read_json reads large integers as strings incorrectly if dtype not explicitly mentioned

[Udayraj123]

Code Sample (Original Problem)

json_content="""
{ 
    "1": {
        "tid": "9999999999999998", 
    }, 
    "2": {
        "tid": "9999999999999999", 
    },
    "3": {
        "tid": "10000000000000001", 
    },
    "4": {
        "tid": "10000000000000002", 
    }
}
"""
df=pd.read_json(json_content,
                orient='index', # read as transposed
                convert_axes=False, # don't convert keys to dates
        )
print(df.info())
print(df)

Problem description

I'm using pandas to load json data, but found some strange behaviour in the read_json function.
In the above code, the integers as strings aren't read correctly, though there shouldn't be an overflow case as the values are well within the integer range.

It is reading correctly on explictly specifying the argument dtype=int, but I don't understand why. What changes when we specify the dtype?

Corresponding SO discussion here:

Current Output

<class 'pandas.core.frame.DataFrame'>
Index: 4 entries, 1 to 4
Data columns (total 1 columns):
tid    4 non-null int64
dtypes: int64(1)
memory usage: 64.0+ bytes
None
                 tid
1   9999999999999998
2  10000000000000000
3  10000000000000000
4  10000000000000002

Expected Output

The tid's should have been stored correctly.

None
                 tid
1   9999999999999998
2   9999999999999999
3  10000000000000001
4  10000000000000002

A minimal pytest example

import pytest
import numpy as np
import pandas as pd
from pandas import (Series, DataFrame, DatetimeIndex, Timestamp, read_json, compat)
from pandas.util import testing as tm

@pytest.mark.parametrize('dtype', ['int'])
def test_large_ints_from_json_strings(dtype):
    # GH 20608
    df1 = DataFrame([9999999999999999,10000000000000001],columns=['tid'])
    df_temp = df1.copy().astype(str)
    df2 = read_json(df_temp.to_json())
    assert (df1 == df2).all()[0] == True # currently False

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.13.0-37-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_IN
LOCALE: en_IN.ISO8859-1

pandas: 0.22.0
pytest: None
pip: 9.0.1
setuptools: 39.0.1
Cython: None
numpy: 1.14.0
scipy: None
pyarrow: None
xarray: None
IPython: 5.1.0
sphinx: None
patsy: None
dateutil: 2.6.1
pytz: 2017.3
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.1.1
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.999999999
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None

[WillAyd]

Hmm well the problem is stemming from the code in _try_convert_data:

pandas/pandas/io/json/json.py

Line 659 in 6d610a4

def _try_convert_data(self, name, data, use_dtypes=True,

It looks like that method is converting the parsed object to a float and then to an int, which is causing the loss of precision on your large numbers

In [10]: int(float("9999999999999999"))
Out[10]: 10000000000000000

Want to try a fix in a PR?

[Udayraj123]

Sure! Though I'm new to source code of pandas, but I can try :)

[WillAyd]

Feel free to ask any questions here, or if you submit a PR make sure to reference this issue. There's plenty of helpful people here to get you through the process

[Udayraj123]

Actually I have a few questions,

1. The assert_frame_equal is also seemingly failing to detect difference between 9999999999999999 and 10000000000000000. Currently I've made a test file (temporarily) at pandas/tests/io/json/my_test.py which contains the following code:

import pytest
import numpy as np
import pandas as pd
from pandas import (Series, DataFrame, DatetimeIndex, Timestamp, read_json, compat)
from pandas.util import testing as tm

@pytest.fixture
def input_json():
    json_content="""
    { 
        "0" : {"tid":"9999999999999999"},
        "1" : {"tid":"10000000000000001"}
    }
    """
    return json_content

@pytest.fixture
def expected_df():
    return DataFrame([9999999999999999,10000000000000001],columns=['tid'])


@pytest.mark.parametrize('dtype', ['int64','int'])
def test_large_ints_from_json_strings(dtype):
    # GH 20608
    data = read_json(input_json(),orient='index')
    # data = read_json(input_json(),orient='index',dtype={'tid':dtype})
    new_data = expected_df()
    print('')
    print(new_data)
    print(data)
    tm.assert_frame_equal(new_data,data)

I am testing with the following command.
pytest my_test.py -v -s --full-trace
And getting PASSED output, is this expected? If yes, how can I verify that correct dataframe is generated after a fix?

2. After finalizing, I'm thinking of placing this into test/io/json/test_pandas.py, the function name would be test_large_ints_from_json_strings. Does this seem fine?

I've gone through the following links till now.
https://pandas.pydata.org/pandas-docs/stable/contributing.html
https://github.com/pandas-dev/pandas/wiki/Testing

[Udayraj123]

Also (in reference to above), the test is changing the 9999999999999999 from int to float at line 693.

pandas/pandas/io/json/json.py

Line 693 in 6d610a4

data = data.astype('float64')

And then converts it back to int at this line

pandas/pandas/io/json/json.py

Line 714 in 6d610a4

new_data = data.astype('int64')

which changes it's value to 10000000000000000. Just below it is a test for data equivalence, but it is also passing without an issue..

If we are passing dtype={'tid':int}, it will directly return as int without converting to float. But I'm assuming we can't do the same in the above case(as it may affect pandas default type conversions)?

[WillAyd]

1. Just in general for your test you don't need a fixture unless you plan on reusing that elsewhere. For now just put all of your items in the method definition. And for what it's worth shouldn't your test be failing? Isn't that the bug you are trying to fix?

2. Yes I think that location is fine

3. That equivalence test is performed against a data variable that was already cast to float, so isn't the precision issue already?

[Udayraj123]

1. Okay, was just trying that out 😄 and Yes it should be failing, the two dataframes are different (one with 999.. and other with 100..), but the assert_frame_equal function is not detecting that. Can you please show me a sample way of testing those two dataframes being different?

2. Cool!

3. Yes, precision issue is there, Maybe we can cast it back to int and then compare, is that right?

Adding to point 1: Comparing with the (new_data == data).all() method is giving correct result (False), while assert_frame_equal is still passing the test. I don't understand why?

def test_large_ints_from_json_strings(dtype):
    json_content="""
    { 
        "0" : {"tid":"9999999999999999"},
        "1" : {"tid":"10000000000000001"}
    }
    """
    data = read_json(json_content,orient='index')
    new_data = DataFrame([9999999999999999,10000000000000001],columns=['tid'])
    # new_data = new_data.astype('int64') 
    print((new_data == data).all()) #False
    tm.assert_frame_equal(new_data,data) #PASSED?

[WillAyd]

Sorry I misread your original point but the below definitely fails for me:

In [3]: json_content="""{ 
   ...:     "0" : {"tid":"9999999999999999"},
   ...:     "1" : {"tid":"10000000000000001"}
   ...: }"""
In [4]: exp = pd.DataFrame([9999999999999999,10000000000000001],columns=['tid'])
In [6]: tm.assert_frame_equal(pd.read_json(json_content), exp)
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)

Perhaps try using the complete example from your original post.

As to your solution, once the precision is lost casting back to int will not work. You'll have to think through another way - maybe we should be attempting to int first instead of float? Not saying that's the answer but think through that.

[Udayraj123]

In your example the orient='index' is missing which transposes the dataframe (making tid a column). So the assert is failing because the transpose isn't taken.

Instead of json_string, I've thought of this more intuitive way using to_json() (which is still passing when it shouldn't)-

@pytest.mark.parametrize('dtype', ['int'])
def test_large_ints_from_json_strings(dtype):
    # GH 20608
    df1 = DataFrame([9999999999999999,10000000000000001],columns=['tid'])
    df_temp = df1.copy().astype(str)
    df2 = read_json(df_temp.to_json())
    print((df1 == df2).all()) #False
    tm.assert_frame_equal(df1,df2) #PASSED?

Hmm I did think about the order of casting types, seems there has to be an additional case. I'll send a patch when I find a solution. But I would appreciate if someone else was working on this too

[Udayraj123]

I've updated a working test in the main issue at the top

[WillAyd]

I haven't looked into your latest issue but you are making this more complicated than it needs to be. Just have one frame constructed via read_json and the other constructed manually. Don't do any type conversions, printing, or copying. You also don't need to parametrize it

[gfyoung]

@Udayraj123 : Do you have a patch for your original bug? I would submit that as a PR. We can move the discussion about the actual changes / test there.

[Udayraj123]

@gfyoung : No I don't have a patch yet.