Also, some very interesting information further on lists vs. arrays in Python ~> Python List vs. Array – when to use?

It could be noted that once I convert my arrays into a list before saving it in a JSON file, in my deployment right now anyways, once I read that JSON file for use later, I can continue to use it in a list form (as opposed to converting it back to an array).

AND actually looks nicer (in my opinion) on the screen as a list (comma seperated) vs. an array (not-comma seperated) this way.

Using @travelingbones’s .tolist() method above, I’ve been using as such (catching a few errors I’ve found too):

SAVE DICTIONARY

def writeDict(values, name):
    writeName = DIR+name+'.json'
    with open(writeName, "w") as outfile:
        json.dump(values, outfile)

READ DICTIONARY

def readDict(name):
    readName = DIR+name+'.json'
    try:
        with open(readName, "r") as infile:
            dictValues = json.load(infile)
            return(dictValues)
    except IOError as e:
        print(e)
        return('None')
    except ValueError as e:
        print(e)
        return('None')

Hope this helps!

Here is an implementation that work for me and removed all nans (assuming these are simple object (list or dict)):

from numpy import isnan

def remove_nans(my_obj, val=None):
    if isinstance(my_obj, list):
        for i, item in enumerate(my_obj):
            if isinstance(item, list) or isinstance(item, dict):
                my_obj[i] = remove_nans(my_obj[i], val=val)

            else:
                try:
                    if isnan(item):
                        my_obj[i] = val
                except Exception:
                    pass

    elif isinstance(my_obj, dict):
        for key, item in my_obj.iteritems():
            if isinstance(item, list) or isinstance(item, dict):
                my_obj[key] = remove_nans(my_obj[key], val=val)

            else:
                try:
                    if isnan(item):
                        my_obj[key] = val
                except Exception:
                    pass

    return my_obj

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

You can use Pandas:

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

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))

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)

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.

This is a different answer, but this might help to help people who are trying to save data and then read it again.
There is hickle which is faster than pickle and easier.
I tried to save and read it in pickle dump but while reading there were lot of problems and wasted an hour and still didn’t find solution though I was working on my own data to create a chat bot.

vec_x and vec_y are numpy arrays:

data=[vec_x,vec_y]
hkl.dump( data, 'new_data_file.hkl' )

Then you just read it and perform the operations:

data2 = hkl.load( 'new_data_file.hkl' )

You could also use default argument for example:

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

json.dump(data, default=myconverter)

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)

May do simple for loop with checking types:

with open("jsondontdoit.json", 'w') as fp:
    for key in bests.keys():
        if type(bests[key]) == np.ndarray:
            bests[key] = bests[key].tolist()
            continue
        for idx in bests[key]:
            if type(bests[key][idx]) == np.ndarray:
                bests[key][idx] = bests[key][idx].tolist()
    json.dump(bests, fp)
    fp.close()

TypeError: array([[0.46872085, 0.67374235, 1.0218339 , 0.13210179, 0.5440686 , 0.9140083 , 0.58720225, 0.2199381 ]], dtype=float32) is not JSON serializable

The above-mentioned error was thrown when i tried to pass of list of data to model.predict() when i was expecting the response in json format.

> 1        json_file = open('model.json','r')
> 2        loaded_model_json = json_file.read()
> 3        json_file.close()
> 4        loaded_model = model_from_json(loaded_model_json)
> 5        #load weights into new model
> 6        loaded_model.load_weights("model.h5")
> 7        loaded_model.compile(optimizer='adam', loss='mean_squared_error')
> 8        X =  [[874,12450,678,0.922500,0.113569]]
> 9        d = pd.DataFrame(X)
> 10       prediction = loaded_model.predict(d)
> 11       return jsonify(prediction)

But luckily found the hint to resolve the error that was throwing
The serializing of the objects is applicable only for the following conversion
Mapping should be in following way
object – dict
array – list
string – string
integer – integer

If you scroll up to see the line number 10
prediction = loaded_model.predict(d) where this line of code was generating the output
of type array datatype , when you try to convert array to json format its not possible

Finally i found the solution just by converting obtained output to the type list by
following lines of code

prediction = loaded_model.predict(d)
listtype = prediction.tolist()
return jsonify(listtype)

Bhoom! finally got the expected output,

use NumpyEncoder it will process json dump successfully.without throwing – NumPy array is not JSON serializable

import numpy as np
import json
from numpyencoder import NumpyEncoder
arr = array([   0,  239,  479,  717,  952, 1192, 1432, 1667], dtype=int64) 
json.dumps(arr,cls=NumpyEncoder)

The other answers will not work if someone else’s code (e.g. a module) is doing the json.dumps(). This happens often, for example with webservers that auto-convert their return responses to JSON, meaning we can’t always change the arguments for json.dump() .

This answer solves that, and is based off a (relatively) new solution that works for any 3rd party class (not just numpy).

TLDR

pip install json_fix

import json_fix # import this anytime before the JSON.dumps gets called
import json

# create a converter
import numpy
json.fallback_table[numpy.ndarray] = lambda array: array.tolist()

# no additional arguments needed: 
json.dumps(
   dict(thing=10, nested_data=numpy.array((1,2,3)))
)
#>>> '{"thing": 10, "nested_data": [1, 2, 3]}'