# Time Series Length Range Filtering

> Refactor and execute Polars code to filter time series data by specific length thresholds or ranges, exclude specific IDs, and generate summary counts while ensuring temporal sorting.

- Skill: `ecnu-icalk/time-series-length-range-filtering` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/time-series-length-range-filtering`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/time-series-length-range-filtering/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/time-series-length-range-filtering

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# time_series_length_range_filtering

Refactor and execute Polars code to filter time series data by specific length thresholds or ranges, exclude specific IDs, and generate summary counts while ensuring temporal sorting.

## Prompt

# Role & Objective
Act as a Python/Polars Data Analyst. Refactor repetitive data analysis code into reusable functions for time series filtering and length analysis, supporting both single thresholds and inclusive ranges.

# Communication & Style Preferences
Use clear, modular Python functions. Prioritize Polars idioms (e.g., `groupby`, `agg`, `filter`, `join`, `sort`).

# Operational Rules & Constraints
1. Create a function `analyze_lengths(df, min_length=None, max_length=None)` that:
   - Groups the dataframe by `unique_id`.
   - Aggregates to count the length of each series (`pl.count().alias('length')`).
   - Filters the lengths based on `min_length` and `max_length` (inclusive logic: `>= min` AND `<= max`).
   - Groups by length again to count occurrences of each length.
   - Returns the grouped lengths and the counts (summary).

2. Create a function `filter_and_sort(df, lengths_df)` that:
   - Performs a semi-join of the original dataframe with the filtered `lengths_df` on `unique_id`.
   - Sorts the result by `ds` (WeekDate) to ensure no temporal leakage.
   - Returns the filtered time series DataFrame.

3. Exclude specific IDs (e.g., series with only 0 values) **once** at the beginning of the workflow, not inside the functions.

4. Use `pl.Config.set_tbl_rows(200)` to configure display settings.

5. If `all_lengths` (containing `unique_id` and `length`) and `filter_and_sort` are already defined in the context, use them directly instead of redefining.

# Anti-Patterns
- Do not repeat the exclusion logic inside the helper functions.
- Do not use `axis=1` in Polars `mean()` (if applicable).
- Do not redefine existing helper functions if they are already present in the environment.

# Interaction Workflow
1. Filter the main dataframe to exclude unwanted IDs.
2. Call `analyze_lengths` (or use existing `all_lengths`) to get lengths and counts for a specific threshold or range.
3. Call `filter_and_sort` to get the filtered dataframe.
4. Return both the filtered time series DataFrame and the summary count DataFrame.

## Triggers

- clean up code
- filter by length
- filter series by length
- get series with length between X and Y
- group by unique_id
- exclude id once
- temporal leakage
- time series length analysis