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Pandas Data Processing

Checked
vamseeachanta·vlatest·data

Processes data in Pandas DataFrames using Python code to clean, transform, analyze, and visualize datasets effectively.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add vamseeachanta/workspace-hub --skill pandas-data-processing -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

vamseeachanta

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned vamseeachanta/workspace-hub at 5190ace4e5.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
5190ace4e5
Manifest
_archive/skills/programming/pandas-data-processing/SKILL.md
Scanned
3 files, 1,369 lines
Copies in repo
3 — all of them scanned
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

What it does

This skill enables AI agents to process data within Pandas DataFrames using Python code. It's specifically designed for time series analysis, handling OrcaFlex simulation results, and creating configuration-driven data workflows common in marine and offshore engineering contexts. The skill facilitates cleaning, transforming, analyzing, and visualizing datasets from various formats like CSV, Excel, HDF5, and Parquet.

When to use it

  • Time series analysis: Analyzing wave elevation, vessel motions, or mooring tensions over time.
  • OrcaFlex results processing: Analyzing load simulation results, Response Amplitude Operators (RAOs), and dynamics data from OrcaFlex simulations.
  • Combining multiple data formats: Working with datasets in CSV, Excel, HDF5, or Parquet formats.
  • Statistical analysis: Calculating summary statistics, applying rolling windows, or resampling time series data.
  • Automated report generation: Extracting and summarizing data for engineering reports.

Key capabilities

  • Time Series Analysis: Loading and processing time series data from CSV files, setting a specified column as the index (and parsing it as dates if desired).
  • Data Loading: Supports loading data from CSV, Excel, HDF5, and Parquet file formats.
  • Pandas DataFrame Manipulation: Provides capabilities for pivot, melt, merge, and group operations on DataFrames.
  • Statistical Analysis: Enables the calculation of summary statistics and resampling time series.

Example prompts

  • "Load the vessel motions data from data/processed/vessel_motions.csv and show me the time range."
  • "Analyze the OrcaFlex results to calculate the average RAO over a rolling window of 10 seconds."
  • “Create a summary table of the mooring tensions, including min, max, and mean values.”

Tips & gotchas

  • Dataset Size: This skill is best suited for datasets up to approximately 100GB. For larger datasets, consider using alternatives like Dask, Vaex, or PySpark.
  • Real-time Data: This skill isn't designed for real-time streaming data; use libraries optimized for that purpose instead (e.g., Polars).
  • Numerical Computation: If your task involves primarily numerical computation without the need for DataFrame manipulation, consider using NumPy directly.