API: query / boolean selection with nullable dtypes with NAs · Issue #31503 · pandas-dev/pandas (original) (raw)

Code Sample

Create a dataframe with nullable integer, string, and float data types.

df = df = pd.DataFrame({'a': [1, 3, np.nan], 'b': ['rain', 'shine', None], 'a_float': [1.1, 3.3, np.nan]}) df = df.convert_dtypes() df

Verify data types and attempt to use query

df.dtypes a Int64 b string a_float float64

df.query('a > 2') # same as df[df['a'] > 2]

ValueError Traceback (most recent call last) ~/Documents/Code Practice/pandas-dev/pandas/pandas/core/frame.py in query(self, expr, inplace, **kwargs) 3227 try: -> 3228 new_data = self.loc[res] 3229 except ValueError:

~/Documents/Code Practice/pandas-dev/pandas/pandas/core/indexing.py in getitem(self, key) 1683 maybe_callable = com.apply_if_callable(key, self.obj) -> 1684 return self._getitem_axis(maybe_callable, axis=axis) 1685

~/Documents/Code Practice/pandas-dev/pandas/pandas/core/indexing.py in _getitem_axis(self, key, axis) 1798 return self._get_slice_axis(key, axis=axis) -> 1799 elif com.is_bool_indexer(key): 1800 return self._getbool_axis(key, axis=axis)

~/Documents/Code Practice/pandas-dev/pandas/pandas/core/common.py in is_bool_indexer(key) 133 if np.any(key.isna()): --> 134 raise ValueError(na_msg) 135 return True

ValueError: cannot mask with array containing NA / NaN values

During handling of the above exception, another exception occurred:

ValueError Traceback (most recent call last) in ----> 1 df.query('a > 2')

~/Documents/Code Practice/pandas-dev/pandas/pandas/core/frame.py in query(self, expr, inplace, **kwargs) 3230 # when res is multi-dimensional loc raises, but this is sometimes a 3231 # valid query -> 3232 new_data = self[res] 3233 3234 if inplace:

~/Documents/Code Practice/pandas-dev/pandas/pandas/core/frame.py in getitem(self, key) 2784 2785 # Do we have a (boolean) 1d indexer? -> 2786 if com.is_bool_indexer(key): 2787 return self._getitem_bool_array(key) 2788

~/Documents/Code Practice/pandas-dev/pandas/pandas/core/common.py in is_bool_indexer(key) 132 if is_extension_array_dtype(key.dtype): 133 if np.any(key.isna()): --> 134 raise ValueError(na_msg) 135 return True 136 elif isinstance(key, list):

ValueError: cannot mask with array containing NA / NaN values

df.query('a_float > 2')

Using query with strings works...

df.query('b == "rain"')

...but fails for boolean selection

df[df['b'] == 'rain'] ValueError: cannot mask with array containing NA / NaN values

strings also fail for inequalities

>>> df.query('b >= "rain"') # also df.query('b > "rain"')
ValueError: cannot mask with array containing NA / NaN values

Problem description

The query method behaves differently for nullable integers, strings, and floats. Here's my summary of how I think they work with the query method assuming there are missing values in the columns.

• nullable integers - fails
• strings - works with equality, fails with inequality
• float - works for all

I find it extremely difficult to use if the behavior for all of these types are different for query and boolean selection.

Expected Output

I think I would prefer to have both query and boolean selection working like they do with floats, where missing values evaluate as False in a condition. And even if there are missing values in the boolen mask itself, treat those as False. This would harmonize the behavior for all data types.

This would leave it up to the user to check for missing values. I believe SQL where clauses work in such a manner (missing values in conditions evaluate as False).

Output of pd.show_versions()

INSTALLED VERSIONS

commit : None
python : 3.8.1.final.0
python-bits : 64
OS : Darwin
OS-release : 19.2.0
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 0+untagged.1.gce8af21.dirty
numpy : 1.18.1
pytz : 2019.3
dateutil : 2.8.1
pip : 20.0.2
setuptools : 45.1.0.post20200127
Cython : 0.29.14
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.5.0
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 2.10.3
IPython : 7.11.1
pandas_datareader: None
bs4 : None
bottleneck : None
fastparquet : None
gcsfs : None
lxml.etree : 4.5.0
matplotlib : 3.1.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pytest : None
pyxlsb : None
s3fs : None
scipy : 1.3.1
sqlalchemy : 1.3.13
tables : None
tabulate : 0.8.3
xarray : None
xlrd : None
xlwt : None
xlsxwriter : None
numba : None