Create dictionary of value frequencies from numpy array

Question

I need to create a dictionary from a loop that runs through an array of 2 columns of numbers. Below is a small subset of the array:

array([[  0,   1],
       [  1,   0],
       [  1,   2],
       [  2,   3],
       [  2,   1]])

I would like to create a dictionary that takes the unique numbers of the first column as keys (eg {0,1,2} in this example) and the corresponding numbers in the second column as values.

For this example the dictionary would look like:

dict = {0:[1], 1:[0,2], 2:[3,1]}

My array is very long (370,000 x 2) so I would like to do this through an efficient loop. Any advice would be greatly appreciated!

Answer 1

You can use defaultdict to accomplish this.

from collections import defaultdict
a = np.array([[  0,   1],[  1,   0],[  1,   2],[  2,   3], [  2,   1]])
d = defaultdict(list)
for x,y in a:
    d[x].append(y)

Answer 2

Assuming your array has been sorted by the first column, you can use groupby :

from itertools import groupby
{k: [v for _, v in g] for k, g in groupby(arr, lambda x: x[0])}
# {0: [1], 1: [0, 2], 2: [3, 1]}

---

#arr = np.array([[  0,   1],
#                [  1,   0],
#                [  1,   2],
#                [  2,   3],
#                [  2,   1]])

Answer 3

nice one-liner to do that:

import itertools

array = [[  0,   1],
       [  1,   0],
       [  1,   2],
       [  2,   3],
       [  2,   1]]

d = {k:[i[1] for i in v] for k,v in itertools.groupby(sorted(array),lambda x : x[0])}

result:

{0: [1], 1: [0, 2], 2: [1, 3]}

• group by first items on a sorted version of the list (in case the sort isn't already done)
• create the dictionary in a dict comprehension building the value list with only the 2nd element of the grouped items

Answer 4

If your first column is a "range with repeats"

steps_at = np.searchsorted(a[:,0], np.arange(a[-1,0]+1))
result = {k:v for k,v in zip(a[steps_at,0], np.split(a[:,1], steps_at[1:]))}

If your first column has equal items clustered but not sorted

steps_at = np.where(np.diff(np.r_[np.nan, a[:,0]]))[0]
return {k:v for k,v in zip(a[steps_at,0], np.split(a[:,1], steps_at[1:]))}

General case

ind = np.argsort(a[:, 0], kind='mergesort')
aa = a[ind, 0]
steps_at = np.where(np.diff(np.r_[np.nan, aa]))[0]
return {k:v for k,v in zip(aa[steps_at], np.split(a[ind,1], steps_at[1:]))}

Shootout:

(19, 2) correctness

Psidom               {0: [0, 28, 38, 97, 99, 65, 73], 1: [64, 91, 70, 40, 9], 2: [94, 96, 69, 46], 3: [85, 15, 65]}
Daniel_Jimenez       defaultdict(<class 'list'>, {0: [0, 28, 38, 97, 99, 65, 73], 1: [64, 91, 70, 40, 9], 2: [94, 96, 69, 46], 3: [85, 15, 65]})
Jean_Francois_Fabre  {0: [0, 28, 38, 97, 99, 65, 73], 1: [64, 91, 70, 40, 9], 2: [94, 96, 69, 46], 3: [85, 15, 65]}
Alexandre_Kempf      {0: array([ 0, 28, 38, 97, 99, 65, 73]), 1: array([64, 91, 70, 40,  9]), 2: array([94, 96, 69, 46]), 3: array([85, 15, 65])}
Or_Duan              {0: [0, 28, 38, 97, 99, 65, 73], 1: [64, 91, 70, 40, 9], 2: [94, 96, 69, 46], 3: [85, 15, 65]}
Paul_Panzer_sorted   {0: array([ 0, 28, 38, 97, 99, 65, 73]), 1: array([64, 91, 70, 40,  9]), 2: array([94, 96, 69, 46]), 3: array([85, 15, 65])}
Paul_Panzer_grouped  {0: array([ 0, 28, 38, 97, 99, 65, 73]), 1: array([64, 91, 70, 40,  9]), 2: array([94, 96, 69, 46]), 3: array([85, 15, 65])}
Paul_Panzer_general  {0: array([ 0, 28, 38, 97, 99, 65, 73]), 1: array([64, 91, 70, 40,  9]), 2: array([94, 96, 69, 46]), 3: array([85, 15, 65])}
B_M_sorted           {0: array([ 0, 28, 38, 97, 99, 65, 73]), 1: array([64, 91, 70, 40,  9]), 2: array([94, 96, 69, 46]), 3: array([85, 15, 65])}
B_M_general          {0: array([ 0, 28, 38, 65, 73, 97, 99]), 1: array([ 9, 40, 64, 70, 91]), 2: array([46, 69, 94, 96]), 3: array([15, 65, 85])}

 (40194, 2) speed (seconds used for 10 repeats)

Psidom               0.4336233548820019
Daniel_Jimenez       0.3609276609495282
Jean_Francois_Fabre  0.17962428089231253
Alexandre_Kempf      3.5392782238777727
Or_Duan              0.1873011060524732
Paul_Panzer_sorted   0.08001555898226798
Paul_Panzer_grouped  0.08144942414946854
Paul_Panzer_general  0.10183193604461849
B_M_sorted           0.09192353091202676
B_M_general          0.16612185980193317

 (400771, 2) speed (seconds used for 10 repeats)

Psidom               3.968917251098901
Daniel_Jimenez       3.619185874937102
Jean_Francois_Fabre  1.7871235068887472
Or_Duan              1.9176530800759792
Paul_Panzer_sorted   0.8291062880307436
Paul_Panzer_grouped  0.8662846579682082
Paul_Panzer_general  1.0812653130851686
B_M_sorted           1.031000167131424
B_M_general          2.16174431797117
Alexandre_Kempf      513.2718367418274

code:

from collections import defaultdict
from itertools import groupby
import numpy as np
import timeit

Psidom = lambda a: {k: [v for _, v in g] for k, g in groupby(a, lambda x: x[0])}

def Daniel_Jimenez(a):
    d = defaultdict(list)
    for x,y in a:
        d[x].append(y)
    return d

Jean_Francois_Fabre = lambda a: {k:[i[1] for i in v] for k,v in groupby(a,lambda x : x[0])}

def Alexandre_Kempf(a):
    keys = a[:,0]
    items = a[:,1]
    uniqkey = np.unique(keys)
    prelist = [items[keys==i] for i in uniqkey]
    dico = {}
    for i in np.arange(len(uniqkey)):
        dico[uniqkey[i]] = prelist[i]
    return dico

def Or_Duan(a):
    default = {}
    for elm in a:
        try:
            default[elm[0]].append(elm[1])
        except KeyError:
            default[elm[0]] = [elm[1]]
    return default

def Paul_Panzer_sorted(a):
    steps_at = np.searchsorted(a[:,0], np.arange(a[-1,0]+1))
    return {k:v for k,v in zip(a[steps_at,0], np.split(a[:,1], steps_at[1:]))}

def Paul_Panzer_grouped(a):
    steps_at = np.where(np.diff(np.r_[np.nan, a[:,0]]))[0]
    return {k:v for k,v in zip(a[steps_at,0], np.split(a[:,1], steps_at[1:]))}

def Paul_Panzer_general(a):
    ind = np.argsort(a[:, 0], kind='mergesort')
    aa = a[ind, 0]
    steps_at = np.where(np.diff(np.r_[np.nan, aa]))[0]
    return {k:v for k,v in zip(aa[steps_at], np.split(a[ind,1], steps_at[1:]))}

def B_M_sorted(b):
    keys,values=b.T
    uniq,steps=np.unique(keys,return_index =True)
    bins=np.split(values,steps[1:])
    return dict(zip(uniq,bins))

def B_M_general(a):
    b=a[np.lexsort(a.T[::-1])]
    keys,values=b.T
    uniq,steps=np.unique(keys,return_index =True)
    bins=np.split(values,steps[1:])
    return dict(zip(uniq,bins))

c = np.arange(4).repeat(np.random.randint(1,10,(4)))
d = np.random.randint(100, size=c.shape)
t = np.c_[c, d]
c = np.arange(8000).repeat(np.random.randint(1,10,(8000)))
d = np.random.randint(100, size=c.shape)
a = np.c_[c, d]
c = np.arange(80000).repeat(np.random.randint(1,10,(80000)))
d = np.random.randint(100, size=c.shape)
b = np.c_[c, d]

print(t.shape, 'correctness\n')
i = 0
for f in (Psidom, Daniel_Jimenez, Jean_Francois_Fabre, Alexandre_Kempf,
          Or_Duan, Paul_Panzer_sorted, Paul_Panzer_grouped,
          Paul_Panzer_general, B_M_sorted, B_M_general):
    name = f.__name__
    if name == '<lambda>':
        name = ['Psidom', 'Jean_Francois_Fabre'][i]
        i += 1
    print(name + (20 - len(name)) * ' ', f(t))

print('\n', a.shape, 'speed (seconds used for 10 repeats)\n')
i = 0
for f in (Psidom, Daniel_Jimenez, Jean_Francois_Fabre, Alexandre_Kempf,
          Or_Duan, Paul_Panzer_sorted, Paul_Panzer_grouped,
          Paul_Panzer_general, B_M_sorted, B_M_general):
    name = f.__name__
    if name == '<lambda>':
        name = ['Psidom', 'Jean_Francois_Fabre'][i]
        i += 1
    print(name + (20 - len(name)) * ' ', timeit.timeit("f(a)",number=10,
                                                       globals={'f':f, 'a':a}))

print('\n', b.shape, 'speed (seconds used for 10 repeats)\n')
i = 0
for f in (Psidom, Daniel_Jimenez, Jean_Francois_Fabre,
          Or_Duan, Paul_Panzer_sorted, Paul_Panzer_grouped,
          Paul_Panzer_general, B_M_sorted, B_M_general, Alexandre_Kempf):
    name = f.__name__
    if name == '<lambda>':
        name = ['Psidom', 'Jean_Francois_Fabre'][i]
        i += 1
    print(name + (20 - len(name)) * ' ', timeit.timeit("f(a)",number=10,
                                                       globals={'f':f, 'a':b}))

Answer 5

a pure numpy solution :

b=a[np.lexsort(a.T[::-1])] # if necessary.
keys,values=b.T
uniq,steps=np.unique(keys,return_index =True)
bins=np.split(values,steps[1:])

If uniq==range(len(uniq)) , let it like that : bins[key] will work, and it is the fastest way.

Else :

d=dict(zip(uniq,bins))
#{0: array([1]), 1: array([0, 2]), 2: array([1, 3])}

will build your dictionary.

Answer 6

One solution would be to do (if a is your array):

keys = a[:,0]
items = a[:,1]
uniqkey = np.unique(keys)
prelist = [items[keys==i] for i in uniqkey]
dico = {}
for i in np.arange(len(uniqkey)):
    dico[uniqkey[i]] = prelist[i]

Answer 7

Alternative to the defaultdict is to use try except and implement the "ask forgiveness not permission" approach.

array = [[  0,   1],
           [  1,   0],
           [  1,   2],
           [  2,   3],
           [  2,   1]]
default = {}
for elm in array:
    try:
        default[elm[0]].append(elm[1])
    except KeyError:
        default[elm[0]] = [elm[1]]