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PERF: Setting an item of incompatible dtype #61456

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muhannad125 opened this issue May 19, 2025 · 1 comment
Open
2 of 3 tasks

PERF: Setting an item of incompatible dtype #61456

muhannad125 opened this issue May 19, 2025 · 1 comment
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Indexing Related to indexing on series/frames, not to indexes themselves Needs Info Clarification about behavior needed to assess issue Performance Memory or execution speed performance

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@muhannad125
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Pandas version checks

  • I have checked that this issue has not already been reported.

  • I have confirmed this issue exists on the latest version of pandas.

  • I have confirmed this issue exists on the main branch of pandas.

Reproducible Example

df["feature"] = np.nan
for cluster in df["cluster"].unique():
df.loc[df["cluster"] == cluster, "feature"] = "string"

Installed Versions

INSTALLED VERSIONS

commit : 0691c5c
python : 3.10.12
python-bits : 64
OS : Linux
OS-release : 5.15.0-138-generic
Version : #148-Ubuntu SMP Fri Mar 14 19:05:48 UTC 2025
machine : x86_64
processor : x86_64
byteorder : little
LC_ALL : None
LANG : en_GB.utf8
LOCALE : en_GB.UTF-8

pandas : 2.2.3
numpy : 2.2.4
pytz : 2025.1
dateutil : 2.9.0.post0
pip : 25.0.1
Cython : None
sphinx : None
IPython : 8.34.0
adbc-driver-postgresql: None
adbc-driver-sqlite : None
bs4 : None
blosc : None
bottleneck : None
dataframe-api-compat : None
fastparquet : None
fsspec : 2023.6.0
html5lib : None
hypothesis : None
gcsfs : None
jinja2 : 3.1.6
lxml.etree : 4.9.4
matplotlib : 3.10.1
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
psycopg2 : None
pymysql : None
pyarrow : 19.0.1
pyreadstat : None
pytest : None
python-calamine : None
pyxlsb : None
s3fs : 2023.6.0
scipy : 1.15.2
sqlalchemy : None
tables : None
tabulate : None
xarray : 2025.1.2
xlrd : None
xlsxwriter : None
zstandard : None
tzdata : 2025.1
qtpy : None
pyqt5 : None

Prior Performance

Setup

Dataset: df with 148,858 rows

Task: Assign "string" to a new column "feature" based on unique values in the "cluster" column.

Environment: Running on LSF

Test 1: Initialize with np.nan

import numpy as np

df["feature"] = np.nan
for cluster in df["cluster"].unique():
df.loc[df["cluster"] == cluster, "feature"] = "string"

Runtime: ~52.5 seconds

Warning:

FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas. 
Value 'string' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.

Test 2: Initialize with "None"

df["feature"] = "None"
for cluster in df["cluster"].unique():
df.loc[df["cluster"] == cluster, "feature"] = "string"

Runtime: ~1 minute 35 seconds

No warnings

Observation: Slower performance despite avoiding the dtype mismatch warning.
@muhannad125 muhannad125 added Performance Memory or execution speed performance Needs Triage Issue that has not been reviewed by a pandas team member labels May 19, 2025
@rhshadrach
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@muhannad125 - please provide a reproducible example. Setup an example df with synthetic data.

You would might be interested in using map if performance is a concern.

@rhshadrach rhshadrach added Needs Info Clarification about behavior needed to assess issue Indexing Related to indexing on series/frames, not to indexes themselves and removed Needs Triage Issue that has not been reviewed by a pandas team member labels May 19, 2025
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@rhshadrach @muhannad125