BUG: to_numeric does not validate the errors keyword

[sorenwacker]

From discussion below: pd.to_numeric does not validate the value passed to the errors keyword, so any random value is interpreted as errors='coerce'.

---

Original report:

Code Sample, a copy-pastable example if possible

df = pd.DataFrame({'Strings': ['fire', 'hose'], 'Numbers': ['3838.2', '99']})
print(df.apply(pd.to_numeric, args={'errors': 'ignore'}).to_string())

Problem description

The code above should return:

Strings Numbers
0 'fire' 3838.2
1 'hose' 99.0

Instead it returns:

Strings Numbers
0 NaN 3838.2
1 NaN 99.0

Output of pd.show_versions()

INSTALLED VERSIONS

commit: None
python: 3.6.6.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-48-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

pandas: 0.24.2
pytest: 4.4.1
pip: 19.1
setuptools: 41.0.0
Cython: 0.29.7
numpy: 1.16.3
scipy: 1.2.1
pyarrow: 0.10.0
xarray: 0.12.1
IPython: 7.5.0
sphinx: None
patsy: 0.5.1
dateutil: 2.8.0
pytz: 2019.1
blosc: None
bottleneck: 1.2.1
tables: 3.4.4
numexpr: 2.6.8
feather: 0.4.0
matplotlib: 3.0.3
openpyxl: None
xlrd: 1.2.0
xlwt: 1.3.0
xlsxwriter: 1.1.7
lxml.etree: 4.3.3
bs4: 4.6.3
html5lib: 0.9999999
sqlalchemy: 1.3.3
pymysql: None
psycopg2: 2.7.5 (dt dec pq3 ext lo64)
jinja2: 2.10.1
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: None
gcsfs: None

[gfyoung]

How odd! Investigation and PR are welcome!

[jorisvandenbossche]

The odd thing is that it actually works correctly on a single column:

In [8]: df = pd.DataFrame({'Strings': ['fire', 'hose'], 'Numbers': ['3838.2', '99']}) 

In [9]: df.apply(pd.to_numeric, args={'errors': 'ignore'}) 
Out[9]: 
   Strings  Numbers
0      NaN   3838.2
1      NaN     99.0

In [10]: pd.to_numeric(df['Strings'], errors='ignore') 
Out[10]: 
0    fire
1    hose
Name: Strings, dtype: object

In [11]: pd.to_numeric(df.iloc[0, :], errors='ignore')
Out[11]: 
Strings      fire
Numbers    3838.2
Name: 0, dtype: object

[jorisvandenbossche]

Ah, but the args keyword of apply expects (a tuple of) positional arguments, so the dict is not seen as keyword argument for to_numeric, but just as a dict passed to the second argument of to_numeric.

With proper passing through, this works:

In [24]: df.apply(pd.to_numeric, errors='ignore')  # or .., **{'errors': 'ignore'})
Out[24]: 
  Strings  Numbers
0    fire   3838.2
1    hose     99.0

[jorisvandenbossche]

The second argument of to_numeric is errors, so this was actually happening:

In [26]: pd.to_numeric(df['Strings'], errors={'errors': 'ignore'})                                                                                            
Out[26]: 
0   NaN
1   NaN
Name: Strings, dtype: float64

So this is actually a bug, as to_numeric should validate the value passed to errors keyword (currently you have something like coerce_numeric = errors not in ('ignore', 'raise') in the code, which will incorrectly give True in this case).

@soerendip interested in doing a PR to fix that validation?

[sorenwacker]

I am, but I am also really, really, really busy right now. So, I can probably work on this in approx 3 weeks.

[gfyoung]

In light of #26394 (comment), reopening this issue.