Pandas Ge Scalar Float

Of the whole comparison family in this pack, this is the one you'll reach for most: Pandas Ge Scalar Float marks every cell True when it's greater than or equal to a float you specify, and False otherwise. It's the "threshold" node. If you've ever thought "keep only the rows where the value is at least 0.5," or "flag anything above 60," this is that thought turned into a wire.

The pattern is almost always the same, and it's worth memorizing because it comes up constantly: load your data, run this node with the threshold you care about, feed the boolean result into Pandas Boolean Index to filter the rows. That single pairing replaces what would be a line of pandas in a notebook - "keep score >= 60" - and it's the same mental model as the pack's flagship baseball tutorial, where the whole point is winnowing a big stats table down to the rows that matter.

How it works

dataframe.ge(number), element-wise. The comparison is against a float, so it handles fractional thresholds naturally, and because it's >= it's inclusive - a cell exactly equal to your threshold counts as a match. The number widget accepts the full int64-scale range with a fine step, so thresholds like 3.14159 type in fine.

Pandas quirks to keep in mind: NaN compares as neither greater nor equal, so missing cells come back False - which is usually what you want for a threshold filter, but worth knowing when you see a suspiciously clean result. And this is an element-wise comparison across the whole frame, so text columns get compared too (text vs float is False). If you only care about one column, slice it first with Pandas Select Columns rather than filtering the whole frame and getting confusing output.

Inputs and outputs

• dataframe (required, DATAFRAME) - the frame to test.
• number (required, FLOAT, default 0) - the inclusive threshold.
• DATAFRAME output - boolean mask. Wire to Pandas Boolean Index to filter, or Pandas Show DataFrame to inspect.

Installing it

Installed with the pack: ComfyUI Manager → search "Data analysis" → install ComfyUI-Data-Analysis → restart → reload. Manual:

cd ComfyUI/custom_nodes
git clone https://github.com/HowToSD/ComfyUI-Data-Analysis
mv ComfyUI-Data-Analysis data-analysis
pip install -r data-analysis/requirements.txt

pandas, matplotlib, seaborn, scipy, scikit-learn, openpyxl, lxml in requirements. No GPU, no model downloads. If this node or any sibling shows up red, it's a dependency install problem, not a node problem - pandas doesn't ship with ComfyUI.