Performance improvements for python-polars `df.select` operations

I'm currently trying to migrate some code to polars but noticed some performance differences in the process.

import os, platform, timeit, numpy as np, pandas as pd, polars as pl

data = np.random.rand(100000, 1)
df_pandas = pd.DataFrame(data)
df_polars = pl.DataFrame(data)

def timer(expr):
    return round(min(timeit.repeat(expr, repeat=5, number=5)), 8)

print("---- info ----")
print(f"platform={platform.platform()}; processor={platform.processor()}; CPUs={os.cpu_count()}")
print(f"python={platform.python_version()}; numpy={np.__version__}; pandas={pd.__version__}; polars={pl.__version__}")

print("---- pow(2) ----")
print("pandas:", timer(lambda: df_pandas.pow(2)))
print("polars:", timer(lambda: df_polars.select(pl.all().pow(2))))

print("---- sum ----")
print("pandas:", timer(lambda: df_pandas.sum()))
print("polars:", timer(lambda: df_polars.sum()))

The output of this snippet is

---- info ----
platform=macOS-11.6.5-x86_64-i386-64bit; processor=i386; CPUs=4
python=3.8.13; numpy=1.22.4; pandas=1.4.2; polars=0.13.47
---- pow(2) ----
pandas: 0.00147684
polars: 0.01482804
---- sum ----
pandas: 0.00300668
polars: 0.00027682

These results imply that polars is much slower than pandas for operations that include a Python select, but are faster for ones that are performed directly on the dataframe.

In reality, my dataframe is much different bigger (rows > 1,000,000, cols > 100,000), where the performance difference is much more significant.

Any suggestions for what might be going on and if there is a faster way to achieve the same (or better) performance in polars?