CDA Show Int

Integers are the quiet little numbers of a data pipeline - the row counts, the group sizes, the slice positions - and they're the easiest to lose track of. CDA Show Int takes an INT and prints it as text on the node body, so when your graph runs, you actually see the number instead of hoping the wire is telling the truth. It's a two-second debugging habit that saves you from discovering a wrong row count ten nodes downstream.

It's the integer half of the CDA display pair in HowToSD's ComfyUI-Data-Analysis pack, sitting alongside CDA Show Float. Both are rebuilt on the Show Text pattern from pythongosssss's ComfyUI-Custom-Scripts (MIT, credited in the pack). The author's description: "Displays a Pandas Int as text."

How it works

Same machinery as the other show nodes: it's an output node with INPUT_IS_LIST = True, so it reads the first value from its input, runs it through str(), and hands the text to the pack's ShowText.js frontend via a ui payload. The hidden unique_id/extra_pnginfo inputs tell the frontend which node to repaint. Run the graph, the integer appears on the node. The STRING output is also a genuine output if you want to forward or save the text.

Inputs and outputs

• int_scalar (required, INT, default 0) - the integer to display. Wire it from CDA Int Create, Pandas Count, Pandas Sum on a boolean mask, or any INT source.
• STRING output (list) - the text form, usable as a real output.

When you'd reach for it

Debugging, and it's the right tool for verifying scalar results before they feed something important. A classic: run Pandas Is NA (which gives a boolean DataFrame), feed it through Pandas Sum to count the missing values per column, then CDA Show Int to see the totals on screen. Same for row counts before you slice or join - confirm the number, then commit.

The author's framing for the pack is data analysis in the graph - the README's baseball example is literally "which MLB player had the most hits per year" - and show nodes are the ones that let you read the answers, not just compute them silently.

Installing it

Standard pack install. ComfyUI Manager: search "Data analysis", install ComfyUI-Data-Analysis, restart, reload the browser tab. Manual:

cd ComfyUI/custom_nodes
git clone https://github.com/HowToSD/ComfyUI-Data-Analysis
mv ComfyUI-Data-Analysis data-analysis   # README: examples expect this folder name
pip install -r data-analysis/requirements.txt

Dependencies: pandas, matplotlib, seaborn, scipy, scikit-learn, openpyxl, lxml. No GPU required. The PyTorch-tensor nodes moved to ComfyUI-Pt-Wrapper in the March 2025 update.

Gotchas

• First value only. List inputs display element zero - the Show Text design.
• Terminal node. Inspect at the end of a branch; don't route a working pipeline through it.
• Widget bounds are oddly tight. The built-in int_scalar widget min is -1 and max is 2^31, which can surprise you if you're typing large negatives - drive the value from CDA Int Create when you're out of that range.