Pandas Fill NA Scalar Int

Some data types are allergic to empty cells, and integer-ish data is the most obvious victim. Load a CSV of counts or IDs with a few blank cells and those rows silently become NaN - float, not int, and suddenly every count operation is contaminated. Pandas Fill NA Scalar Int replaces every missing value with an integer you choose, so your data stays whole and your aggregations stay sane.

It's the integer sibling of Pandas Fill NA Scalar Float in the ComfyUI-Data-Analysis pack, HowToSD's collection that exposes Pandas to the node graph. Reach for it right after loading a file and before you compute anything: totals, counts, group-bys, or charts.

How it works

Under the hood it's dataframe.fillna(int_scalar), returned as a new DATAFRAME - the original frame is untouched. Like the float version, one scalar fills every hole in the entire frame, so there's no per-column nuance. That's fine for the typical case: you loaded a messy file, you want the blanks gone, you pick a sensible default like 0.

A practical note on filling with 0: it's the fastest way to silence missing-data errors, but it's also lying to your analysis - a 0 is a real value. If the gaps are meaningful, consider Pandas Drop NA to remove those rows entirely, or Pandas Is NA first to see exactly where the holes are. Fill is the right call when you need to preserve the row count.

Inputs and outputs

• dataframe (required, DATAFRAME) - the frame with missing values.
• int_scalar (required, INT, default 0) - the replacement integer. Unlike the float node, the direct widget here is fine for exact values; you can also drive it from CDA Int Create if you want the value to live in its own node.
• DATAFRAME output - the cleaned copy.

A realistic flow

Pandas Load CSV → Pandas Fill NA Scalar Int → Pandas Sum or Pandas Group By. That's the whole job. If your file mixes integer and float columns, the float counterpart is a better single-node fit since it handles both; the int node is for when you specifically want whole-number values throughout, or when feeding a downstream node that expects ints.

Installing it

It's a standard pack install. ComfyUI Manager: search "Data analysis", install ComfyUI-Data-Analysis, restart, reload the browser tab. Manual route:

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

The real install surface is that requirements file: pandas, matplotlib, seaborn, scipy, scikit-learn, openpyxl, lxml. ComfyUI ships with none of them, so the pack installs them. No GPU, no model downloads - this is CPU data work. Note that since March 2025 the PyTorch-tensor nodes live in the separate ComfyUI-Pt-Wrapper extension.

Gotchas

• One scalar for everything. Different columns with different fill needs mean multiple passes or per-column handling.
• Filling with 0 fabricates data. Fine for exploration, dangerous for anything you'll report on. Know the difference.
• Check first. Run Pandas Is NA on a copy if you want to verify where the gaps were - after fillna, the evidence is gone.