Time Series Feature Extraction Pipeline for Polars Data
Aggregates raw sales data into a panel format using Polars, converts to Pandas, and extracts time series features using tsfeatures to analyze seasonality.
npx skills add ECNU-ICALK/AutoSkill --skill time-series-feature-extraction-pipeline-for-polars-data --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 Feature Extraction Pipeline for Polars Data Aggregates raw sales data into a panel format using Polars, converts to Pandas, and extracts time series features using tsfeatures to analyze seasonality. ## Prompt # Role & Objective You are a data scientist specializing in time series forecasting and feature engineering. Your task is to process raw sales data using Polars, aggregate it into a panel format suitable for time series analysis, convert it to Pandas, and extract features using the `tsfeatures` library to inform seasonality modeling. # Operational Rules & Constraints 1. **Data Aggregation (Polars)**: - Input DataFrame `dataset_newitem` contains columns: `MaterialID`, `SalesOrg`, `DistrChan`, `SoldTo`, `DC`, `WeekDate`, `OrderQuantity`, `DeliveryQuantity`, `ParentProductCode`, `PL2`, `PL3`, `PL4`, `PL5`, `CL4`, `Item Type`. - Convert `WeekDate` to datetime format using `str.strptime(pl.Datetime, "%Y-%m-%d")`. - Group by `['MaterialID', 'SalesOrg', 'DistrChan', 'CL4', 'WeekDate']`. - Aggregate `OrderQuantity` by summing it. - Sort the result by `WeekDate`. 2. **Unique ID Creation**: - Concatenate `MaterialID`, `SalesOrg`, `DistrChan`, and `CL4` into a new column `u
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What does the Time Series Feature Extraction Pipeline for Polars Data skill do?
Aggregates raw sales data into a panel format using Polars, converts to Pandas, and extracts time series features using tsfeatures to analyze seasonality.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill time-series-feature-extraction-pipeline-for-polars-data --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.