Python - TypeError: Object Of Type 'Int64' Is Not JSON Serializable

To solve the error - object of type int64 is not json serializable -

My solution

numpyencoder and its repository.

from numpyencoder import NumpyEncoder

numpy_data = np.array([0, 1, 2, 3])

with open(json_file, 'w') as file:

    json.dump(numpy_data, file, indent=4, sort_keys=True,

              separators=(', ', ': '), ensure_ascii=False,

              cls=NumpyEncoder)

The breakdown

If you dig into hmallen's code in the numpyencoder/numpyencoder.py file you'll see that it's very similar to @Jie Yang's answer:

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Python - TypeError: Object of type 'int64' is not JSON serializable

Asked 3 years, 11 months ago

Modified 6 months ago

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15

I have a data frame that stores store name and daily sales count. I am trying to insert this to Salesforce using the Python script below.

However, I get the following error:

TypeError: Object of type 'int64' is not JSON serializable

Below, there is the view of the data frame.

Storename,Count

Store A,10

Store B,12

Store C,5

I use the following code to insert it to Salesforce.

update_list = []

for i in range(len(store)):

    update_data = {

        'name': store['entity_name'].iloc[i],

        'count__c': store['count'].iloc[i] 

    }

    update_list.append(update_data)

sf_data_cursor = sf_datapull.salesforce_login()

sf_data_cursor.bulk.Account.update(update_list)

I get the error when the last line above gets executed.

How do I fix this?

python

numpy

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edited Jul 6, 2021 at 22:55

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Russia Must Remove Putin♦

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asked Jun 18, 2018 at 19:39

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dark horse

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That call to range is suspicious. You are taking len(store) and wrapping that in a tuple, and then calling range on the tuple. If you remove one set of parentheses, does it fix the code? That is, try this: for i in range(len(store)):. – 

Tim Johns

 Jun 18, 2018 at 20:00 

@TimJohns A pair of parentheses around a number does not make it a tuple. (34) is still a number 34. But (34,) is a tuple. – 

DYZ

 Jun 18, 2018 at 20:04 

@DyZ Good point, I didn't realise that parentheses with a single argument are treated differently than if there are multiple arguments. – 

Tim Johns

 Jun 18, 2018 at 20:13

2

@TimJohns The parents are irrelevant. a=34, is also a tuple – 

Sebastian Wozny

 Feb 20, 2020 at 15:27

There is an open bug report about this issue: bugs.python.org/issue24313 – 

Mihai Capotă

 Jul 22, 2020 at 20:59

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10 Answers

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159

json does not recognize NumPy data types. Convert the number to a Python int before serialise the object:

'count__c': int(store['count'].iloc[i])

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answered Jun 18, 2018 at 20:07

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DYZ

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Thanks a lot buddy! – 

codingbruh

 May 26, 2021 at 10:04

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142

You can define your own encoder to solve this problem.

import json

import numpy as np

class NpEncoder(json.JSONEncoder):

    def default(self, obj):

        if isinstance(obj, np.integer):

            return int(obj)

        if isinstance(obj, np.floating):

            return float(obj)

        if isinstance(obj, np.ndarray):

            return obj.tolist()

        return super(NpEncoder, self).default(obj)

# Your codes .... 

json.dumps(data, cls=NpEncoder)

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edited Jul 31, 2021 at 11:24

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Tommy

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answered Sep 12, 2019 at 22:29

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Jie Yang

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2

@IvanCamilitoRamirezVerdes That's exactly what I was looking for so it was quite helpful, for me. I like the readability of throwing in an encoding class to the json.dumps function as well. – 

Jason R Stevens CFA

 May 20, 2020 at 1:54

did not work for me for some reason... Does this support recursive calls? – 

adir abargil

 Dec 2, 2020 at 14:43

2

I also needed to add elif isinstance(obj, np.bool_):             return bool(obj) – 

James McKeown

 Feb 8, 2021 at 19:32

2

I've edited this answer to remove the unnecessary "elifs' ' since there are returns. – 

Tommy

 Jul 31, 2021 at 11:28

For most cases, this is quite an overkill. The solution by Tharindu Sathischandra is more straightforward. – 

Gnnr

 Mar 11 at 7:47

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33

I'll throw in my answer to the ring as a bit more stable version of @Jie Yang's excellent solution.

My solution

numpyencoder and its repository.

from numpyencoder import NumpyEncoder

numpy_data = np.array([0, 1, 2, 3])

with open(json_file, 'w') as file:

    json.dump(numpy_data, file, indent=4, sort_keys=True,

              separators=(', ', ': '), ensure_ascii=False,

              cls=NumpyEncoder)

The breakdown

If you dig into hmallen's code in the numpyencoder/numpyencoder.py file you'll see that it's very similar to @Jie Yang's answer:

class NumpyEncoder(json.JSONEncoder):

    """ Custom encoder for numpy data types """

    def default(self, obj):

        if isinstance(obj, (np.int_, np.intc, np.intp, np.int8,

                            np.int16, np.int32, np.int64, np.uint8,

                            np.uint16, np.uint32, np.uint64)):

            return int(obj)

        elif isinstance(obj, (np.float_, np.float16, np.float32, np.float64)):

            return float(obj)

        elif isinstance(obj, (np.complex_, np.complex64, np.complex128)):

            return {'real': obj.real, 'imag': obj.imag}

        elif isinstance(obj, (np.ndarray,)):

            return obj.tolist()

elif isinstance(obj, (np.bool_)):

            return bool(obj)

        elif isinstance(obj, (np.void)): 

            return None

        return json.JSONEncoder.default(self, obj)