Description
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
from decimal import Decimal
import pandas as pd
df = pd.DataFrame({"a": [1, 2, None, 4], "b": [1.5, None, 2.5, 3.5]})
selected_df = df.select_dtypes(include=["number", Decimal])
selected_df.fillna(0, inplace=True)
df.to_markdown()Issue Description
On Pandas 1.3.4 and previously, mutating the DataFrame returned by select_dtypes didn't change the original DataFrame. However, on 1.4.3, it appears mutating the selected DataFrame changes the original one. I suspect this is the PR that has caused the change in behavior: #42611
On 1.4.3, the original DataFrame is mutated:
| a | b | |
|---|---|---|
| 0 | 1 | 1.5 |
| 1 | 2 | 0 |
| 2 | 0 | 2.5 |
| 3 | 4 | 3.5 |
Expected Behavior
on 1.3.4, the original DataFrame is unchanged:
| a | b | |
|---|---|---|
| 0 | 1 | 1.5 |
| 1 | 2 | nan |
| 2 | nan | 2.5 |
| 3 | 4 | 3.5 |
Installed Versions
pandas : 1.4.3
numpy : 1.23.2
pytz : 2022.2.1
dateutil : 2.8.2
setuptools : 63.2.0
pip : 22.2.2
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : 4.9.1
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.1.2
IPython : 8.4.0
pandas_datareader: None
bs4 : 4.11.1
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : 2.1.1
matplotlib : None
numba : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : None
snappy : None
sqlalchemy : None
tables : None
tabulate : 0.8.10
xarray : None
xlrd : None
xlwt : None
zstandard : None