mpld3 results in TypeError: Object of type 'ndarray' is not JSON serializable [duplicate]

Question

After creating a NumPy array, and saving it as a Django context variable, I receive the following error when loading the webpage:

array([   0,  239,  479,  717,  952, 1192, 1432, 1667], dtype=int64) is not JSON serializable

What does this mean?

Answer 1

I regularly "jsonify" np.arrays. Try using the ".tolist()" method on the arrays first, like this:

import numpy as np
import codecs, json 

a = np.arange(10).reshape(2,5) # a 2 by 5 array
b = a.tolist() # nested lists with same data, indices
file_path = "/path.json" ## your path variable
json.dump(b, codecs.open(file_path, 'w', encoding='utf-8'), 
          separators=(',', ':'), 
          sort_keys=True, 
          indent=4) ### this saves the array in .json format

In order to "unjsonify" the array use:

obj_text = codecs.open(file_path, 'r', encoding='utf-8').read()
b_new = json.loads(obj_text)
a_new = np.array(b_new)

Answer 2

Store as JSON a numpy.ndarray or any nested-list composition.

class NumpyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        return json.JSONEncoder.default(self, obj)

a = np.array([[1, 2, 3], [4, 5, 6]])
print(a.shape)
json_dump = json.dumps({'a': a, 'aa': [2, (2, 3, 4), a], 'bb': [2]}, 
                       cls=NumpyEncoder)
print(json_dump)

Will output:

(2, 3)
{"a": [[1, 2, 3], [4, 5, 6]], "aa": [2, [2, 3, 4], [[1, 2, 3], [4, 5, 6]]], "bb": [2]}

To restore from JSON:

json_load = json.loads(json_dump)
a_restored = np.asarray(json_load["a"])
print(a_restored)
print(a_restored.shape)

Will output:

[[1 2 3]
 [4 5 6]]
(2, 3)

Answer 3

I found the best solution if you have nested numpy arrays in a dictionary:

import json
import numpy as np

class NumpyEncoder(json.JSONEncoder):
    """ Special json encoder for numpy types """
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        return json.JSONEncoder.default(self, obj)

dumped = json.dumps(data, cls=NumpyEncoder)

with open(path, 'w') as f:
    json.dump(dumped, f)

Thanks to this guy.

Answer 4

You can use Pandas:

import pandas as pd
pd.Series(your_array).to_json(orient='values')

Answer 5

Use the json.dumps default kwarg:

default should be a function that gets called for objects that can’t otherwise be serialized. ... or raise a TypeError

In the default function check if the object is from the module numpy, if so either use ndarray.tolist for a ndarray or use .item for any other numpy specific type.

import numpy as np

def default(obj):
    if type(obj).__module__ == np.__name__:
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        else:
            return obj.item()
    raise TypeError('Unknown type:', type(obj))

dumped = json.dumps(data, default=default)

Answer 6

This is not supported by default, but you can make it work quite easily! There are several things you'll want to encode if you want the exact same data back:

• The data itself, which you can get with obj.tolist() as @travelingbones mentioned. Sometimes this may be good enough.
• The data type. I feel this is important in quite some cases.
• The dimension (not necessarily 2D), which could be derived from the above if you assume the input is indeed always a 'rectangular' grid.
• The memory order (row- or column-major). This doesn't often matter, but sometimes it does (e.g. performance), so why not save everything?

Furthermore, your numpy array could part of your data structure, e.g. you have a list with some matrices inside. For that you could use a custom encoder which basically does the above.

This should be enough to implement a solution. Or you could use json-tricks which does just this (and supports various other types) (disclaimer: I made it).

pip install json-tricks

Then

data = [
    arange(0, 10, 1, dtype=int).reshape((2, 5)),
    datetime(year=2017, month=1, day=19, hour=23, minute=00, second=00),
    1 + 2j,
    Decimal(42),
    Fraction(1, 3),
    MyTestCls(s='ub', dct={'7': 7}),  # see later
    set(range(7)),
]
# Encode with metadata to preserve types when decoding
print(dumps(data))

Answer 7

I had a similar problem with a nested dictionary with some numpy.ndarrays in it.

def jsonify(data):
    json_data = dict()
    for key, value in data.iteritems():
        if isinstance(value, list): # for lists
            value = [ jsonify(item) if isinstance(item, dict) else item for item in value ]
        if isinstance(value, dict): # for nested lists
            value = jsonify(value)
        if isinstance(key, int): # if key is integer: > to string
            key = str(key)
        if type(value).__module__=='numpy': # if value is numpy.*: > to python list
            value = value.tolist()
        json_data[key] = value
    return json_data

Answer 8

You could also use default argument for example:

def myconverter(o):
    if isinstance(o, np.float32):
        return float(o)

json.dump(data, default=myconverter)