How do you copy data from a dataframe to another - pandas

Question

I am having a difficult time getting the correct data from a reference csv file to the one I am working on.
I have a csv file that has over 6 million rows and 19 columns. I looks something like this :
enter image description here
For each row there is a brand and a model of a car amongst other information.
I want to add to this file the fuel consumption per 100km traveled and the type of fuel that is used.
I have another csv file that has the fuel consumption of every model of car that looks something like this : enter image description here
What I want to ultimately do is add the matching values of G,H, I and J columns from the second file to the first one.
Because of the size of the file I was wondering if there is another way to do it other than with a "for" or a "while" loop?
EDIT :
For example...
The first df would look something like this
ID
Brand
Model
Other_columns
Fuel_consu_1
Fuel_consu_2
1
Toyota
Rav4
a
NaN
NaN
2
Honda
Civic
b
NaN
NaN
3
GMC
Sierra
c
NaN
NaN
4
Toyota
Rav4
d
NaN
NaN
The second df would be something like this
ID
Brand
Model
Fuel_consu_1
Fuel_consu_2
1
Toyota
Corrola
100
120
2
Toyota
Rav4
80
84
3
GMC
Sierra
91
105
4
Honda
Civic
112
125
The output should be :
ID
Brand
Model
Other_columns
Fuel_consu_1
Fuel_consu_2
1
Toyota
Rav4
a
80
84
2
Honda
Civic
b
112
125
3
GMC
Sierra
c
91
105
4
Toyota
Rav4
d
80
84
The first df may have many times the same brand and model for different ID's. The order is completely random.

Answer 1

Thank you for providing updates I was able to put something together that should be able to help you
#You drop these two columns because you won't need them once you join them to df1 (which is your 2nd table provided)
df.drop(['Fuel_consu_1', 'Fuel_consu_2'], axis = 1 , inplace = True)
#This will join your first and second column to each other on the Brand and Model columns
df_merge = pd.merge(df, df1, on=['Brand', 'Model'])