join or merge or reshape dataframe based on two conditions - pandas

Question

I have two dataframes df and df1 which I want to merge or join.
import pandas as pd
df = pd.DataFrame(columns=['lt1', 'lt2','lt3','lt4','lt5','lt6'])
df['date'] = pd.date_range('2016-1-1', periods=5, freq='D')
df
lt1 lt2 lt3 lt4 lt5 lt6 date
0 NaN NaN NaN NaN NaN NaN 2016-01-01
1 NaN NaN NaN NaN NaN NaN 2016-01-02
2 NaN NaN NaN NaN NaN NaN 2016-01-03
3 NaN NaN NaN NaN NaN NaN 2016-01-04
4 NaN NaN NaN NaN NaN NaN 2016-01-05
df1 = pd.DataFrame({'location': ['lt1','lt3', 'lt6', 'lt1','lt2', 'lt3'], \
'date': ['2016-01-1', '2016-01-02','2016-01-1','2016-01-03','2016-01-5','2016-01-4'], \
'counts': ['2', '1','1','1', '3','1']})
df1.date = pd.to_datetime(df1.date)
df1
counts date location
0 2 2016-01-01 lt1
1 1 2016-01-02 lt3
2 1 2016-01-01 lt6
3 2 2016-01-03 lt1
4 3 2016-01-05 lt2
5 1 2016-01-04 lt3
I want to put counts values depending on location from df1 into df. The merge will be based on date column but the values to be added will be from df2.counts column and those values will be properly assigned into respective location names columns in df. Column names in df contains all the names present in df1.location column.
Merging just by date alone is easy but since it is not really a straightaway merge, it is more like reshaping or join. Any suggestion how to get the following df as output:
df
date lt1 lt2 lt3 lt4 lt5 lt6
0 2016-01-01 2 0 0 0 0 1
1 2016-02-01 0 0 1 0 0 0
2 2016-03-01 1 0 0 0 0 0
3 2016-04-01 0 0 1 0 0 0
4 2016-05-01 0 3 0 0 0 0

Answer 1

Here is one way using pivot_table and combine_first:
m=df1.pivot_table(index='date',columns='location',values='counts',aggfunc='sum')
final=df.set_index('date').combine_first(m).fillna(0).reset_index()
Or just:
(df.set_index('date').combine_first(df1.pivot('date','location','counts'))
.fillna(0).reset_index())
date lt1 lt2 lt3 lt4 lt5 lt6
0 2016-01-01 2 0 0 0 0 1
1 2016-01-02 0 0 1 0 0 0
2 2016-01-03 1 0 0 0 0 0
3 2016-01-04 0 0 1 0 0 0
4 2016-01-05 0 3 0 0 0 0