BUG: df.__setitem__ can be 10x slower than pd.concat(..., axis=1) #37954
Description
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I have checked that this issue has not already been reported.
- I think so, it's a bit hard to search for though. Nothing under concat and setindex
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I have confirmed this bug exists on the latest version of pandas.
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(optional) I have confirmed this bug exists on the master branch of pandas.
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Code Sample, a copy-pastable example
import pandas as pd, numpy as np from string import ascii_lowercase def setitem(x, x_cols, df): new = pd.DataFrame(index=df.index) new[x_cols] = x new[df.columns] = df return new def concat(x, x_cols, df): return pd.concat( [ pd.DataFrame(x, columns=x_cols, index=df.index), df, ], axis=1, ) x = np.ones((1000, 10)) x_col = list(ascii_lowercase[:10]) df = pd.DataFrame( { "str": np.random.choice(np.array(list(ascii_lowercase)), size=1000), "int": np.arange(1000, dtype=int), } ) %timeit setitem(x, x_col, df) # 3.78 ms ± 193 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) %timeit concat(x, x_col, df) # 306 µs ± 9.29 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)Problem description
This seems unintuitive from a performance perspective. I would assume that these would be close to equivalent. The setitem implementation might even be expected to have better performance due to less allocation.
While this is a stripped down example, the use case is building a dataframe to return from a function. Making a dataframe, then adding columns seemed like the natural idiom here.
Output of pd.show_versions()
INSTALLED VERSIONS ------------------ commit : 67a3d4241ab84419856b84fc3ebc9abcbe66c6b3 python : 3.8.5.final.0 python-bits : 64 OS : Darwin OS-release : 19.6.0 Version : Darwin Kernel Version 19.6.0: Thu Oct 29 22:56:45 PDT 2020; root:xnu-6153.141.2.2~1/RELEASE_X86_64 machine : x86_64 processor : i386 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8 pandas : 1.1.4 numpy : 1.19.4 pytz : 2020.1 dateutil : 2.8.1 pip : 20.2.4 setuptools : 50.3.2 Cython : 0.29.21 pytest : 6.1.2 hypothesis : None sphinx : 3.2.1 blosc : None feather : None xlsxwriter : None lxml.etree : None html5lib : None pymysql : None psycopg2 : None jinja2 : 2.11.2 IPython : 7.19.0 pandas_datareader: None bs4 : None bottleneck : None fsspec : 0.8.4 fastparquet : None gcsfs : None matplotlib : 3.3.3 numexpr : 2.7.1 odfpy : None openpyxl : None pandas_gbq : None pyarrow : 2.0.0 pytables : None pyxlsb : None s3fs : None scipy : 1.5.4 sqlalchemy : 1.3.18 tables : 3.6.1 tabulate : 0.8.7 xarray : 0.16.1 xlrd : 1.2.0 xlwt : None numba : 0.51.2