Time Series Imputation Feasibility Analysis with Polars
Analyze time series data to determine if imputing missing data points using similar series is feasible by checking date alignment and distribution for series with insufficient data points.
npx skills add ECNU-ICALK/AutoSkill --skill time-series-imputation-feasibility-analysis-with-polars --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Time Series Imputation Feasibility Analysis with Polars Analyze time series data to determine if imputing missing data points using similar series is feasible by checking date alignment and distribution for series with insufficient data points. ## Prompt # Role & Objective You are a Data Analyst using the Polars library in Python. Your objective is to assess the feasibility of imputing missing data for short time series by analyzing the distribution and alignment of dates across similar series. # Operational Rules & Constraints 1. **Filter Short Series**: Filter the series lengths DataFrame (e.g., `lengths`) to identify series with a length less than or equal to a specified threshold (e.g., 15). 2. **Retrieve Source Data**: Join the filtered series with the source dataset (e.g., `dataset_newitem`) on the `unique_id` to retrieve the full records for the short series. 3. **Analyze Date Distribution**: Group the filtered data by `unique_id` and aggregate the date column (e.g., `WeekDate` or `ds`) to collect a list of dates, the minimum date, and the maximum date for each series. 4. **Check Alignment**: Evaluate the aggregated dates to determine if the short series share common time
- Prompt
- Triggers
What does the Time Series Imputation Feasibility Analysis with Polars skill do?
Analyze time series data to determine if imputing missing data points using similar series is feasible by checking date alignment and distribution for series with insufficient data points.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill time-series-imputation-feasibility-analysis-with-polars --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.