CODEWITHSUNDEEP
pythonpython-3.xnumpy
fightstarr20
fightstarr20

Reputation: 12608

Python - Convert numpy array to JSON

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

[[148 362]
 [153 403]
 [163 443]
 [172 483]
 [186 521]
 [210 553]
 [239 581]
 [273 604]
 [314 611]
 [353 602]]

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?

Upvotes: 3

Views: 8817

Answers (4)

Lotramus
Lotramus

Reputation: 59

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

Upvotes: 0

Fatih1923
Fatih1923

Reputation: 2691

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.

Upvotes: 2

andnik
andnik

Reputation: 2804

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.

Upvotes: 2

jacob
jacob

Reputation: 256

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]]

Upvotes: 3

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