file-converter

name	file-converter
description	Convert files between formats including CSV, JSON, YAML, XML, Markdown, and image formats.

File Converter

Convert between data, document, and image formats. One-liners for each conversion pair.

Tool Map

Domain	Tool	Install
CSV/JSON/Excel/Parquet	pandas	pip install pandas openpyxl pyarrow
YAML	pyyaml	pip install pyyaml
XML ↔ dict	xmltodict	pip install xmltodict
Any doc format ↔ any	pandoc (CLI)	apt install pandoc or pip install pypandoc_binary
Markdown → HTML	markdown	pip install markdown
HTML → Markdown	markdownify	pip install markdownify
.docx read/write	python-docx	pip install python-docx
PDF → text/tables	pdfplumber	pip install pdfplumber
PDF → images	pdf2image	pip install pdf2image + apt install poppler-utils
PDF manipulation	pypdf	pip install pypdf
Images	Pillow	pip install Pillow
SVG → PNG	cairosvg	pip install cairosvg
HEIC → JPG	pillow-heif	pip install pillow-heif

Data Formats

import pandas as pd, json, yaml, xmltodict

# --- CSV ↔ JSON ---
pd.read_csv("in.csv").to_json("out.json", orient="records", indent=2)
pd.read_json("in.json").to_csv("out.csv", index=False)

# --- CSV → Excel / Excel → CSV ---
pd.read_csv("in.csv").to_excel("out.xlsx", index=False, engine="openpyxl")
pd.read_excel("in.xlsx", sheet_name="Sheet1").to_csv("out.csv", index=False)

# All sheets: pd.read_excel("in.xlsx", sheet_name=None) → dict of DataFrames

# --- CSV → Parquet (columnar, compressed) ---
pd.read_csv("in.csv").to_parquet("out.parquet", engine="pyarrow", compression="snappy")

# --- YAML ↔ JSON ---
data = yaml.safe_load(open("in.yaml"))          # ALWAYS safe_load, never load()
json.dump(data, open("out.json", "w"), indent=2)
yaml.safe_dump(json.load(open("in.json")), open("out.yaml", "w"), sort_keys=False)

# --- XML ↔ JSON ---
data = xmltodict.parse(open("in.xml").read())
json.dump(data, open("out.json", "w"), indent=2)
open("out.xml", "w").write(xmltodict.unparse(data, pretty=True))

# --- JSONL (one JSON object per line) ---
pd.read_json("in.jsonl", lines=True).to_csv("out.csv", index=False)

Encoding gotchas:

• pd.read_csv("f.csv", encoding="utf-8-sig") strips the BOM that Excel inserts
• Auto-detect: import chardet; enc = chardet.detect(open("f.csv","rb").read())["encoding"]
• CSV delimiter sniffing: pd.read_csv("f.csv", sep=None, engine="python")

Nested JSON → flat CSV:

pd.json_normalize(data, sep=".").to_csv("out.csv", index=False)  # {"a":{"b":1}} → column "a.b"

Document Formats — pandoc is the Swiss Army knife

# Markdown → PDF (requires LaTeX: apt install texlive-xetex)
pandoc input.md -o output.pdf --pdf-engine=xelatex

# Markdown → DOCX
pandoc input.md -o output.docx

# DOCX → Markdown (extracts images to ./media/)
pandoc input.docx -o output.md --extract-media=.

# HTML → Markdown
pandoc input.html -o output.md -t gfm

# Any → Any (pandoc supports ~40 formats)
pandoc -f docx -t rst input.docx -o output.rst

# From Python
import pypandoc
pypandoc.convert_file("in.md", "docx", outputfile="out.docx")

Without pandoc (pure Python):

# Markdown → HTML
import markdown
html = markdown.markdown(open("in.md").read(), extensions=["tables", "fenced_code", "toc"])

# HTML → Markdown
from markdownify import markdownify
md = markdownify(html, heading_style="ATX")  # ATX = # headers, not underlines

PDF Operations

# --- Extract text + tables ---
import pdfplumber
with pdfplumber.open("in.pdf") as pdf:
    text = "\n".join(p.extract_text() or "" for p in pdf.pages)
    tables = pdf.pages[0].extract_tables()  # list of list-of-rows

# --- PDF → images (one PNG per page) ---
from pdf2image import convert_from_path
for i, img in enumerate(convert_from_path("in.pdf", dpi=200)):
    img.save(f"page_{i+1}.png")

# --- Merge / split / rotate ---
from pypdf import PdfReader, PdfWriter
writer = PdfWriter()
for path in ["a.pdf", "b.pdf"]:
    for page in PdfReader(path).pages:
        writer.add_page(page)
writer.write("merged.pdf")

# Extract pages 2–5
reader = PdfReader("in.pdf")
writer = PdfWriter()
for p in reader.pages[1:5]:
    writer.add_page(p)
writer.write("pages_2-5.pdf")

PDF gotchas:

• pdf2image needs poppler-utils installed system-wide (not a pip package)
• Scanned PDFs have no text layer — pdfplumber returns None. Use pytesseract OCR on pdf2image output.
• PyPDF2 is deprecated → use pypdf (same API, maintained fork)

Image Formats

from PIL import Image

# --- Basic conversion ---
Image.open("in.png").convert("RGB").save("out.jpg", quality=90)

# convert("RGB") is REQUIRED: JPEG can't store alpha channel, will raise OSError

# --- WebP (best web format) ---
Image.open("in.jpg").save("out.webp", quality=85, method=6)  # method 0-6, 6=best compression

# --- AVIF (smallest, Pillow 11+) ---
Image.open("in.jpg").save("out.avif", quality=75)

# --- HEIC (iPhone photos) → JPG ---
from pillow_heif import register_heif_opener
register_heif_opener()
Image.open("in.heic").convert("RGB").save("out.jpg", quality=90)

# --- SVG → PNG ---
import cairosvg
cairosvg.svg2png(url="in.svg", write_to="out.png", output_width=1024)

# --- Batch convert directory ---
from pathlib import Path
for p in Path("imgs").glob("*.png"):
    Image.open(p).convert("RGB").save(p.with_suffix(".jpg"), quality=85)

Image gotchas:

• PNG → JPG: must convert("RGB") first or transparency crashes the save
• quality for PNG is meaningless (lossless) — use optimize=True, compress_level=9
• Pillow can't open .svg natively — use cairosvg or svglib
• GIF → MP4 is a video operation: ffmpeg -i in.gif -pix_fmt yuv420p out.mp4

Validation

Always verify output:

# Row count parity
assert len(pd.read_csv("out.csv")) == len(pd.read_json("in.json"))

# JSON well-formed
json.load(open("out.json"))

# Image opens
Image.open("out.jpg").verify()