I have a dictionary with entries labelled as {(k,i): value, ...}
. I now want to convert this dictionary into a 2d array where the value given for an element of the array at position [k,i]
is the value from the dictionary with label (k,i)
. The length of the rows will not necessarily be of the same size (e.g. row k = 4
may go up to index i = 60
while row k = 24
may go up to index i = 31
). Due to the asymmetry, it is fine to make all additional entries in a particular row equal to 0 in order to have a rectangular matrix.
It's sometimes required to convert a dictionary in Python into a NumPy array and Python provides an efficient method to perform this operation. Converting a dictionary to NumPy array results in an array holding the key-value pairs of the dictionary. Let's see the different methods: Method 1: Using numpy.
We can use Python's numpy. save() function from transforming an array into a binary file when saving it. This method also can be used to store the dictionary in Python.
Unlike sequences, which are indexed by a range of numbers, dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys.
Here's an approach -
# Get keys (as indices for output) and values as arrays
idx = np.array(d.keys())
vals = np.array(d.values())
# Get dimensions of output array based on max extents of indices
dims = idx.max(0)+1
# Setup output array and assign values into it indexed by those indices
out = np.zeros(dims,dtype=vals.dtype)
out[idx[:,0],idx[:,1]] = vals
We could also use sparse matrices to get the final output. e.g. with coordinate format sparse matrices
. This would be memory efficient when kept as sparse matrices. So, the last step could be replaced by something like this -
from scipy.sparse import coo_matrix
out = coo_matrix((vals, (idx[:,0], idx[:,1])), dims).toarray()
Sample run -
In [70]: d
Out[70]: {(1, 4): 120, (2, 2): 72, (2, 3): 100, (5, 2): 88}
In [71]: out
Out[71]:
array([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 120],
[ 0, 0, 72, 100, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 88, 0, 0]])
To make it generic for ndarrays of any number of dimensions, we can use linear-indexing and use np.put
to assign values into the output array. Thus, in our first approach, just replace the last step of assigning values with something like this -
np.put(out,np.ravel_multi_index(idx.T,dims),vals)
Sample run -
In [106]: d
Out[106]: {(1,0,0): 99, (1,0,4): 120, (2,0,2): 72, (2,1,3): 100, (3,0,2): 88}
In [107]: out
Out[107]:
array([[[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]],
[[ 99, 0, 0, 0, 120],
[ 0, 0, 0, 0, 0]],
[[ 0, 0, 72, 0, 0],
[ 0, 0, 0, 100, 0]],
[[ 0, 0, 88, 0, 0],
[ 0, 0, 0, 0, 0]]])
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