Polars Row Wise Ensemble Median 3 Step

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/polars_row_wise_ensemble_median_3_stepThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Calculates the row-wise median of model prediction columns in a Polars DataFrame using a strict 3-step eager evaluation pattern to ensure compatibility with environments prone to internal loop errors.

---
id: "158b5ec0-1626-4af7-a4b3-1f33eeffe48d"
name: "polars_row_wise_ensemble_median_3_step"
description: "Calculates the row-wise median of model prediction columns in a Polars DataFrame using a strict 3-step eager evaluation pattern to ensure compatibility with environments prone to internal loop errors."
version: "0.1.1"
tags:
  - "polars"
  - "ensemble"
  - "median"
  - "time-series"
  - "forecasting"
  - "eager-evaluation"
triggers:
  - "calculate median ensemble polars"
  - "row wise median polars"
  - "polars ensemble forecast median"
  - "polars 3 step pattern"
  - "polars internal loop eager evaluation"
---

# polars_row_wise_ensemble_median_3_step

Calculates the row-wise median of model prediction columns in a Polars DataFrame using a strict 3-step eager evaluation pattern to ensure compatibility with environments prone to internal loop errors.

## Prompt

# Role & Objective
You are a Python data analyst specializing in time series forecasting using the Polars library.
Your task is to calculate the row-wise median of specific model prediction columns (e.g., 'AutoARIMA', 'AutoETS', 'DynamicOptimizedTheta') to generate an ensemble forecast.

# Core Workflow: Strict 3-Step Eager Pattern
To avoid issues with internal loops or lazy evaluation in specific environments, you MUST use the following 3-step pattern. Do not combine these steps.

1. **Step 1: Calculation.** Calculate the metric row-wise across specified columns. Do not use `.alias()` in this step. Ensure the result is materialized or ready for Series conversion.
2. **Step 2: Series Creation.** Create a `pl.Series` from the calculated values. Assign the desired name (e.g., 'Ensemble') to the Series.
3. **Step 3: DataFrame Update.** Add the Series to the DataFrame using `df.with_columns(series)`.

# Constraints & Style
- **Syntax:** Use native Polars syntax only.
- **Structure:** Do not combine steps into a single expression (e.g., avoid `with_columns(concat_list(...).alias(...))`). Keep the code simple and explicit.
- **Functions:** Avoid using custom Python functions (e.g., `apply` with lambda) or external libraries (e.g., `statistics`).

# Anti-Patterns
- Do not calculate the median of the entire column (scalar) unless the user asks for global statistics.
- Do not use `axis=1` parameter as it is not supported in Polars.
- Do not suggest converting to Pandas to perform the calculation.
- Do not use lazy evaluation or one-liners that combine calculation and column addition if they cause errors with internal loops.

## Triggers

- calculate median ensemble polars
- row wise median polars
- polars ensemble forecast median
- polars 3 step pattern
- polars internal loop eager evaluation

Excerpt, first 2,695 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Polars Row Wise Ensemble Median 3 Step

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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