Closed
Description
Code Sample
import pandas as pd
import numpy as np
print(pd.array([23842938553820651, np.nan], dtype=pd.Int64Dtype())) # from the docs
print()
print(pd.array([23842938553820651, pd.NaT], dtype=pd.Int64Dtype()))
Problem description
Examples in the documentation of Nullable integers don't work as expected https://pandas.pydata.org/pandas-docs/stable/user_guide/integer_na.html
The integers are converted to float before casting to the new Nullable integer, so they lose precision anyway (which is the the most usual reason for using them, as stated in the docs). Using the native pd.NaT solves the problem.
Expected Output
<IntegerArray>
[23842938553820651, NaN]
Length: 2, dtype: Int64
<IntegerArray>
[23842938553820651, NaN]
Length: 2, dtype: Int64
Output of code sample
<IntegerArray>
[23842938553820652, NaN]
Length: 2, dtype: Int64
<IntegerArray>
[23842938553820651, NaN]
Length: 2, dtype: Int64
Output of pd.show_versions()
commit : None
pandas : 0.25.3
numpy : 1.17.4
pytz : 2018.9
dateutil : 2.8.0
pip : 19.0.3
setuptools : 41.0.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.1
IPython : 7.5.0
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : None
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : 0.14.1
pytables : None
s3fs : None
scipy : None
sqlalchemy : 1.3.2
tables : None
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None