Closed
Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
import pandas as pd
import gc
import os
import psutil
# create example data
items = [f'item_{i}' for i in range(10_000)]
data = {}
for i, col in enumerate(items[:1000]):
data[col] = [1]*len(items)
df = pd.DataFrame(index=items, data=data)
gc.collect()
process = psutil.Process(os.getpid())
rss = process.memory_info().rss/1024/1024
for item in df.columns[:10]:
df[item][item] = -10
gc.collect()
new_rss = process.memory_info().rss/1024/1024
print('{:.2f} MiB'.format(new_rss-rss))Issue Description
In pandas version 1.4.2 the memory usage increases at each iteration of the last for loop, outputs:
76.55 MiB
152.99 MiB
229.16 MiB
305.08 MiB
381.00 MiB
456.92 MiB
532.58 MiB
608.24 MiB
683.96 MiB
759.51 MiB
Expected Behavior
In pandas version 1.3.5 the memory usage remains constant, output:
0.00 MiB
0.00 MiB
0.00 MiB
0.00 MiB
0.00 MiB
0.00 MiB
0.00 MiB
0.00 MiB
0.00 MiB
0.00 MiB
Installed Versions
INSTALLED VERSIONS
------------------
commit : 4bfe3d07b4858144c219b9346329027024102ab6
python : 3.8.12.final.0
python-bits : 64
OS : Linux
OS-release : 5.10.0-13-amd64
Version : #1 SMP Debian 5.10.106-1 (2022-03-17)
machine : x86_64
processor :
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.2
numpy : 1.21.6
pytz : 2022.1
dateutil : 2.8.2
pip : 22.1.1
setuptools : 56.0.0
Cython : 0.29.30
pytest : None
hypothesis : None
sphinx : 4.5.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : 1.0.2
psycopg2 : None
jinja2 : 3.1.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : 2.1.1
matplotlib : 3.5.1
numba : 0.55.1
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.0
snappy :
sqlalchemy : 1.4.36
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
zstandard : None