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pandas.isnull() has poor behavior for lists #20675

bfollinprm opened this Issue Apr 13, 2018 · 4 comments


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bfollinprm commented Apr 13, 2018

Code Sample, a copy-pastable example if possible

import pandas as pd
pd.isnull([np.NaN, 'world'])

# returns:
# array([False, False], dtype=bool)

Problem description

The output of pd.isnull/pd.isna on lists depends on the inferred dtype of the numpy conversion.
In cases where the array is inferred to be of a string type, numpy converts np.NaN to the string "nan", which pd.isnull() no longer recognizes as a null value. The following solve the underlying problem, which is numpy auto-inferring a string dtype for mixed lists containing strings and float('nan') float values:

  • explicitly convert to object arrays instead of string arrays, as is done in pd.Series construction
  • convert to a pd.Series object instead of the numpy object (leverages the above)
  • applying pd.isna() in a list comprehension for list objects, e.g.
def isna(a): #a is a list
    np.array([pd.isna(el) for el in a])

Expected Output

array([True, False], dtype=bool)



if it is undesirable to support lists with mixed float/string types with pd.isnull()

Output of pd.show_versions()


commit: None
python-bits: 64
OS: Linux
OS-release: 4.4.0-1048-aws
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LOCALE: None.None

pandas: 0.22.0
pytest: 3.2.2
pip: 9.0.1
setuptools: 27.2.0
Cython: None
numpy: 1.13.3
scipy: 0.19.1
pyarrow: None
xarray: None
IPython: 5.3.0
sphinx: 1.5.4
patsy: None
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: None
numexpr: None
feather: None
matplotlib: 2.0.2
openpyxl: None
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.9
pymysql: None
psycopg2: None
jinja2: 2.9.6
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None


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jorisvandenbossche commented Apr 13, 2018

As you mention, this is due to np.asarray(...) coercing everything to a string once there is a string in it. I would say this is a bug (or design issue) in numpy, but since it is a well known one, we should workaround it, as we do in other places.

From a quick look, this might be used instead of asarray:

In [58]: pd.core.dtypes.cast.maybe_convert_platform([np.nan, 'world'])
Out[58]: array([nan, 'world'], dtype=object)

PR welcome!

@jorisvandenbossche jorisvandenbossche added this to the Next Major Release milestone Apr 13, 2018


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bfollinprm commented Apr 13, 2018

I'll work through this hopefully early next week.


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bfollinprm commented Apr 18, 2018

I want to update so I'm not thought of as a raise-and-dump kind of person: awaiting approval to contribute from work, but have a fix ready if/when that happens.


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jorisvandenbossche commented Apr 19, 2018

OK, hopefully that will be no problem, and looking forward to the PR

@jreback jreback modified the milestones: Next Major Release, 0.23.0 May 7, 2018

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