to_dict doesn't convert np.int64 to python integers

[kszucs]

Code Sample, a copy-pastable example if possible

# Your code here
data = [{'id': 11, 'text': 'Osv1wbZoL'},
        {'id': 0, 'text': 'KQpPReW3S9nZOS3'},
        {'id': 0, 'text': 'cbqLhjrb0B2Ah6E'},
        {'id': 3, 'text': 'qu1Jlnyba'},
        {'id': 14, 'text': 'aJUv5DBjbcGc3'},
        {'id': 12, 'text': 'Yobf9'},
        {'id': 4, 'text': 'awzZCV'},
        {'id': 4, 'text': '3NvBAVL'},
        {'id': 11, 'text': '80sPCxIf9s5wmEZ1'},
        {'id': 5, 'text': 'afrPD0X6mIzFK'}]

df = pd.DataFrame(data)
# out:
# id       int64
# text    object
# dtype: object

type(df[['id', 'text']].to_dict(orient='records')[0]['id'])
# out: int

type(df[['id']].to_dict(orient='records')[0]['id'])
# out: numpy.int64

Problem description

depending on the count of output columns, numpy integers getting converted to python integers

afterwards both json.dumps and ujson.dumps fails to encode

Expected Output

int for both cases

Output of pd.show_versions()

INSTALLED VERSIONS ------------------ commit: None python: 3.6.1.final.0 python-bits: 64 OS: Darwin OS-release: 16.5.0 machine: x86_64 processor: i386 byteorder: little LC_ALL: en_US.UTF-8 LANG: en_US.UTF-8 LOCALE: en_US.UTF-8

pandas: 0.19.2
nose: 1.3.7
pip: 9.0.1
setuptools: 33.1.1.post20170320
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
statsmodels: 0.8.0
xarray: None
IPython: 5.3.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.0
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.3.0
numexpr: 2.6.2
matplotlib: 2.0.0
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.999
httplib2: None
apiclient: None
sqlalchemy: 1.1.9
pymysql: 0.7.10.None
psycopg2: 2.6.2 (dt dec pq3 ext lo64)
jinja2: 2.9.5
boto: None
pandas_datareader: None

[TomAugspurger]

Looks like a duplicate of #13258

@kszucs Feel free to dive in if you're interested.

[TomAugspurger]

afterwards both json.dumps and ujson.dumps fails to encode

If you're only dumping to JSON, then df.to_json might work for you. It knows how to serialize numpy types.

[kszucs]

Sadly there is no orient I could use in to_json.

[makmanalp]

@kszucs I think that has been fixed recently FYI: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_json.html

[0anton]

Still observe the same behavior (numpy.int64 instead of python int type) in current pandas 0.23.4.
Related #13258, which @TomAugspurger used to close the issue, must bring the fix in 0.21.0.
Vote for re-open.

[nataschaZima]

I am having the same issue. I have to convert CSV output to dict, doing it with pandas (df.to_dict('records')) gives me in one case correct python types, in the other case still leaves the types as numpy. Is there any workaround at all? I cannot find proper documentation on how to user pandas.compat with pandas method to_dict.

[stevenanton-bc]

For posterity, I ended up doing something like json.loads(df.to_json()) to coerce the numpy data types to internal types (e.g. np.int64 to int).

[kumudraj]

use:
from pandas.io.json import dumps
import json
data_dictionary = df.iloc[0].to_dict()
data_dictionary=json.loads(dumps(data_dictionary,double_precision=0))