Int64 with null value mangles large-ish integers

[craigdsthompson]

Code Sample, a copy-pastable example if possible

import pandas as pd
import numpy as np

x = 9999999999999999
y = 123123123123123123
z = 10000000000000543
s = pd.Series([1, 2, 3, x, y, z], dtype="Int64")
s[3] == x  # True
s
s2 = pd.Series([1, 2, 3, x, y, z, np.nan], dtype="Int64")
s2[3] == x  # False
s2
np.iinfo(np.int64).max

With interpreter output:

>>> import pandas as pd
>>> import numpy as np
>>> 
>>> x = 9999999999999999
>>> y = 123123123123123123
>>> z = 10000000000000543
>>> s = pd.Series([1, 2, 3, x, y, z], dtype="Int64")
>>> s[3] == x  # True
True
>>> s
0                     1
1                     2
2                     3
3      9999999999999999
4    123123123123123123
5     10000000000000543
dtype: Int64
>>> s2 = pd.Series([1, 2, 3, x, y, z, np.nan], dtype="Int64")
>>> s2[3] == x  # False
False
>>> s2
0                     1
1                     2
2                     3
3     10000000000000000
4    123123123123123120
5     10000000000000544
6                   NaN
dtype: Int64
>>> np.iinfo(np.int64).max
9223372036854775807

Problem description

It seams that the presence of np.nan values in a column being typed as Int64 causes some non-null values to be mangled. This seems to happen with large-ish values (but still below the max int limit).

Given that Int64 is the "Nullable integer" data type, null values should be allowed, and should certainly not silently change the values of other elements in the data frame.

Expected Output

>>> import pandas as pd
>>> import numpy as np
>>> 
>>> x = 9999999999999999
>>> y = 123123123123123123
>>> z = 10000000000000543
>>> s = pd.Series([1, 2, 3, x, y, z], dtype="Int64")
>>> s[3] == x  # True
True
>>> s
0                     1
1                     2
2                     3
3      9999999999999999
4    123123123123123123
5     10000000000000543
dtype: Int64
>>> s2 = pd.Series([1, 2, 3, x, y, z, np.nan], dtype="Int64")
>>> s2[3] == x  # True (was False above)
True
>>> s2
0                     1
1                     2
2                     3
3      9999999999999999
4    123123123123123123
5     10000000000000543
6                   NaN
dtype: Int64
>>> np.iinfo(np.int64).max
9223372036854775807

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.7.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.0.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_CA.UTF-8
LOCALE : en_CA.UTF-8

pandas : 0.25.3
numpy : 1.17.4
pytz : 2019.3
dateutil : 2.8.1
pip : 10.0.1
setuptools : 39.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None

[WillAyd]

Thanks for the report. Must be an unwanted float cast in the mix as precision looks to get lost around the same range:

>>> x = 2 ** 53
>>> y = 2 ** 53 + 1
>>> z = 2 ** 53  + 2
>>> s2 = pd.Series([1, 2, 3, x, y, z, np.nan], dtype="Int64")
>>> s2
0                   1
1                   2
2                   3
3    9007199254740992
4    9007199254740992
5    9007199254740994
6                 NaN

Investigation and PRs certainly welcome

[jorisvandenbossche]

This is caused by a converting the list to a numpy array, and letting numpy do type inference. Because there is a np.nan, numpy will create a float array. And this then indeed causes this precision loss.

With a quick hack like:

--- a/pandas/core/arrays/integer.py
+++ b/pandas/core/arrays/integer.py
@@ -205,7 +205,10 @@ def coerce_to_array(values, dtype, mask=None, copy=False):
             mask = mask.copy()
         return values, mask
 
-    values = np.array(values, copy=copy)
+    if isinstance(values, list):
+        values = np.array(values, dtype=object)
+    else:
+        values = np.array(values, copy=copy)
     if is_object_dtype(values):
         inferred_type = lib.infer_dtype(values, skipna=True)
         if inferred_type == "empty":

I get the correct behaviour (by ensuring we convert the list to an object array, so we don't loose precision due to the intermediate float array, and we can do the conversion to an integer array ourselves).

For a proper fix, the if/else check will need to be a bit more advanced. I think we should basically check if the input values already are a ndarray or have a dtype, and if that is the case keep that dtype, otherwise convert to object dtype.

Contributions certainly welcome!

[rushabh-v]

take

[fcollman]

is this behavior caused by the same bug?
Here there are no NaNs involved, so casting was not even needed

>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({"id":[864691135341199281, 864691135341199281]}, dtype='Int64')
>>> df['cid']=[49912377,49912377]
>>> print(df)
                   id       cid
0  864691135341199281  49912377
1  864691135341199281  49912377
>>> print(df.groupby('cid').first())
                          id
cid                         
49912377  864691135341199232
>>> 
>>> 
>>> print(df.groupby('cid').first().id.dtype, df.id.dtype)
Int64 Int64
>>> 
>>> import numpy as np
>>> print(np.int64(np.float64(864691135341199281)))
864691135341199232

[rushabh-v]

#31108 is the reason!