Dictionary keys and values to separate numpy arrays

Question

I have a dictionary as

Samples = {5.207403005022627: 0.69973543384229719, 6.8970222167794759: 0.080782939731898179, 7.8338517407140973: 0.10308033284258854, 8.5301143255505334: 0.018640838362318335, 10.418899728838058: 0.14427355015329846, 5.3983946820220501: 0.51319796560976771} 

I want to separate the keys and values into 2 numpy arrays. I tried np.array(Samples.keys(),dtype=np.float) but i get an error TypeError: float() argument must be a string or a number

Answer 1

You can use np.fromiter to directly create numpy arrays from the dictionary key and values views:

In python 3:

keys = np.fromiter(Samples.keys(), dtype=float) vals = np.fromiter(Samples.values(), dtype=float) 

In python 2:

keys = np.fromiter(Samples.iterkeys(), dtype=float) vals = np.fromiter(Samples.itervalues(), dtype=float) 

Answer 2

On python 3.4, the following simply works:

Samples = {5.207403005022627: 0.69973543384229719, 6.8970222167794759: 0.080782939731898179, 7.8338517407140973: 0.10308033284258854, 8.5301143255505334: 0.018640838362318335, 10.418899728838058: 0.14427355015329846, 5.3983946820220501: 0.51319796560976771}  keys = np.array(list(Samples.keys())) values = np.array(list(Samples.values())) 

The reason np.array(Samples.values()) doesn't give what you expect in Python 3 is that in Python 3, the values() method of a dict returns an iterable view, whereas in Python 2, it returns an actual list of the keys.

keys = np.array(list(Samples.keys())) will actually work in Python 2.7 as well, and will make your code more version agnostic. But the extra call to list() will slow it down marginally.