python - NumPy : normalize column B according to value of column A -

given numpy array [a b], a different indexes , b count values. how can normalize b values according a value?

i tried:

 def normalize(np_array):     normalized_array = np.empty([1, 2])     indexes= np.unique(np_array[:, 0]).tolist()      index in indexes:         index_array= np_array[np_array[:, 0] == index]         mean_id = np.mean(index_array[:, 1])         std_id = np.std(index_array[:, 1])         if mean_id * std_id > 0:             index_array[:, 1] = (index_array[:, 1] - mean_id) / std_id             normalized_array = np.concatenate([normalized_array, index_array])     return np.delete(normalized_array, 0, 0) # apologies 

which doing job, i'm looking more noble way achieve this.

any input warmly welcome.

looks pandas can of here:

import pandas pd  df = pd.dataframe({'id': [1, 1, 2, 2, 1],                    'value': [10, 20, 15, 100, 12]})  byid = df.groupby('id') mean = byid.mean() std = byid.std()  df['normalized'] = df.apply(lambda x: (x.value - mean.ix[x.id]) / std.ix[x.id], axis=1) print(df) 

output:

   id  value  normalized 0   1     10   -0.755929 1   1     20    1.133893 2   2     15   -0.707107 3   2    100    0.707107 4   1     12   -0.377964 

coming numpy array:

>>> array([[  1,  10],        [  1,  20],        [  2,  15],        [  2, 100],        [  1,  12]]) 

you can create dataframe this:

>>> df = pd.dataframe({'id': a[:, 0], 'value': a[:, 1]}) >>> df    id  value 0   1     10 1   1     20 2   2     15 3   2    100 4   1     12