Pandas Gt Scalar Float

Pandas Gt Scalar Float flags every cell strictly greater than the float you set. True when the value beats your threshold, False otherwise, and - here's the strict part - a cell exactly equal to the threshold comes back False. It's the "strictly above" sibling of Pandas Ge Scalar Float, and the only meaningful difference between them is whether the boundary value passes. Ge is inclusive, Gt is exclusive.

That one-character semantic difference is actually the whole reason both nodes exist. "At least 0.5" and "more than 0.5" are different questions, and when you're building a filter you want the one that matches the question, not a shrug. "Show me values over the mean," "flag anything above 0.9" - those are strict, and this is the node. The wiring is the same as every mask in the pack: boolean output into Pandas Boolean Index to filter, or into a show node to inspect.

How it works

dataframe.gt(number), element-wise. The number input is a FLOAT widget with a generous range and a fine step, so fractional thresholds work as you'd expect. Same pandas rules as the rest of the family: NaN compares as neither greater nor anything, coming back False; text columns compare as False against a float. If your frame has mixed columns and you only care about one, slice it with Pandas Select Columns first, or you'll get a mask that's mostly False for reasons unrelated to your threshold.

One float gotcha worth repeating from the Eq article: on computed values, "strictly greater than" is fine, but if your plan is "flag cells above 0.1" where 0.1 was itself computed, floating-point representation means some cells that display as 0.1000001 might trip it while "0.1" might not. It's rarely a problem for real thresholds, but don't be confused when near-boundary values behave oddly.

Inputs and outputs

• dataframe (required, DATAFRAME) - the frame to test.
• number (required, FLOAT, default 0) - the exclusive threshold.
• DATAFRAME output - boolean mask, True where a cell is strictly above the threshold.

Installing it

One pack install covers it. 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

Requirements: pandas, matplotlib, seaborn, scipy, scikit-learn, openpyxl, lxml. No GPU, no model files. If the whole pack fails to load, the culprit is pandas/matplotlib missing from your ComfyUI environment - the requirements install is the step people skip, and every pandas node pays for it.