PERF: DataFrame.update of pandas version is slower than the older version

[warm200]

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.

Reproducible Example

import pandas as pd
from time import time

data = {}
for i in range(0, 10000):
    data[i] = [100] * 100

 

new_data = {}
for i in range(2000):
    new_data[i] = [1000] * 100

df = pd.DataFrame(data)
new_df = pd.DataFrame(new_data)

 
start = time()
df.update(new_df)
end = time()
print("DataFrame took: ", end - start)

Installed Versions

for latest version:

pandas.show_versions()

INSTALLED VERSIONS

commit : 4bfe3d0
python : 3.9.2.final.0
python-bits : 64
OS : Darwin
OS-release : 21.4.0
Version : Darwin Kernel Version 21.4.0: Fri Mar 18 00:45:05 PDT 2022; root:xnu-8020.101.4~15/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8

pandas : 1.4.2
numpy : 1.22.4
pytz : 2022.1
dateutil : 2.8.2
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
brotli : None
fastparquet : None
fsspec : None
gcsfs : None
markupsafe : None
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 : None
xarray : None
xlrd : None
xlwt : None
zstandard : None

for older version 1.1.5

INSTALLED VERSIONS ------------------ commit : b5958ee python : 3.9.2.final.0 python-bits : 64 OS : Darwin OS-release : 21.4.0 Version : Darwin Kernel Version 21.4.0: Fri Mar 18 00:45:05 PDT 2022; root:xnu-8020.101.4~15/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.1.5
numpy : 1.22.4
pytz : 2022.1
dateutil : 2.8.2
pip : 20.2.3
setuptools : 49.2.1
Cython : None
pytest : None
hypothesis : None
sphinx : None
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : None
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : None
fsspec : None
fastparquet : None
gcsfs : None
matplotlib : None
numexpr : None
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pytables : None
pyxlsb : None
s3fs : None
scipy : None
sqlalchemy : None
tables : None
tabulate : None
xarray : None
xlrd : None
xlwt : None
numba : None

Prior Performance

Pandas 1.1.5 result

here is the result from same code example above but run with pandas v1.1.5
1.031 is in seconds.

DataFrame update took:  0.7426297664642334

pandas 1.4.2 result

Here is the result from save code example above but run with pandas v.1.4.2

DataFrame update took:  2.155642032623291

you can see the latest version is almost double in running time than older version.

[phofl]

Hi, thanks for your report. Update uses setitem under the hood, hence this is a duplicate of #46267

[warm200]

@phofl thanks for redirecting link, I have read everything through and I think this line is cuprit https://github.com/pandas-dev/pandas/blob/main/pandas/core/frame.py#L8003
when I changed it to
self.loc[:, col] = expressions.where(mask, this, that)
the run time is a lot faster. do you think i should report this as a improvement or a bug?
or this is already in the roadmap?