How to merge two time series dataframes with different end dates and keep the longer end date-CodePudding

I have two time series with same sampling frequency but different end dates. I want to combine them into one and keep the total time range instead of the intersection. Leave the data outside the intersection NaN.

I've tried:

df_to_merge= [df1, df2]
df_merged = reduce(lambda left,right: pd.merge(left,right, on='timestamp'), df_to_merge)

The data:

df1
timestamp          col1
2010-10-10 00:00    10
2010-10-10 00:01    15
...
2010-10-15 00:00    10

df2 
timestamp          col2
2010-10-07 00:00    20
2010-10-10 00:01    25
...
2010-10-18 00:00    20

Desired outcome:

timestamp          col1    col2
2010-10-07 00:00    NaN     20
2010-10-07 00:01    NaN     25
...
2010-10-10 00:01    10      30
2010-10-15 00:00    10      40
..
2010-10-18 00:00    NaN     20

CodePudding user response：

You can perform a join operation:

df_merged = df1.join(df2,how='right')

By using right you ensure all values from the right (longer df) will be kept.

For example:

df1 = pd.DataFrame({'timestamp':pd.to_datetime(pd.Series(['2020-10-10 23:32',
                                                         '2020-10-13 23:28'])),
                  'col1':[5,8]})
df1 = df1.set_index('timestamp').resample('1d').fillna(method='ffill')

            col1
timestamp       
2020-10-10   NaN
2020-10-11   5.0
2020-10-12   5.0
2020-10-13   5.0

And

df2 = pd.DataFrame({'timestamp':pd.to_datetime(pd.Series(['2020-10-08 23:32',
                                                         '2020-10-15 23:28'])),
                  'col2':[50,80]})
df2 = df2.set_index('timestamp').resample('1d').fillna(method='ffill')

            col1
timestamp       
2020-10-08   NaN
2020-10-09  50.0
2020-10-10  50.0
2020-10-11  50.0
2020-10-12  50.0
2020-10-13  50.0
2020-10-14  50.0
2020-10-15  50.0

Return:

            col1  col2
timestamp             
2020-10-08   NaN   NaN
2020-10-09   NaN  50.0
2020-10-10   NaN  50.0
2020-10-11   5.0  50.0
2020-10-12   5.0  50.0
2020-10-13   5.0  50.0
2020-10-14   NaN  50.0
2020-10-15   NaN  50.0