Pandas merging/joining tables with multiple key columns and duplicating rows where necessary

Question

I have several tables that contain lab results, with a 'master' table of sample data with things like a description. The results tables are also broken down by specimen (sub-samples). They contain multiple results columns - I'm just showing one here. I want to combine all the results tables into one dataframe, like this:

Table 1:
Location    Sample   Description
1           A        Yellow
1           B        Red
2           A        Blue
2           B        Violet

Table 2
Location  Sample    Specimen   Result1
1         A         X          5
1         A         Y          6
1         B         X          10
2         A         X          1

Table 3
Location  Sample    Specimen   Result2
1         A         X          "Heavy"
1         A         Q          "Soft"
1         B         K          "Grey"
2         B         Z          "Bananas"

Desired Output:
Location  Sample  Description  Specimen  Result1  Result2
1         A       Yellow       X         5        "Heavy"
1         A       Yellow       Y         6        nan
1         A       Yellow       Q         nan      "Soft"
1         B       Red          X         10       nan
1         B       Red          K         nan      "Grey"
2         A       Blue         X         1        nan
2         B       Violet       Z         nan      "Bananas"      

I currently have a solution for this using iterrows() and df.append(), but these are both slow operations and when there are thousands of results it takes too long. Is there better way? I have tried using join() and merge() but I can't seem to get the result I want.

Quick code to reproduce my dataframes:

dict1 = {'Location': [1,1,2,2], 'Sample': ['A','B','A','B'], 'Description': ['Yellow','Red','Blue','Violet']}
dict2 = {'Location': [1,1,1,2], 'Sample': ['A','A','B','A'], 'Specimen': ['x', 'y','x', 'x'], 'Result1': [5,6,10,1]}
dict3 = {'Location': [1,1,1,2], 'Sample': ['A','A','B','B'], 'Specimen': ['x', 'q','k', 'z'], 'Result2': ["Heavy","Soft","Grey","Bananas"]}

df1 = pd.DataFrame.from_dict(dict1)
df2 = pd.DataFrame.from_dict(dict2)
df3 = pd.DataFrame.from_dict(dict3)