Concatenate two data frames to a data frame of square matrix - python-2.7

Question

I have two pandas dataframes of which shapes are "n x n" and "m x n" (m < n). For example:
df1 = pd.DataFrame([[0,1,0,1],[1,0,0,1],[0,0,0,1],[1,1,1,0]])
df2 = pd.DataFrame([[1,1,1,0],[1,1,0,1]])
I'd like to get the dataframe of a square matrix by concatenating above dataframes:
df3 = foo(df1, df2)
print df3.values
This should print like the following matrix.
[[0,1,0,1,1,1],
[1,0,0,1,1,1],
[0,0,0,1,1,0],
[1,1,1,0,0,1],
[1,1,1,0,0,0],
[1,1,0,1,0,0]]
The logic of concatination is like this:
the upper-left part of the square matrix comes from df1
the upper-right part of it comes from the transpose of df2
the bottom-left part of it comes from df2
all element of the rest of it (bottom-right part) is zero.
How do I implement the above logic (foo method)?

Answer 1

Here is a sample of foo:
def foo(_df1,_df2):
df1 = _df1.reset_index(drop=True) #to make sure the index is ordered
df2 = _df2.reset_index(drop=True) #to make sure the index is ordered
df2_transpose = df2.transpose().reset_index(drop=True) #reset the index to match the join below
df_upper = df1.join(df2_transpose,rsuffix="_") #add suffix for additional columns
df_upper.columns = [i for i in range(df_upper.shape[1])] #reset column names to int
df = pd.concat([df_upper,df2]) #fill the bottom left
df.fillna(0,inplace=True) #fill with 0 the bottom right
return df

Answer 2

The foo function:
def foo(df1_data,df2_data):
df_test = pd.concat([df1_data,df2_data])
a = np.concatenate((df2_data.values.T,np.zeros(shape = (df_test.values.shape[0] - df_test.values.shape[1],df2_data.values.shape[0]))))
final_array = np.append(df_test.values,a, axis=1).astype(int)
df3_data = pd.DataFrame(final_array)
return df3_data
df3 = foo(df1,df2)