BUG: Different colors for NA in `df.style.background_gradient()`

[janosh]

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Reproducible Example

import itertools

import pandas as pd


df_rand = pd.DataFrame(index=range(5), columns=range(5), dtype=float)

for idx1, idx2 in itertools.combinations_with_replacement(df_rand.index, 2):
    df_rand.loc[idx1, idx2] = np.random.randint(0, 100)

df_rand.style.format("{:.0f}", na_rep="").background_gradient()

Issue Description

Code above outputs HTML for a styled df with 1st column showing different colors (white) for nan values than the others (black).

Expected Behavior

I'd like all nan cells to be black.

Installed Versions

INSTALLED VERSIONS

commit : e8093ba
python : 3.10.5.final.0
python-bits : 64
OS : Darwin
OS-release : 21.5.0
Version : Darwin Kernel Version 21.5.0: Tue Apr 26 21:08:37 PDT 2022; root:xnu-8020.121.3~4/RELEASE_ARM64_T6000
machine : arm64
processor : arm
byteorder : little
LC_ALL : None
LANG : None
LOCALE : None.UTF-8

pandas : 1.4.3
numpy : 1.23.1
pytz : 2021.3
dateutil : 2.8.2
setuptools : 59.5.0
pip : 22.1
Cython : 0.29.26
pytest : 7.1.2
hypothesis : 6.42.0
sphinx : 4.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.3
IPython : 7.30.1
pandas_datareader: None
bs4 : 4.10.0
bottleneck : None
brotli : 1.0.9
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : 2.0.1
matplotlib : 3.5.2
numba : None
numexpr : None
odfpy : None
openpyxl : 3.0.10
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.7.3
snappy : None
sqlalchemy : 1.4.31
tables : None
tabulate : 0.8.9
xarray : None
xlrd : 2.0.1
xlwt : 1.3.0
zstandard : None

[attack68]

The background gradient is being applied columnwise, since axis defaults to 0 in your case. I assume that axis=None does not have this effect.

I can't off the top of my head think of a fix but the problem is probably related to there only being a single valid entry in the first column whilst other columns have at least a min and max.

Workarounds may involve manually using the function arguments to define the mins and maxs where only a single valid element is in the column. Please explore and possibly post a little more info. I should have more time to look at this shortly.

[janosh]

Cool! axis=None indeed fixes the problem.

df_rand.style.format("{:.0f}", na_rep="").background_gradient(axis=None)