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Fast in-memory DataFrame library for datasets that fit in RAM.
Passed 21 of 22 test cases with the skill, and 20 of 22 without it.
1 fixed20 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
In a Polars DataFrame with columns `id`, `q1_value`, `q2_value`, and `q3_value`, I need to double all columns that end with `_value`. Should I iterate over column names in Python or use expression pattern matching? Provide the expression code.
| Checked | Without skill | With skill |
|---|---|---|
| Uses a regex pattern string inside pl.col matching columns ending in _value. | Missed | Passed |
The instructions your model reads when the skill loads.
-----|--------|--------|
| Select column | df["col"] | df.select("col") |
| Filter | df[df["col"] > 10] | df.filter(pl.col("col") > 10) |
| Add column | df.assign(x=...) | df.with_columns(x=...) |
| Group by | df.groupby("col").agg(...) | df.group_by("col").agg(...) |
| Window | df.groupby("col").transform(...) | df.with_columns(...).over("col") |
Pandas sequential (slow):
df.assign(
col_a=lambda df_: df_.value * 10,
col_b=lambda df_: df_.value * 100
)
Polars parallel (fast):
df.with_columns(
col_a=pl.col("value") * 10,
col_b=pl.col("value") * 100,
)
For comprehensive migration guide, load references/pandas_migration.md.
Use lazy evaluation for large datasets:
lf = pl.scan_csv("large.csv") # Don't use read_csv
result = lf.filter(...).select(...).collect()
Avoid Python functions in hot paths:
.map_elements() only when necessarytest cases passed, vs 20 / 22 without
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