varview

A tiny, zero-dependency debug-printing helper for people who check their work, for clean automation.

from varview import debug

count = 42
total = 137
status = "active"

debug(["count", "total", "status"])

count: 42

total: 137

status: active

Why this exists

Most debugging habits start the same way: print(f"count: {count}"), scattered through a script, forgotten about, and left in, or worse, silently shaping a result nobody meant to ship.

varview was built out of a data analyst's day-to-day discipline: if you can't see your intermediate values clearly, you can't trust your final ones. Threshold sweeps, fold splits, feature combos, backtest tables: every step that touches a metric is a step where leakage, silent type coercion, or an off-by-one slice can quietly corrupt a result. The habit that catches this isn't a debugger or a notebook full of stray print() calls, it's making every checkpoint visible, on purpose, every time, with a single flip to turn it all off before anything ships.

That's the whole philosophy behind this package:

• See it before you trust it. Print the name and the value, side by side, with no risk of the label drifting out of sync with what it's labeling.
• Validate loudly, not quietly. Every parameter is checked up front; bad input fails fast with a clear error instead of producing a confusing result three functions later.
• Make leakage a choice, not an accident. One global switch silences every debug call in a file at once, so nothing you used to sanity-check a fold split or a train/test boundary can slip into a shared notebook or a production run by accident.

Install

pip install varview

Usage

Basic

from varview import debug

count = 42
debug(["count"])       # list of names
debug("count")          # a single string also works, no need to wrap it

Color-code by what the value means to you

debug(["count"], color="green")   # default, all good
debug(["count"], color="yellow")  # worth a second look
debug(["count"], color="red")     # flag it

Lay it out the way you're scanning

debug(["count", "total", "status"])                     # vertical (default)
debug(["count", "total", "status"], orientation="horizontal")  # count: 42, total: 137, status: active

Turn one call off without deleting it

debug(["count"], display=False)

Turn every call off at once, before you share or ship

from varview import debug_off, debug_on

debug_off()   # every debug() call in the process goes silent, even display=True ones
# ... run the rest of your pipeline, notebook export, whatever needs to be clean ...
debug_on()    # back to normal for your next debugging session

debug_off() is the one-line answer to "did I leave a debug print in this notebook before I sent it to someone." Flip it at the top of a cell, or right before a scheduled job runs, and every debug() call downstream goes quiet, no hunting through the file for calls you forgot about.

Typos don't kill the whole block

debug(["count", "totall", "status"])
# count: 42
# totall: <not found>
# status: active

One bad name prints <not found> in place instead of raising and losing every other value you wanted to see.

API

debug(names, color="green", display=True, orientation="vertical")

All four parameters are validated on every call: an invalid color or orientation raises ValueError, an invalid display type raises TypeError, and names must be a string or a list/tuple of strings or it raises TypeError. Nothing gets a chance to fail silently or print something misleading.

debug_off() / debug_on()

Module-level switch. debug_off() silences every debug() call in the running process, regardless of that call's own display value. debug_on() restores normal behavior.

License

MIT