Fastest way to parse JSON strings into numpy arrays

Question

I have huge json objects containing 2D lists of coordinates that I need to transform into numpy arrays for processing.

However using json.loads followed with np.array() is too slow.

Is there a way to increase the speed of creation of numpy arrays from json?

import json
import numpy as np

json_input = '{"rings" : [[[-8081441.0, 5685214.0], [-8081446.0, 5685216.0], [-8081442.0, 5685219.0], [-8081440.0, 5685211.0], [-8081441.0, 5685214.0]]]}'

dict = json.loads(json_input)
numpy_2d_arrays = [np.array(ring) for ring in dict["rings"]]

I would take any solution whatsoever!

Solution 1

The simplest answer would just be:

numpy_2d_arrays = np.array(dict["rings"])

As this avoids explicitly looping over your array in python you would probably see a modest speedup. If you have control over the creation of json_input it would be better to write out as a serial array. A version is here.

Solution 2

Since JSON syntax is really near to Python syntax, I suggest you to use ast.literal_eval. It may be faster…

import ast
import numpy as np

json_input = """{"rings" : [[[-8081441.0, 5685214.0],
                             [-8081446.0, 5685216.0],
                             [-8081442.0, 5685219.0],
                             [-8081440.0, 5685211.0],
                             [-8081441.0, 5685214.0]]]}"""

rings = ast.literal_eval(json_input)
numpy_2d_arrays = [np.array(ring) for ring in rings["rings"]]

Give it a try. And tell us.

Comments

[Below the Radar]

Thank you but ast.literal_eval is slower than json.loads() with my data

[ei-grad]

using eval is unsafe

[Gribouillis]

@ei-grad It depends on the context. Python has always been made for consenting adults!