Dataset size?
├─ < 1M rows → Pandas OK (simpler API, richer ecosystem)
├─ 1M-10M rows → Consider Polars (2-5x faster, less memory)
└─ > 10M rows → Use Polars (required for memory efficiency)

Operations?
├─ Simple transforms → Either works
├─ Group-by aggregations → Polars 5-10x faster
├─ Complex joins → Polars with lazy evaluation
└─ Streaming/chunked → Polars scan_* functions

Integration?
├─ scikit-learn heavy → Pandas (better interop)
├─ PyTorch/custom → Polars + Arrow (zero-copy to tensor)
└─ ClickHouse source → Arrow stream → Polars (optimal)

# BAD: 3 memory copies
df = pd.read_sql(query, conn)     # Copy 1: DB → pandas
X = df[features].values           # Copy 2: pandas → numpy
tensor = torch.from_numpy(X)      # Copy 3: numpy → tensor
# Peak memory: 3x data size

# GOOD: 0-1 memory copies
import clickhouse_connect
import polars as pl
import torch

client = clickhouse_connect.get_client(...)
arrow_table = client.query_arrow("SELECT * FROM bars")  # Arrow in DB memory
df = pl.from_arrow(arrow_table)                          # Zero-copy view
X = df.select(features).to_numpy()                       # Single allocation
tensor = torch.from_numpy(X)                             # View (no copy)
# Peak memory: 1.2x data size

def query_arrow(client, query: str) -> pl.DataFrame:
    """ClickHouse → Arrow → Polars (zero-copy chain)."""
    arrow_table = client.query_arrow(f"{query} FORMAT ArrowStream")
    return pl.from_arrow(arrow_table)

# Usage
df = query_arrow(client, "SELECT * FROM bars WHERE ts >= '2024-01-01'")

# Polars has native ClickHouse support (see pola.rs for version requirements)
df = pl.read_database_uri(
    query="SELECT * FROM bars",
    uri="clickhouse://user:pass@host/db"
)

# For reproducible batch processing
client.query("SELECT * FROM bars INTO OUTFILE 'data.parquet' FORMAT Parquet")
df = pl.scan_parquet("data.parquet")  # Lazy, memory-mapped

from torch.utils.data import TensorDataset, DataLoader

# Accept both pandas and polars
def prepare_data(df) -> tuple[torch.Tensor, torch.Tensor]:
    if isinstance(df, pd.DataFrame):
        df = pl.from_pandas(df)

    X = df.select(features).to_numpy()
    y = df.select(target).to_numpy()

    return (
        torch.from_numpy(X).float(),
        torch.from_numpy(y).float()
    )

X, y = prepare_data(df)
dataset = TensorDataset(X, y)
loader = DataLoader(dataset, batch_size=32, pin_memory=True)

class PolarsDataset(torch.utils.data.Dataset):
    """Memory-efficient dataset from Polars DataFrame."""

    def __init__(self, df: pl.DataFrame, features: list[str], target: str):
        self.arrow = df.to_arrow()  # Arrow backing for zero-copy slicing
        self.features = features
        self.target = target

    def __len__(self) -> int:
        return self.arrow.num_rows

    def __getitem__(self, idx: int) -> tuple[torch.Tensor, torch.Tensor]:
        row = self.arrow.slice(idx, 1)
        x = torch.tensor([row[f][0].as_py() for f in self.features], dtype=torch.float32)
        y = torch.tensor(row[self.target][0].as_py(), dtype=torch.float32)
        return x, y

# Define transformations lazily (no computation yet)
pipeline = (
    pl.scan_parquet("raw_data.parquet")
    .filter(pl.col("timestamp") >= start_date)
    .with_columns([
        (pl.col("close").pct_change()).alias("returns"),
        (pl.col("volume").log()).alias("log_volume"),
    ])
    .select(features + [target])
)

# Execute only when needed
train_df = pipeline.filter(pl.col("timestamp") < split_date).collect()
test_df = pipeline.filter(pl.col("timestamp") >= split_date).collect()

# Process file in chunks (never loads full file)
def process_large_file(path: str, chunk_size: int = 100_000):
    reader = pl.scan_parquet(path)

    for batch in reader.iter_batches(n_rows=chunk_size):
        # Process each chunk
        features = compute_features(batch)
        yield features.to_numpy()

from pydantic import BaseModel, field_validator

class FeatureConfig(BaseModel):
    features: list[str]
    target: str
    seq_len: int = 15

    @field_validator("features")
    @classmethod
    def validate_features(cls, v):
        required = {"returns_vs", "momentum_z", "atr_pct"}
        missing = required - set(v)
        if missing:
            raise ValueError(f"Missing required features: {missing}")
        return v

def validate_schema(df: pl.DataFrame, required: list[str], stage: str) -> None:
    """Fail-fast schema validation."""
    missing = [c for c in required if c not in df.columns]
    if missing:
        raise ValueError(
            f"[{stage}] Missing columns: {missing}\n"
            f"Available: {sorted(df.columns)}"
        )

# BAD: Convert back and forth
df_polars = pl.from_pandas(df_pandas)
df_pandas_again = df_polars.to_pandas()  # Why?

# BAD: Defeats lazy evaluation
df = pl.scan_parquet("data.parquet").collect()  # Full load
filtered = df.filter(...)  # After the fact

# GOOD: Filter before collect
df = pl.scan_parquet("data.parquet").filter(...).collect()

# BAD: Loads entire file
df = pl.read_parquet("huge_file.parquet")

# GOOD: Stream in chunks
for batch in pl.scan_parquet("huge_file.parquet").iter_batches():
    process(batch)