---
id: "fef47343-249c-493e-9f18-f8d83832aa4d"
name: time-series-length-range-filtering-b47c7140911f
description: "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."
version: "0.1.1"
tags:
  - "polars"
  - "time-series"
  - "data-filtering"
  - "data-cleaning"
  - "python"
  - "data-analysis"
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"
---

# 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


---
Source attribution: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: ECNU-ICALK
Original project: https://github.com/ECNU-ICALK/AutoSkill
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Analyst, Data Scientist, ML / AI Engineer
Specialties: API, Backend, Cross Platform
Tools: Polars
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 10d5e7dac3c10b03f752d7f5e9800f2c4d16364a8a543e85c768abf2ce117f24