>>> merged.with_columns(
...     (
...         pl.col("before","after").str.extract(r"(\d+(?:\.\d+)?)").cast(pl.Float64)
...         *
...         pl.col("before","after").str.extract(r"(B|KB|MB|GB)$").replace(units).cast(pl.Int64)...     )
... ).with_columns(delta=(pl.col("after")/pl.col("before"))-1)
shape: (15, 4)
┌──────────────────────────┬────────────┬────────────┬───────────┐
│ arch                     ┆ before     ┆ after      ┆ delta     │
│ ---                      ┆ ---        ┆ ---        ┆ ---       │
│ str                      ┆ f64        ┆ f64        ┆ f64       │
╞══════════════════════════╪════════════╪════════════╪═══════════╡
│ wheels-linux-aarch64     ┆ 6.5746e6   ┆ 4404019.2  ┆ -0.330144 │
│ wheels-linux-armv7       ┆ 5.0541e6   ┆ 3.9636e6   ┆ -0.215768 │
│ wheels-linux-ppc64le     ┆ 6920601.6  ┆ 4.8549e6   ┆ -0.298485 │
│ wheels-linux-s390x       ┆ 6.291456e6 ┆ 5.767168e6 ┆ -0.083333 │
│ wheels-linux-x86         ┆ 4.5298e6   ┆ 3198156.8  ┆ -0.293981 │
│ wheels-linux-x86_64      ┆ 4.7815e6   ┆ 3460300.8  ┆ -0.276316 │
│ wheels-macos-aarch64     ┆ 1.8770e6   ┆ 1.3946e6   ┆ -0.256983 │
│ wheels-macos-x86_64      ┆ 1.8665e6   ┆ 1.4470e6   ┆ -0.224719 │
│ wheels-musllinux-aarch64 ┆ 7.9797e6   ┆ 5.8301e6   ┆ -0.269382 │
│ wheels-musllinux-armv7   ┆ 7.1198e6   ┆ 6.0503e6   ┆ -0.150221 │
│ wheels-musllinux-x86     ┆ 7.4134e6   ┆ 5.6518e6   ┆ -0.237624 │
│ wheels-musllinux-x86_64  ┆ 7.7385e6   ┆ 5976883.2  ┆ -0.227642 │
│ wheels-sdist             ┆ 44339.2    ┆ 44441.6    ┆ 0.002309  │
│ wheels-windows-x64       ┆ 3.1562e6   ┆ 2.0552e6   ┆ -0.348837 │
│ wheels-windows-x86       ┆ 3.0304e6   ┆ 2044723.2  ┆ -0.32526  │
└──────────────────────────┴────────────┴────────────┴───────────┘
>>> normalised = merged.with_columns(
...     (
...         pl.col("before","after").str.extract(r"(\d+(?:\.\d+)?)").cast(pl.Float64)
...         *
...         pl.col("before","after").str.extract(r"(B|KB|MB|GB)$").replace(units).cast(pl.Int64)
...     )
... ).with_columns(delta=(pl.col("after")/pl.col("before"))-1)
>>> normalised.get_column("delta").mean()
-0.23575903794617664
>>> normalised.get_column("delta").min()
-0.34883720930232553
>>> normalised.get_column("delta").max()
0.002309468822170979