export

name	export
description	Write a dataset (array of records) to CSV, JSON, XLSX, or PDF through one interface — format chosen by the output extension.
version	0.1.0

export

One way to hand a dataset off as a file. Use this at the end of a task when you have rows of data (a list of records) and need to deliver them as CSV, JSON, XLSX, or PDF — without remembering a different library for each.

The helper at scripts/export.py takes a JSON array of objects on stdin (or from a file) and writes the format implied by the output filename's extension:

Extension	Format	Dependency
.csv	CSV	stdlib
.json	pretty JSON	stdlib
.xlsx	Excel workbook	openpyxl (pip install openpyxl)
.pdf	table on a PDF page	reportlab (pip install reportlab)

CSV and JSON need nothing installed. XLSX and PDF require their library — install it before running, and only when you actually target that format.

Input shape

A JSON array of flat objects (the records). The column order is taken from the union of keys, first-seen order:

[
  {"id": 1, "name": "Ada",  "score": 91},
  {"id": 2, "name": "Linus", "score": 88}
]

Using the helper

# From a file, choosing format by extension
python scripts/export.py data.json --out report.csv
python scripts/export.py data.json --out report.xlsx
python scripts/export.py data.json --out report.pdf --title "Scores"

# From stdin (e.g. piped from another tool)
some-command | python scripts/export.py - --out out.json

Procedure

1. Assemble the records as a JSON array of objects. Normalize each record to flat key/value pairs (nested objects don't map cleanly to a table — flatten or JSON-encode them into a single cell first).
2. Pick the output format by naming the file — .csv, .json, .xlsx, or .pdf. Install the dependency for xlsx/pdf if you're using them.
3. Run the helper, then verify the file exists and is non-empty (ls -l report.xlsx).

Cautions

• Declare xlsx/pdf dependencies to the runner: openpyxl / reportlab are not stdlib.
• Inconsistent keys across records are fine — missing cells are written empty — but a wildly ragged dataset usually signals the records weren't normalized.
• PDF export renders a simple table; very wide datasets won't fit a page well — prefer CSV/XLSX for many columns.