02-10   0.0  15.0
5  c 2020-02-22  30.0  60.0