// Fix function call sites to pass positional args by position and assign constants to intermediate variables
Refactor unit test files by aligning strings, renaming methods, and factoring out common test code
Add figures to a blog post
Write a blog post about a machine learning library or technique for a technical audience
Identify and refactor duplicated code blocks into shared functions across Python files
Find documentation files for a given dir, file, class, or function and summarize in 3 bullet points
Replace "from X import Y" style imports with "import X" and update usages throughout a file
| name | coding.fix_param_use |
| description | Fix function call sites to pass positional args by position and assign constants to intermediate variables |
I will pass you a file
In that file, make sure that:
For a function with the signature
def apply_llm_prompt_to_df(
prompt: str,
df: pd.DataFrame,
extractor: Callable[[Union[str, pd.Series]], str],
target_col: str,
batch_mode: str,
*,
model: str,
batch_size: int = 50,
dump_every_batch: Optional[str] = None,
tag: str = "Processing",
testing_functor: Optional[Callable[[str], str]] = None,
use_sys_stderr: bool = False,
) -> Tuple[pd.DataFrame, Dict[str, int]]:
Bad
df, stats = hllmcli.apply_llm_prompt_to_df(
prompt=prompt,
df=df,
extractor=extract_person_industry_from_df,
target_col="industry",
batch_mode=batch_mode,
batch_size=batch_size,
model=model,
tag=tag,
)
Good
target_col = "industry"
df, stats = hllmcli.apply_llm_prompt_to_df(
prompt,
df,
extract_person_industry_from_df,
target_col,
batch_mode,
batch_size=batch_size,
model=model,
tag=tag,
)