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, 8.53         


        

Answer 1

In Python 3.7:

import numpy as np

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()))
vals = np.array(list(Samples.values()))

Note: It's important to say that in this Python version dict.keys() and dict.values() return objects of type dict_keys and dict_values, respectively.

Answer 2

Just assign all of the values to a list, and then convert to a np.array().

import numpy as np

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(Samples.keys())
vals = np.array(Samples.values())

Or, if you want to iterate over it:

import numpy as np

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 = vals = []

for k, v in Samples.items():
    keys.append(k)
    vals.append(v)

keys = np.array(keys)
vals = np.array(vals)

Answer 3

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.

Answer 4

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 5

If you care about speed (Python 3.7)

rnd = np.random.RandomState(10)

for i in [10,100,1000,10000,100000]:
    test_dict = {j:j for j in rnd.uniform(-100,100,i)}
    assert len(test_dict) == i
    print(f"\nFor {i} keys\n-----------")
    
    %timeit keys = np.fromiter(test_dict.keys(), dtype=float)
    
    %timeit keys = np.array(list(test_dict.keys()))

np.fromiter is 5-7 times faster

For 10 keys
-----------
712 ns ± 4.77 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
1.65 µs ± 9.15 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

For 100 keys
-----------
1.87 µs ± 13.7 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
8.02 µs ± 22.3 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)

For 1000 keys
-----------
13.7 µs ± 27.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
70.5 µs ± 251 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)

For 10000 keys
-----------
128 µs ± 70.6 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
698 µs ± 455 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)

For 100000 keys
-----------
1.45 ms ± 374 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each)
7.14 ms ± 6.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Answer 6

keys = np.array(dictionary.keys())
values = np.array(dictionary.values())