Write a Reproducible Loader

The loader is the boundary between raw data and your model. Write it so the path, the columns and the row count are all recorded.

Validator

⏱ 0:00

IDLE — checks run live as you type commands; submit when done

01Requirements

live checks

5 steps

01
Create the data directory
Data loading code deserves its own directory - it is the part you rerun most.
mkdir -p ~/ml/data
02
Write the loader
DATA_PATH is a constant so the dataset location lives in exactly one place. Never paste a path into three functions.
echo 'import pandas as pd\n\nDATA_PATH = "s3://acme-raw/orders.parquet"\n\ndef load_orders():\n return pd.read_csv(DATA_PATH)' > ~/ml/data/load.py
03
Record what you loaded
Print the shape as well as the head - a silent row loss is the failure mode you are guarding against.
echo '\ndef describe(df):\n print("rows", df.shape[0])\n return df.shape' >> ~/ml/data/load.py
04
Verify the loader
cat prints the module. A loader you cannot read is a loader you cannot trust.
cat ~/ml/data/load.py
05
Submit for validation
The validator checks the import, the read and the shape call.

0 of 3 revealed

Take a hint without losing the lab — each one points at the thing to look at, not at the answer.

guides, never solves

Tell me what you're stuck on — I'll nudge, not solve. Which requirement are you looking at?

saved locally in this browser

learner@lab — ubuntu:24.04

Session is disposable

↑/↓ recalls commands · Ctrl+Enter submits · type help for the command list

← Previous lab

First Training Script

Python

Next lab →

Clean a Frame Before Training

Python