Python - Convert numpy array to JSON

Question

I have a Python numpy array called myarray that looks like this..

I want to create JSON that looks like this..

myjson = [
    {'section': '3',
     'x': '163',
     'y': '362',
    },
    {'section': '7',
     'x': '239',
     'y': '581',
    },
    {'section': '10',
     'x': '353',
     'y': '602',
    },
]

This represents the 3rd, 7th and 10th line in the original numpy array. Does anybody have an example of something similar being achieved?

Can you please let me know? This affects the answer you get. When should the filtering be done? Before or during serialisation? I am not sure. Or is this just an example, and you want all rows with indices in this manner? — cs95
– cs95, Commented Jan 19, 2019 at 20:15

iz_ · Accepted Answer · 2019-01-19 20:24:27Z

3

To add on to Andrii's answer, I believe you can also unpack the arrays like this to make it slightly cleaner:

[{'section': i+1, 'x': x, 'y': y} for i, [x, y] in enumerate(myarray) if i in [2, 6, 9]]

edited Jan 19, 2019 at 20:24

iz_

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answered Jan 19, 2019 at 20:10

jacob

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2 Comments

fightstarr20 Over a year ago

When I try and serialise that I get an error message - TypeError: Object of type 'int64' is not JSON serializable - I am assuming it is because it is numypy values instead of python int

andnik Over a year ago

@fightstarr20 , you're right. I believe solution with pd.DataFrame is better, but if you want to make this work you need to wrap x and y in int(x) and int(y).

andnik · Accepted Answer · 2019-01-19 20:35:35Z

2

If your input array is arr I believe you want something like:

[{'section': i+1, 'x': x[0], 'y': x[1]} for i, x in enumerate(arr) if i in [2, 6, 9]]

[2, 6, 9] are your [3, 7, 10] positions, only starting from 0.

edited Jan 19, 2019 at 20:35

answered Jan 19, 2019 at 20:03

andnik

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Comments

Fatih1923 · Accepted Answer · 2019-01-19 21:02:20Z

2

pandas presents a convenient solution:

import pandas as pd
import numpy as np

df=pd.DataFrame(myarray, columns=["x", "y"])
df["Section"]=df.index

df.to_json(orient="records")

this yields:

 '[{"x":148,"y":362,"Section":0},{"x":153,"y":403,"Section":1},
{"x":163,"y":443,"Section":2},{"x":172,"y":483,"Section":3},
{"x":186,"y":521,"Section":4},{"x":210,"y":553,"Section":5},
{"x":239,"y":581,"Section":6},{"x":273,"y":604,"Section":7},
{"x":314,"y":611,"Section":8},{"x":353,"y":602,"Section":9}]'

The solution is a little bit different, but you can easily find your way in pure python to convert it into your structure.

edited Jan 19, 2019 at 21:02

answered Jan 19, 2019 at 20:52

Fatih1923

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Comments

Lotramus · Accepted Answer · 2022-03-30 08:10:49Z

I needed something similar but for a more general case. This replaces any numpy ndarray in a python dict, list, set or any mix of them recursively either in place or should return a numpyified copy.

from copy import deepcopy
import numpy as np

def unnumpyify(something, inplace=False):
    if inplace:
        new_something = something
    else:
        new_something = deepcopy(something)
    if type(new_something) == np.ndarray:
        new_something = new_something.tolist()
    if type(new_something) == dict:
        for k in new_something:
            if type(new_something[k]) == np.ndarray:
                new_something[k] = new_something[k].tolist()
            if type(new_something[k]) == dict:
                new_something[k] = unnumpyify(new_something[k])
            elif type(new_something[k]) == list:
                new_something[k] = unnumpyify(new_something[k])
    elif type(new_something) == list:
        for i,k in enumerate(new_something):
            if type(k) == np.ndarray:
                new_something[i] = k.tolist()
            if type(k) == dict:
                new_something[i] = unnumpyify(k)
            elif type(k) == list:
                new_something[i] = unnumpyify(k)
    elif type(new_something) == set:
        for k in new_something:
            if type(k) == np.ndarray:
                new_something.remove(k)
                new_k = k.tolist()
                new_something.add(new_k)
            if type(k) == dict:
                new_something.remove(k)
                new_k = unnumpyify(k)
                new_something.add(new_k)
            elif type(k) == list:
                new_something.remove(k)
                new_k = unnumpyify(k)
                new_something.add(new_k)
    return new_something

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