ChatGPT Code Interpreter Tutorial: A Persistent Alternative on Zo

ChatGPT Code Interpreter (now usually labeled Advanced Data Analysis) is a good way to do quick, one-off work: upload a CSV/PDF, ask for a chart, download the result.

If you’re here, you probably want something slightly different:

• a workflow that doesn’t disappear when a chat resets

• repeatability (same analysis next week, on new data)

• files you can keep, version, and re-run

This tutorial explains what Code Interpreter is good at, where it tends to fail, and how to get the same “upload → analyze → output files” loop on Zo Computer, where your files live on a real Linux machine and persist.

What ChatGPT Code Interpreter is (and what it isn’t)

OpenAI exposes “Code Interpreter” as a tool that runs code in a sandboxed environment, usually with Python, to analyze uploaded files and generate outputs (plots, spreadsheets, transformed files, etc.).^1

It’s excellent for:

• quick charts and summaries

• data cleaning and format conversion

• exploratory analysis when you don’t need long-term state

It’s not designed to be your long-running “analysis workspace.” In practice, you’ll eventually hit issues like:

• session / download links expiring

• inability to re-run the exact same work later unless you carefully copy everything out

• inability to turn the workflow into an automation that runs on a schedule

The Zo alternative: treat “analysis” like a real project

On Zo, you’re not relying on a temporary chat sandbox. You have:

• a persistent filesystem (folders and files stick around)

• a terminal for installing tools and running scripts

• an AI that can read/write files and run commands

• agents to re-run a workflow on a schedule

Step 1: Create a project folder and put your data there

Create a dedicated folder for the analysis, so it stays organized and repeatable.

Example layout:

• Data/my-analysis/input/ — raw uploads

• Data/my-analysis/output/ — generated charts/files

• Data/my-analysis/scripts/ — scripts you can re-run

• Data/my-analysis/notes.md — what you did and why

If you already have a file you would normally upload to ChatGPT (CSV, XLSX, PDF), put it in input/.

Step 2: Ask Zo to analyze the file and save real outputs

In ChatGPT Code Interpreter, you might say “plot revenue over time” and download a chart.

On Zo, do the same, but with persistence:

1. Tell Zo what file to analyze (in input/).

2. Tell Zo what to produce (a chart, a cleaned CSV, a summary report).

3. Tell Zo where to save it (in output/).

A good instruction is concrete:

• “Read Data/my-analysis/input/sales.csv. Clean obvious nulls, parse dates, and write Data/my-analysis/output/sales_clean.csv. Then generate a monthly revenue chart and save it as Data/my-analysis/output/revenue_by_month.png.”

Because outputs land in your filesystem, you don’t lose them when a chat ends.

Step 3: Make it repeatable (save a script)

If you expect to run the same analysis again (new export every week), convert the work into a script.

A minimal approach is to have Zo write a Python script into scripts/.

Example script name:

• Data/my-analysis/scripts/run.py

Then the workflow becomes:

python3 Data/my-analysis/scripts/run.py \
  --input Data/my-analysis/input/sales.csv \
  --outdir Data/my-analysis/output

Repeatability is the whole point: you should be able to delete output/, rerun the script, and regenerate everything.

Step 4: Turn it into an automation (optional, but this is the real unlock)

Once you have a repeatable script, you can run it automatically.

Examples:

• Every morning: pull the latest CSV from a synced folder, run the script, write a short summary, and email it.

• Every Friday: regenerate charts and publish them to a shared folder.

On Zo this is done with Agents (scheduled tasks). See the Agents docs for the basic model: a schedule + an instruction + a delivery method.^2

Step 5: When you should still use ChatGPT Code Interpreter

Use ChatGPT Code Interpreter / Advanced Data Analysis when:

• you’re doing a one-off exploration and don’t care about long-term persistence

• you don’t need to keep files or reproduce the exact analysis later

Move the workflow to Zo when:

• you’ve been burned by “session expired” / vanishing downloads

• you need a stable place for files and scripts

• you want the analysis to run again automatically

Summary

ChatGPT Code Interpreter is a fast scratchpad.

Zo is a durable workspace: the same “upload → analyze → output files” loop, but backed by a real filesystem, real scripts, and automation.

If you tell me what file type you’re usually analyzing (CSV, Excel, PDF, logs) and what output you want (chart, cleaned dataset, weekly report), I can turn that into a ready-to-rerun project structure and an agent instruction.