Polars Common Issues

Sub-skill of polars: Common Issues.

vamseeachanta 2e940e4 1.0 KB Updated

File contents

Common Issues

Common Issues

Issue: Out of Memory

# Solution 1: Use streaming
result = lf.collect(streaming=True)

# Solution 2: Sink to file
lf.sink_parquet("output.parquet")

# Solution 3: Process in chunks
for chunk in pl.read_csv_batched("large.csv", batch_size=100000):
    process(chunk)

Issue: Slow Performance

# Check query plan for inefficiencies
print(lf.explain(optimized=True))

# Use profiling
result = lf.profile()
print(result[1])  # Timing information

Issue: Type Mismatch in Join

# Ensure matching types before join
df1 = df1.with_columns(pl.col("id").cast(pl.Int64))
df2 = df2.with_columns(pl.col("id").cast(pl.Int64))
result = df1.join(df2, on="id")

Issue: Date Parsing Errors

# Explicit format specification
df = df.with_columns([
    pl.col("date_str").str.strptime(pl.Date, "%Y-%m-%d"),
    pl.col("datetime_str").str.strptime(pl.Datetime, "%Y-%m-%d %H:%M:%S")
])

vamseeachanta/workspace-hub/tree/main/.agents/skills/_archive/data/analysis/polars/common-issues commit 2e940e4f93

Frequently asked questions

npx skillmds@latest add vamseeachanta/polars-common-issues