It’s just the lazy/expression part of the API, which is really the bread and butter of polars, rather than just being “replacement syntax” for pandas. This allows you to tap into abstraction that SQL can’t keep up with:

  import polars as pl

  # 1. Base Dataset
  lazy_df = pl.LazyFrame(
    {
      "store_id": ["S01", "S02", "S03", "S04", "S05"],
      "revenue": [5000.0, 2400.0, 15000.0, 900.0, 3200.0],
      "margin": [0.45, 0.30, 0.60, 0.15, 0.50],
      "tx_count": [120, 45, 300, 20, 85],
      "returns": [5, 12, 45, 2, 8],
    }
  )

  # 2. Define Layer Abstractions
  def get_kpi_layer() -> list[pl.Expr]:
    return [
      (pl.col("returns") / pl.col("tx_count")).alias("return_rate"),
      (pl.col("revenue") / pl.col("tx_count")).alias("avg_order_value"),
    ]

  def get_threshold_layer(thresholds: dict[str, list[float]]) -> list[pl.Expr]:
    return [
      (pl.col(col) > limit).alias(f"is_{col}above{int(limit)}")
      for col, limits in thresholds.items()
      for limit in limits
    ]

  def get_interaction_layer(numeric_cols: list[str]) -> list[pl.Expr]:
    return [
      (pl.col(a) / (pl.col(b) + 1e-5)).alias(f"ratio_{a}per{b}")
      for i, a in enumerate(numeric_cols)
      for b in numeric_cols[i + 1 :]
    ]

  def get_segmentation_layer() -> list[pl.Expr]:
    return [
      pl.when(pl.col("margin") > 0.4)
      .then(pl.literal("High"))
      .otherwise(pl.literal("Low"))
      .alias("margin_profile")
    ]

  # 3. Consolidate and Execute Single Graph Pass
  thresholds = {"revenue": [1000.0, 5000.0, 10000.0], "tx_count": [50, 100, 200]}
  numeric_cols = ["revenue", "margin", "tx_count", "returns"]

  expr_pool = [
    *get_kpi_layer(),
    *get_threshold_layer(thresholds),
    *get_interaction_layer(numeric_cols),
    *get_segmentation_layer(),
  ]

  final_df = lazy_df.with_columns(expr_pool).collect()