How can I add a new column to a DataFrame based on a common row with another DataFrame in pandas?

Question

I have a DataFrame like this one:

import pandas as pd
import numpy as np

df = pd.DataFrame({'Sl.No.': {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6},
 'Area': {0: 'AREA 1',
  1: 'AREA 1',
  2: 'AREA 1',
  3: 'AREA 2',
  4: 'AREA 2',
  5: 'AREA 2'},
 'Location': {0: 'Loc A',
  1: 'Loc B',
  2: 'Loc C',
  3: 'Loc D',
  4: 'Loc E',
  5: 'Loc F'},
 'Total Depth': {0: 5.6, 1: 6.8, 2: 2.9, 3: 4.3, 4: 6.0, 5: 5.3},
 'Jan': {0: 2.3, 1: 4.2, 2: 2.1, 3: 2.9, 4: 5.2, 5: 4.3},
 'Feb': {0: 1.9, 1: 3.8, 2: 2.0, 3: 2.5, 4: 4.6, 5: 3.2}})

This produces:

	Sl.No.	Area	Location	Total Depth	Jan	Feb
0	1	AREA 1	Loc A	5.6	2.3	1.9
1	2	AREA 1	Loc B	6.8	4.2	3.8
2	3	AREA 1	Loc C	2.9	2.1	2
3	4	AREA 2	Loc D	4.3	2.9	2.5
4	5	AREA 2	Loc E	6	5.2	4.6
5	6	AREA 2	Loc F	5.3	4.3	3.2

I have another DataFrame which has all the columns in the above DataFrame but the Area column, somewhat like the following DataFrame:

df_1 = {'Sl.No.': {0: 1, 1: 2, 2: 3, 3: 4},
 'Location': {0: 'Loc A', 1: 'Loc C', 2: 'Loc E', 3: 'Loc F'},
 'Total Depth': {0: 5.6, 1: 2.9, 2: 6.0, 3: 5.3},
 'Jan': {0: 2.3, 1: 2.1, 2: 5.2, 3: 4.3},
 'Feb': {0: 1.9, 1: 2.0, 2: 4.6, 3: 3.2}}

This gives:

	Sl.No.	Location	Total Depth	Jan	Feb
0	1	Loc A	5.6	2.3	1.9
1	2	Loc C	2.9	2.1	2
2	3	Loc E	6	5.2	4.6
3	4	Loc F	5.3	4.3	3.2

Now, how can I add the Area column to df_1 to get a table like this one:

	Sl.No.	Location	Total Depth	Jan	Feb	Area
0	1	Loc A	5.6	2.3	1.9	AREA I
1	2	Loc C	2.9	2.1	2	AREA I
2	3	Loc E	6	5.2	4.6	AREA II
3	4	Loc F	5.3	4.3	3.2	AREA II