How to allocation a bunch of 2-D array into each grid as a time series Pandas.DataFrame?

Question

Here is my question:
I have some 2-D array data which represent the concentration of some chemical of each grid by the time, like follows:
http://i12.tietuku.com/4501009ea445c286.png .
I want to extract the data of each grid from the 2-D arrays and save each one into a independent DataFrames follow the time series.
How to make this loop process simple? For example:

Input data：

K1  = np.random.rand(50,50),          
K2 = np.random.rand(50,50), 
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 Kn = np.random.rand(50,50).  

Aim:

f1 = [K1[0,0],K2[0,0], K3[0,0] ..... Kn[0,0]]        
f2 = [K1[0,1],K2[0,1], K3[0,1] ..... Kn[0,1]]         
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f2500 = [K1[49,49],K2[49,49], K3[49,49] ..... Kn[49,49]]

Answer 1

It looks you might be better off just create one dataframe, with f1, f2, f3 being different columns:

In [9]:

K1 = np.random.rand(50,50)        
K2 = np.random.rand(50,50) 
K3 = np.random.rand(50,50) 
K_list = [K1, K2, K3]
In [10]:

df = pd.DataFrame(np.vstack([item.ravel() for item in K_list]).T,
                  columns=['f1','f2','f3'],
                  index=pd.date_range(start='1900-01-01', periods=2500))
print df.head()
                  f1        f2        f3
1900-01-01  0.433388  0.530670  0.603563
1900-01-02  0.420193  0.870925  0.008032
1900-01-03  0.535433  0.941497  0.407027
1900-01-04  0.754523  0.523739  0.320910
1900-01-05  0.091197  0.582181  0.834773