Python JSON encoder to support datetime?

Question

is there any elegant way to make Python JSON encoder support datetime? some 3rd party module or easy hack?

I am using tornado\'s database wrapper to fetch some rows

Answer 1

Convert the datetime type into a unix timestamp, then encode the contents into a json.

e.g. : http://codepad.org/k3qF09Kr

Answer 2

Create a custom decoder/encoder:

class CustomJSONEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, datetime.datetime):
            return http_date(obj)
        if isinstance(obj, uuid.UUID):
            return str(obj)
        return json.JSONEncoder.default(self, obj)

class CustomJSONDecoder(json.JSONDecoder):
    def __init__(self, *args, **kwargs):
        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)

    def object_hook(self, source):
        for k, v in source.items():
            if isinstance(v, str):
                try:
                    source[k] = datetime.datetime.strptime(str(v), '%a, %d %b %Y %H:%M:%S %Z')
                except:
                    pass
        return source

Answer 3

Just create a custom encoder

(the small but important addition to Cole's answer is the handling of pd.NaT (or null/empty timestamp values), since without the addition you will get very weird timestamp conversions for NaT/missing timestamp data)

class CustomEncoder(json.JSONEncoder):
    def default(self, obj):
        if pd.isnull(obj):
            return None
        elif isinstance(obj, datetime):
            return obj.isoformat()
        elif isinstance(obj, date):
            return obj.isoformat()
        elif isinstance(obj, timedelta):
            return (datetime.min + obj).time().isoformat()
        else:
            return super(CustomEncoder, self).default(obj)

Then use it to encode a dataframe:

df_as_dict = df.to_dict(outtype = 'records')  # transform to dict

df_as_json = CustomEncoder().encode(df_as_dict) #transform to json

Since the encoder standardized the data, the regular decoder will act fine in transforming it back to a dataframe:

result_as_dict = json.JSONDecoder().decode(df_as_json) # decode back to dict

result_df = pd.DataFrame(result)  # transform dict back to dataframe

Of course this will also work if you put the dataframe into a larger dict before encoding, e.g

input_dict = {'key_1':val_1,'key_2':val_2,...,'df_as_dict':df_as_dict}
input_json = CustomEncoder().encode(input_dict)
input_json_back_as_dict = json.JSONDecoder().decode(input_json)
input_df_back_as_dict = input_json_back_as_dict['df_as_dict']
input_df_back_as_df = pd.DataFrame(input_df_back_as_dict)