python - Normalize adjency matrix (in pandas) with MinMaxScaler -

i have adjency matrix (dm) of items vs items; value between 2 items (e.g., item0,item1) refers number of times these items appear together. how can scale values in pandas between 0 1?

from sklearn import preprocessing scaler = preprocessing.minmaxscaler() 

however, not sure how apply scaler pandas data frame.

you can assign resulting array dataframe loc:

df = pd.dataframe(np.random.randint(1, 5, (5, 5)))  df out[277]:     0  1  2  3  4 0  2  3  2  3  1 1  2  3  4  4  2 2  2  3  4  3  2 3  1  1  2  1  4 4  4  2  2  3  1  df.loc[:,:] = scaler.fit_transform(df)  df out[279]:            0    1    2         3         4 0  0.333333  1.0  0.0  0.666667  0.000000 1  0.333333  1.0  1.0  1.000000  0.333333 2  0.333333  1.0  1.0  0.666667  0.333333 3  0.000000  0.0  0.0  0.000000  1.000000 4  1.000000  0.5  0.0  0.666667  0.000000 

you can same (df - df.min()) / (df.max() - df.min()).