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 grew 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. The habit it's built for is simple: being able to see every variable in your code, at the point it's actually computed, is what lets you notice when something is wrong, a value that shifted type, a filter that let too much or too little through, a join that didn't do what you expected. Data leakage and other silent corruption is usually invisible not because it's hard to catch, but because nobody was looking at the value when it happened. Making that look cheap and habitual, instead of a special debugging step you only reach for once something already seems broken, is the whole point.

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 an intermediate value can slip into a shared notebook or a production run by accident.

varview doesn't detect leakage, drift, or corruption on its own, it has no idea what your data means. What it does is make the habit that catches those problems, actually looking at your intermediate values, cheap enough to do every time instead of only after something already looks wrong.

Install

pip install varview

Usage

Basic

from varview import debug

count = 42
debug(count)          # unquoted variable
debug("count")         # a quoted string also works — looked up by name

Multiple variables, comma-separated

Pass as many variables as you want, separated by commas — no need to wrap them in a list:

count = 42
total = 137
status = "active"

debug(count, total, status)

count = 42

total = 137

status = active

Unquoted variables

You don't have to quote the name at all. varview reads the call's own source code to recover the label, so the variable itself can be passed directly:

debug(count)             # prints "count = 42", no quotes needed
debug(count, total)      # prints both, labels taken straight from source

This is resolved from the source text of the call, not from the runtime type of the value, so it stays correct even if a variable happens to hold a string. region = "north" passed unquoted as debug(region) still prints region = north, it is never mistaken for a name lookup the way a quoted "region" would be (which would instead look for a variable literally named north).

If the source can't be recovered (for example, calls made from a REPL or from exec()'d code), varview falls back to a best-effort heuristic instead of failing.

A variable holding a list or tuple is printed as one value

Each comma-separated argument is always treated as exactly one entry, no matter what its runtime value turns out to be, including when that value is itself a list or tuple:

quarterly_revenue = [120000, 135000, 128000, 151000]
debug(quarterly_revenue)

quarterly_revenue = [120000, 135000, 128000, 151000]

This prints as a single line, the variable is never unpacked into one entry per element. To see multiple entries, pass multiple comma-separated arguments instead:

monthly_churn_rate = 0.034
active_subscribers = 8421
quarterly_revenue = [120000, 135000, 128000, 151000]

debug(monthly_churn_rate, active_subscribers, quarterly_revenue)

monthly_churn_rate = 0.034

active_subscribers = 8421

quarterly_revenue = [120000, 135000, 128000, 151000]

Seeing a value instead of assuming it

The pattern that motivated this package: a quick, visible check right where a value is computed, left in place instead of deleted, and silenced in one line before anything ships.

raw_rows = len(df)
df = df.dropna(subset=["customer_id"])
clean_rows = len(df)
dropped = raw_rows - clean_rows

debug(raw_rows, clean_rows, dropped, color="red")

raw_rows = 10482

clean_rows = 10471

dropped = 11

Seeing dropped here is the difference between knowing exactly what a cleaning step did and just assuming it did what you meant.

Note: older versions of varview used a list-bracket calling convention, debug(["count", "total"]). That form is no longer treated specially, debug([a, b]) is now one argument whose value is a 2-element list, printed as a single line, not unpacked into two. Use plain comma-separated arguments instead: debug(a, b).

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

In vertical layout, you can pad labels so every = lines up in a neat column with align:

debug(count, total, status)                    # default: align=None, no padding, labels print as-is
debug(count, total, status, align="right")     # labels right-aligned
debug(count, total, status, align="left")      # labels left-aligned
debug(count, total, status, align="center")    # labels centered

align only affects orientation="vertical". It has no effect on horizontal output, since there's only one line to begin with.

Trim the surrounding whitespace

debug(count, total, margin=False)

By default, debug() prints a blank line before and after its output so it stands out in a busy terminal or log. Set margin=False for tightly packed output with nothing extra around it.

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 quoted name prints <not found> in place instead of raising and losing every other value you wanted to see. This applies to quoted names only. Unquoted variables are resolved directly from the argument itself, so there is no lookup that can fail.

API

debug(*names, color="green", display=True, orientation="vertical", align=None, margin=True)

All parameters are validated on every call: an invalid color, orientation, or align raises ValueError, an invalid display or margin type 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