BUG: df.fillna(dict, inplace=True) triggers FutureWarning in 1.5.0rc0 when DataFrame is empty #48480

marekro · 2022-09-09T10:29:53Z

Pandas version checks

I have checked that this issue has not already been reported.
I have confirmed this bug exists on the latest version of pandas.
I have confirmed this bug exists on the main branch of pandas.

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 : 224458e 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

The text was updated successfully, but these errors were encountered:

phofl · 2022-09-09T11:57:26Z

Hi, thanks for your report. This is fixed on main and also on 1.5.x that will be release soonish, see #48254
This might need a test though

RaphSku · 2022-09-13T12:07:18Z

take

marekro · 2022-09-20T07:26:59Z

Did this fix make it into 1.5.0? It seems to me that recently released 1.5.0 still shows this FutureWarning.

phofl · 2022-09-20T14:00:16Z

I can not see the warning on 1.5.0. Can you provide your environment information?

marekro · 2022-09-28T08:47:56Z

It is a mystery... when using pandas-1.5.0 on MacOS and Linux, plain, I do not see the warning either. In context of my greater program, the FutureWarning keeps appearing - I need to debug further. Maybe some issue with venv, will comment here again with new findings.

marekro · 2022-09-28T13:27:13Z

For completeness, below are the installed versions, printed from within the same run that also emits the FutureWarnings. However, I noticed that because of a mistake, I have been importing pandas twice: import pandas; ...; import pandas as pd. Maybe that causes issues? I have fixed this, running now regressions, and will follow up.

INSTALLED VERSIONS

commit : 87cfe4e
python : 3.8.11.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.1.2.el7.x86_64
Version : #1 SMP Mon Sep 30 14:19:46 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.0
numpy : 1.23.3
pytz : 2022.2.1
dateutil : 2.8.2
setuptools : 56.0.0
pip : 22.2.2
Cython : 0.29.23
pytest : 7.1.3
hypothesis : None
sphinx : 1.8.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
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 : 1.4.41
tables : None
tabulate : 0.8.10
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None

marekro · 2022-09-29T06:57:34Z

This is odd; I am running the corrected code, and I am still getting:

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

The code emitting the warning is this statement:

result_df.fillna({ 'key_vr_folder_id': '', 'key_relationship': '', 'key_parenttasks': '', 'key_subtasks': '' }, inplace=True)

Again, below is the corresponding version list from pandas. I cannot reproduce the FutureWarning with pandas-1.5.0 outside of the program. The warning seems to come out of _setitem_single_column, but the conditionals in there do not ring a bell with me. Any ideas?

INSTALLED VERSIONS

commit : 87cfe4e
python : 3.8.11.final.0
python-bits : 64
OS : Linux
OS-release : 3.10.0-1062.1.2.el7.x86_64
Version : #1 SMP Mon Sep 30 14:19:46 UTC 2019
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : en_US.UTF-8
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.5.0
numpy : 1.23.3
pytz : 2022.2.1
dateutil : 2.8.2
setuptools : 56.0.0
pip : 22.2.2
Cython : 0.29.23
pytest : 7.1.3
hypothesis : None
sphinx : 1.8.6
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : None
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 : 1.4.41
tables : None
tabulate : 0.8.10
xarray : None
xlrd : None
xlwt : None
zstandard : None
tzdata : None

phofl · 2022-09-29T08:27:41Z

As long as there isn't anything we can debug, there is not much we can do

marekro · 2022-09-29T09:16:23Z

Found the corner case - a dataframe with no rows:

df = pd.DataFrame(data=[], columns=['A', 'B'])
df.fillna({'A': '', 'B': ''}, inplace=True)
<input>:1: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`
<input>:1: FutureWarning: In a future version, `df.iloc[:, i] = newvals` will attempt to set the values inplace instead of always setting a new array. To retain the old behavior, use either `df[df.columns[i]] = newvals` or, if columns are non-unique, `df.isetitem(i, newvals)`

Seems that this situation needs to be handled specifically. Sorry for the noise in the previous comments.

olsgaard · 2022-10-12T13:13:12Z

I'm having a similar problem where I apply a function to a grouped dataframe that uses df.loc[i:ii, "colname"] and for some of the groups, I get the warning.

Is there a way to break into pdb when the warning is elicited? I'm not sure how to go about debugging this.

abedhammoud · 2022-10-31T20:24:40Z

I am having the same warning issued from the following statement:

        df.fillna(value={
            'ITEM': '<NA>',

            'TTYPE': '-1',
            'QTY': 0,
            'P': -1,

            'COUNTRY': '<NA>',
            'CITY': '<NA>'

        }, inplace=True)

When I remove the inplace and instead use:

df = df.fillna(...)

The warning disappears.

I am running 1.5.1 with python 3.10.6 on windows.

phofl · 2022-11-01T17:51:29Z

Can you provide something reproducible?

abedhammoud · 2022-11-02T09:11:23Z

Even though I am getting the warning in my program every time, I cannot write something that reproduces the warning. My code is doing something like the following (where df has many more columns and rows):

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=}')

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=}')

I will keep trying and report back.

caoyu-yiyue · 2022-11-30T14:11:20Z

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:

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

Maybe some problem associated with string dtype?

And I also investigate other circumstances.

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

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 · 2022-11-30T14:26:43Z

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=}')

MarcoGorelli · 2023-02-22T10:47:28Z

Hey @marekro

Thanks for having reported this - there's now a release candidate for pandas 2.0.0, which you can install with

mamba install -c conda-forge/label/pandas_rc pandas==2.0.0rc0

or

python -m pip install --upgrade --pre pandas==2.0.0rc0

If you try it out and report bugs, then we can fix them before the final 2.0.0 release

abedhammoud · 2023-02-22T13:17:57Z

@MarcoGorelli the above warning is not happening with 2.0.0rc0. However, I am seeing other issues that I will report separately.
Thank you.

abedhammoud · 2023-02-24T17:17:00Z

I confirm that I am not seeing the above warning after many tests. And, once I took care of some of the warnings related to some depreciated code, all worked perfectly with 2.0.0rc0. Thank you.

MarcoGorelli · 2023-02-24T17:25:10Z

thanks for checking!

marekro added Bug Needs Triage Issue that has not been reviewed by a pandas team member labels Sep 9, 2022

phofl added Missing-data np.nan, pd.NaT, pd.NA, dropna, isnull, interpolate good first issue Needs Tests Unit test(s) needed to prevent regressions Copy / view semantics and removed Bug Needs Triage Issue that has not been reviewed by a pandas team member labels Sep 9, 2022

github-actions bot assigned RaphSku Sep 13, 2022

RaphSku mentioned this issue Sep 15, 2022

TST: Testing that no warnings are emitted and that inplace fillna produces the correct result (GH48480) #48562

Merged

3 tasks

phofl closed this as completed in #48562 Sep 15, 2022

phofl reopened this Sep 29, 2022

phofl mentioned this issue Sep 29, 2022

REGR: Avoid unnecessary warning when setting empty dataframe #48853

Merged

5 tasks

phofl changed the title ~~BUG: df.fillna(dict, inplace=True) triggers FutureWarning in 1.5.0rc0~~ BUG: df.fillna(dict, inplace=True) triggers FutureWarning in 1.5.0rc0 when DataFrame is empty Sep 29, 2022

phofl added Indexing Related to indexing on series/frames, not to indexes themselves Regression Functionality that used to work in a prior pandas version and removed good first issue Needs Tests Unit test(s) needed to prevent regressions labels Sep 29, 2022

phofl added this to the 1.5.1 milestone Sep 29, 2022

mroeschke closed this as completed in #48853 Sep 29, 2022

kvarak mentioned this issue Oct 8, 2022

Investigate:future warning 2 verifa/metrics#117

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BUG: df.fillna(dict, inplace=True) triggers FutureWarning in 1.5.0rc0 when DataFrame is empty #48480

BUG: df.fillna(dict, inplace=True) triggers FutureWarning in 1.5.0rc0 when DataFrame is empty #48480

marekro commented Sep 9, 2022

phofl commented Sep 9, 2022

RaphSku commented Sep 13, 2022

marekro commented Sep 20, 2022

phofl commented Sep 20, 2022

marekro commented Sep 28, 2022

marekro commented Sep 28, 2022

marekro commented Sep 29, 2022

phofl commented Sep 29, 2022

marekro commented Sep 29, 2022

olsgaard commented Oct 12, 2022 •

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abedhammoud commented Oct 31, 2022

phofl commented Nov 1, 2022

abedhammoud commented Nov 2, 2022

caoyu-yiyue commented Nov 30, 2022 •

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abedhammoud commented Nov 30, 2022

MarcoGorelli commented Feb 22, 2023

abedhammoud commented Feb 22, 2023

abedhammoud commented Feb 24, 2023

MarcoGorelli commented Feb 24, 2023

BUG: df.fillna(dict, inplace=True) triggers FutureWarning in 1.5.0rc0 when DataFrame is empty #48480

BUG: df.fillna(dict, inplace=True) triggers FutureWarning in 1.5.0rc0 when DataFrame is empty #48480

Comments

marekro commented Sep 9, 2022

Pandas version checks

Reproducible Example

Issue Description

Expected Behavior

Installed Versions

phofl commented Sep 9, 2022

RaphSku commented Sep 13, 2022

marekro commented Sep 20, 2022

phofl commented Sep 20, 2022

marekro commented Sep 28, 2022

marekro commented Sep 28, 2022

INSTALLED VERSIONS

marekro commented Sep 29, 2022

INSTALLED VERSIONS

phofl commented Sep 29, 2022

marekro commented Sep 29, 2022

olsgaard commented Oct 12, 2022 • edited Loading

abedhammoud commented Oct 31, 2022

phofl commented Nov 1, 2022

abedhammoud commented Nov 2, 2022

caoyu-yiyue commented Nov 30, 2022 • edited Loading

abedhammoud commented Nov 30, 2022

MarcoGorelli commented Feb 22, 2023

abedhammoud commented Feb 22, 2023

abedhammoud commented Feb 24, 2023

MarcoGorelli commented Feb 24, 2023

olsgaard commented Oct 12, 2022 •

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caoyu-yiyue commented Nov 30, 2022 •

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