The answer is
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 could also use default argument for example:
def myconverter(o):
if isinstance(o, np.float32):
return float(o)
json.dump(data, default=myconverter)
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.
You can use Pandas:
import pandas as pd
pd.Series(your_array).to_json(orient='values')
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)
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' )
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()
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
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)
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,
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!
Source: Stackoverflow.com