combining three different timestamp dataframes using duration match

Question

I have three data frames with different dataframes and frequencies. I want to combine them into one dataframe.

First dataframe collects sunlight from sun as given below

Answer 1

Nice question I am using reindex with nearest as method 1

df1['row']=df1.index
s1=df1.reindex(df2.index,method='nearest')
s2=df1.reindex(df3.index,method='nearest')
s1=s1.join(df2).set_index('row')
s2=s2.join(df3).set_index('row')

pd.concat([s1,s2.reindex(s1.index,method='nearest')],1)
Out[67]: 
                     light_data    A  light_data    B
row                                                  
2019-05-01 06:54:00          10  100          40  400
2019-05-01 06:59:00          50  200          60  500
2019-05-01 07:04:00          80  300          90  600

Or at the last line using merge_asof

pd.merge_asof(s1,s2,left_index=True,right_index=True,direction='nearest')
Out[81]: 
                     light_data_x    A  light_data_y    B
row                                                      
2019-05-01 06:54:00            10  100            40  400
2019-05-01 06:59:00            50  200            40  400
2019-05-01 07:04:00            80  300            90  600

---

Make it extendable

df1['row']=df1.index

l=[]
for i,x in enumerate([df2,df3]):
    s1=df1.reindex(x.index,method='nearest')
    if i==0:
        l.append(s1.join(x).set_index('row').add_suffix(x.columns[0].str[-1]))
    else :
        l.append(s1.join(x).set_index('row').reindex(l[0].index,method='nearest').add_suffix(x.columns[0].str[-1]))
pd.concat(l,1)