I have a nested dictionary that I am trying to convert to JSON using json.dumps(unserialized_data), indent=2). The dictionary currently looks like this:
{
"status": "SUCCESS",
"data": {
"cal": [
{
"year": 2022,
"month": 8,
"a": [
{
"a_id": 1,
"b": [
{
"abc_id": 1,
"val": 2342
}
]
}
]
},
{
"year": 2022,
"month": 9,
"a": [
{
"a_id": 2,
"b": [
{
"abc_id": 3,
"val": 2342
}
]
}
]
}
]
}
}
How can I convert all integers of type int64 to int while leaving the structure of the dict and values of any other data type unaffected?
If blhsing's condition doesn't apply, you can drill down recursively into the dictionary and cast any np.int64 to int:
def cast_type(container, from_types, to_types):
if isinstance(container, dict):
# cast all contents of dictionary
return {cast_type(k, from_types, to_types): cast_type(v, from_types, to_types) for k, v in container.items()}
elif isinstance(container, list):
# cast all contents of list
return [cast_type(item, from_types, to_types) for item in container]
else:
for f, t in zip(from_types, to_types):
# if item is of a type mentioned in from_types,
# cast it to the corresponding to_types class
if isinstance(container, f):
return t(container)
# None of the above, return without casting
return container
from_types and to_types are containers where corresponding elements give the type to convert from, and the type to convert to.
Run your dictionary through this function, then dump it to json:
import numpy as np
import json
d = {
"str_data": "foo bar",
"lst": [ np.int64(1000), np.float64(1.234) ],
"dct": {"foo": "bar", "baz": np.float64(6.789), "boo": np.int64(10)}
}
print(json.dumps(d, indent=2)) # throws error
print(json.dumps(
cast_type(d,
[np.int64, np.float64],
[int, float]),
indent=2))
Prints the dictionary as JSON:
{
"str_data": "foo bar",
"lst": [
1000,
1.234
],
"dct": {
"foo": "bar",
"baz": 6.789,
"boo": 10
}
}