BUG: df.fillna(dict, inplace=True) triggers FutureWarning in 1.5.0rc0 when DataFrame is empty

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Reproducible Example

import pandas as pd
df = pd.DataFrame(data=[[None, None], [None,None]], columns=['A', 'B'])
df.fillna({'A': 1, 'B': 2}, inplace=True)

Issue Description

With the most recent development version 1.5.0rc0, I see that the code example raises the following warning:

FutureWarning: In a future version, df.iloc[:, i] = newvalswill attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use eitherdf[df.columns[i]] = newvalsor, if columns are non-unique,df.isetitem(i, newvals)``

The df. fillna() API should not use code that is ambiguous or deprecated.

Expected Behavior

df.fillna() should do its job, silently.

Installed Versions

INSTALLED VERSIONS ------------------ commit : 224458ee25d92ccdf289d1ae2741d178df4f323e python : 3.8.13.final.0 python-bits : 64 OS : Darwin OS-release : 21.6.0 Version : Darwin Kernel Version 21.6.0: Wed Aug 10 14:25:27 PDT 2022; root:xnu-8020.141.5~2/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : None.UTF-8 pandas : 1.5.0rc0 numpy : 1.23.2 pytz : 2021.1 dateutil : 2.8.2 setuptools : 56.0.0 pip : 22.2.2 Cython : None pytest : 7.1.2 hypothesis : None sphinx : 1.8.6 blosc : None feather : None xlsxwriter : None lxml.etree : 4.9.1 html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.3 IPython : 7.21.0 pandas_datareader: None bs4 : None bottleneck : None brotli : None fastparquet : None fsspec : None gcsfs : None matplotlib : 3.4.1 numba : None numexpr : None odfpy : None openpyxl : 3.0.10 pandas_gbq : None pyarrow : None pyreadstat : None pyxlsb : None s3fs : None scipy : None snappy : None sqlalchemy : 1.4.40 tables : None tabulate : 0.8.10 xarray : None xlrd : None xlwt : None zstandard : None tzdata : None

[caoyu-yiyue]

I’m using pandas 1.5.2 on MacOS 13.0.1, with Python@3.11.0 from Homebrew, and I can consistently reproduce the bug with the code below:

import pandas as pd

df = pd.DataFrame(data={'col1': ['a', pd.NA, 'c']}, index=range(3))
df = df.convert_dtypes()
df.fillna({'col1': '<NA>'}, inplace=True)

Three conditions together for the bug:

1. 'col1' is object dtype at first, not int or float.
2. Call the convert_dtypes() method.
3. Set inplace=True in fillna().

Maybe some problem associated with string dtype?

---

And I also investigate other circumstances.

1. Using np.nan instead of pd.NA the problem will still be there.

2. Create a Series using the same list, and using fillna() method of Series will not trigger the bug. Codes below:

   # No bug for Series.
   s = pd.Series(['a', pd.NA, 'b'])
   s = s.convert_dtypes()
   s.fillna('<NA>', inplace=True)
   

Maybe some help.

[abedhammoud]

I confirm that also in my case (see above) if I use df.convert_dtypes() I can reproduce the warning. However, in my original code (which I tried to summarize), I am not using convert_dtypes() explicitly, and I am getting the warning.

import numpy as np
import pandas as pd

df = pd.DataFrame(
    {
        'ITEM': ['ITM1', 'ITM2', pd.NA, np.nan],
        'REF': ['R1', 'R2', 'R3', pd.NA],
        'TYPE': -1,
        'QTY': 0,
        'P': [0, 1, 'Y', 'X'],
    }
)

print(f'before:\n{df=}')

# This will reproduce the warning
df = df.convert_dtypes()

df['P'] = pd.to_numeric(df['P'], errors='coerce')

print(f'after to_numeric:\n{df=}')

df.fillna(value={
    'ITEM': '<NA>',
    'IREF': '<NA>',
    'P': -1}, inplace=True)

print(f'tidy:\n{df=}')