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Sub-skill of polars: 1. Use Lazy Evaluation by Default (+4).
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| name | polars-1-use-lazy-evaluation-by-default |
| description | Sub-skill of polars: 1. Use Lazy Evaluation by Default (+4). |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
# GOOD: Lazy evaluation allows optimization
lf = pl.scan_parquet("data.parquet")
result = (
lf
.filter(pl.col("x") > 0)
.select(["x", "y"])
.collect()
)
# AVOID: Eager evaluation for large files
df = pl.read_parquet("data.parquet") # Loads everything
df = df.filter(pl.col("x") > 0)
df = df.select(["x", "y"])
# GOOD: Single chain, optimized execution
result = (
df
.filter(pl.col("status") == "active")
.with_columns([
(pl.col("a") + pl.col("b")).alias("sum"),
pl.col("date").dt.year().alias("year")
])
.group_by("year")
.agg(pl.col("sum").mean())
)
# AVOID: Multiple separate operations
df = df.filter(pl.col("status") == "active")
df = df.with_columns((pl.col("a") + pl.col("b")).alias("sum"))
df = df.with_columns(pl.col("date").dt.year().alias("year"))
result = df.group_by("year").agg(pl.col("sum").mean())
# Optimize memory with correct types
df = df.with_columns([
pl.col("small_int").cast(pl.Int16),
pl.col("category").cast(pl.Categorical),
pl.col("flag").cast(pl.Boolean),
pl.col("precise_float").cast(pl.Float32) # If precision allows
])
# Check memory usage
print(df.estimated_size("mb"))
# GOOD: Filter before expensive operations
result = (
pl.scan_parquet("data.parquet")
.filter(pl.col("date") >= "2025-01-01") # Filter first
.group_by("category")
.agg(pl.col("value").sum())
.collect()
)
# AVOID: Filter after loading everything
result = (
pl.scan_parquet("data.parquet")
.group_by("category")
.agg(pl.col("value").sum())
.filter(...) # Too late, already processed all data
.collect()
)
# GOOD: Vectorized expression
df.with_columns([
pl.when(pl.col("x") > 0).then(pl.col("x")).otherwise(0).alias("positive_x")
])
# AVOID: Python function (slow)
df.with_columns([
pl.col("x").map_elements(lambda v: v if v > 0 else 0).alias("positive_x")
])