[Python] Interpolate time series data from one df to time axis of another df in Python polars

I have time series data on different time axes in different dataframes. I need to interpolate data from one df to onto the time axis of another df, df_ref. Ex:

import polars as pl

# DataFrame with the reference time axis:
df_ref = pl.DataFrame({"dt": ["2022-12-14T14:00:01.000", "2022-12-14T14:00:02.000",
"2022-12-14T14:00:03.000", "2022-12-14T14:00:04.000",
"2022-12-14T14:00:05.000", "2022-12-14T14:00:06.000"]})
df_ref = df_ref.with_columns(pl.col("dt").str.to_datetime())

# DataFrame with a different frequency time axis, to be interpolated onto the reference time axis:
df = pl.DataFrame({
"dt": ["2022-12-14T14:00:01.500", "2022-12-14T14:00:03.500", "2022-12-14T14:00:05.500"],
"v1": [1.5, 3.5, 5.5]})
df = df.with_columns(pl.col("dt").str.to_datetime())


I cannot join the dfs since keys don't match:

print(df_ref.join(df, on="dt", how="left").interpolate())
shape: (6, 2)
┌─────────────────────┬──────┐
│ dt ┆ v1 │
│ --- ┆ --- │
│ datetime[μs] ┆ f64 │
╞═════════════════════╪══════╡
│ 2022-12-14 14:00:01 ┆ null │
│ 2022-12-14 14:00:02 ┆ null │
│ 2022-12-14 14:00:03 ┆ null │
│ 2022-12-14 14:00:04 ┆ null │
│ 2022-12-14 14:00:05 ┆ null │
│ 2022-12-14 14:00:06 ┆ null │
└─────────────────────┴──────┘


So my 'iterative' approach would be to interpolate each column individually, for instance like

from scipy.interpolate import interp1d

f = interp1d(df["dt"].dt.timestamp(), df["v1"],
kind="linear", bounds_error=False, fill_value="extrapolate")

out = f(df_ref["dt"].dt.timestamp())
df_ref = df_ref.with_columns(pl.Series(out).alias("v1_interp"))

print(df_ref.head(6))
shape: (6, 2)
┌─────────────────────┬───────────┐
│ dt ┆ v1_interp │
│ --- ┆ --- │
│ datetime[μs] ┆ f64 │
╞═════════════════════╪═══════════╡
│ 2022-12-14 14:00:01 ┆ 1.0 │
│ 2022-12-14 14:00:02 ┆ 2.0 │
│ 2022-12-14 14:00:03 ┆ 3.0 │
│ 2022-12-14 14:00:04 ┆ 4.0 │
│ 2022-12-14 14:00:05 ┆ 5.0 │
│ 2022-12-14 14:00:06 ┆ 6.0 │
└─────────────────────┴───────────┘


Although this gives the result I need, I wonder if there is a more idiomatic approach? I hesitate to mention efficiency here since I haven't benchmarked this with real data yet ("measure, don't guess!"). However, I'd assume that a native implementation in the underlying Rust code could add some performance benefits.

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