python - Pearson Correlation after Normalization -


i want normalize data , compute pearson correlation. if try without normalization works. normalization error message: attributeerror: 'numpy.ndarray' object has no attribute 'corr' can solve problem?

import numpy np import pandas pd   filename_train = 'c:\users\xxx.xxx\workspace\dataset\!train_data.csv' names = ['a', 'b', 'c', 'd', 'e', ...] df_train = pd.read_csv(filename_train, names=names)  sklearn.preprocessing import normalizer normalizeddf_train = normalizer().fit_transform(df_train)  #pearson correlation pd.set_option('display.width', 100) pd.set_option('precision', 2) print(normalizeddf_train.corr(method='pearson'))

you need dataframe constructor, because output of fit_transform numpy array , work dataframe.corr:

df_train = pd.dataframe({'a':[1,2,3],                    'b':[4,5,6],                    'c':[7,8,9],                    'd':[1,3,5],                    'e':[5,3,6],                    'f':[7,4,3]})  print (df_train)     b  c  d  e  f 0  1  4  7  1  5  7 1  2  5  8  3  3  4 2  3  6  9  5  6  3  sklearn.preprocessing import normalizer normalizeddf_train = normalizer().fit_transform(df_train) print (normalizeddf_train) [[ 0.08421519  0.33686077  0.58950634  0.08421519  0.42107596  0.58950634]  [ 0.1774713   0.44367825  0.70988521  0.26620695  0.26620695  0.3549426 ]  [ 0.21428571  0.42857143  0.64285714  0.35714286  0.42857143  0.21428571]]  print(pd.dataframe(normalizeddf_train).corr(method='pearson'))           0         1         2         3         4         5 0  1.000000  0.917454  0.646946  0.998477 -0.203152 -0.994805 1  0.917454  1.000000  0.896913  0.894111 -0.575930 -0.872187 2  0.646946  0.896913  1.000000  0.603899 -0.878063 -0.565959 3  0.998477  0.894111  0.603899  1.000000 -0.148832 -0.998906 4 -0.203152 -0.575930 -0.878063 -0.148832  1.000000  0.102420 5 -0.994805 -0.872187 -0.565959 -0.998906  0.102420  1.000000

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