python dict to numpy structured array

python dict to numpy structured array

You could use np.array(list(result.items()), dtype=dtype):

import numpy as np
result = {0: 1.1181753789488595, 1: 0.5566080288678394, 2: 0.4718269778030734, 3: 0.48716683119447185, 4: 1.0, 5: 0.1395076201641266, 6: 0.20941558441558442}

names = [id,data]
formats = [f8,f8]
dtype = dict(names = names, formats=formats)
array = np.array(list(result.items()), dtype=dtype)

print(repr(array))

yields

array([(0.0, 1.1181753789488595), (1.0, 0.5566080288678394),
       (2.0, 0.4718269778030734), (3.0, 0.48716683119447185), (4.0, 1.0),
       (5.0, 0.1395076201641266), (6.0, 0.20941558441558442)], 
      dtype=[(id, <f8), (data, <f8)])

If you dont want to create the intermediate list of tuples, list(result.items()), then you could instead use np.fromiter:

In Python2:

array = np.fromiter(result.iteritems(), dtype=dtype, count=len(result))

In Python3:

array = np.fromiter(result.items(), dtype=dtype, count=len(result))

Why using the list [key,val] does not work:

By the way, your attempt,

numpy.array([[key,val] for (key,val) in result.iteritems()],dtype)

was very close to working. If you change the list [key, val] to the tuple (key, val), then it would have worked. Of course,

numpy.array([(key,val) for (key,val) in result.iteritems()], dtype)

is the same thing as

numpy.array(result.items(), dtype)

in Python2, or

numpy.array(list(result.items()), dtype)

in Python3.


np.array treats lists differently than tuples: Robert Kern explains:

As a rule, tuples are considered scalar records and lists are
recursed upon. This rule helps numpy.array() figure out which
sequences are records and which are other sequences to be recursed
upon; i.e. which sequences create another dimension and which are the
atomic elements.

Since (0.0, 1.1181753789488595) is considered one of those atomic elements, it should be a tuple, not a list.

Even more simple if you accept using pandas :

import pandas
result = {0: 1.1181753789488595, 1: 0.5566080288678394, 2: 0.4718269778030734, 3: 0.48716683119447185, 4: 1.0, 5: 0.1395076201641266, 6: 0.20941558441558442}
df = pandas.DataFrame(result, index=[0])
print df

gives :

          0         1         2         3  4         5         6
0  1.118175  0.556608  0.471827  0.487167  1  0.139508  0.209416

python dict to numpy structured array

Let me propose an improved method when the values of the dictionnary are lists with the same lenght :

import numpy

def dctToNdarray (dd, szFormat = f8):
    
    Convert a rectangular dictionnary to numpy NdArray
    entry 
        dd : dictionnary (same len of list 
    retrun
        data : numpy NdArray 
    
    names = dd.keys()
    firstKey = dd.keys()[0]
    formats = [szFormat]*len(names)
    dtype = dict(names = names, formats=formats)
    values = [tuple(dd[k][0] for k in dd.keys())]
    data = numpy.array(values, dtype=dtype)
    for i in range(1,len(dd[firstKey])) :
        values = [tuple(dd[k][i] for k in dd.keys())]
        data_tmp = numpy.array(values, dtype=dtype)
        data = numpy.concatenate((data,data_tmp))
    return data

dd = {a:[1,2.05,25.48],b:[2,1.07,9],c:[3,3.01,6.14]}
data = dctToNdarray(dd)
print data.dtype.names
print data

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