Pandas DataFrame
Category: Mega-Polis → Gathering → Gathering Data Tools
Node ID:SvMegapolisPandasDataframe
Tooltip: Create a Pandas DataFrame
Dependencies:pandas
Functionality
Creates a Pandas DataFrame from incoming structured data inside Sverchok.
The node is designed as a bridge between list-based Sverchok data and table-based workflows using Pandas.
It accepts lists (columns or records), converts them into a DataFrame object, and outputs the resulting table for use in downstream analysis, filtering, and mapping nodes.
Inputs
| Socket | Type | Description |
|---|---|---|
| Data | SvStringsSocket | Input data used to build the DataFrame. Typically a list of values, nested lists, or structured records. |
| Columns | SvStringsSocket | Optional list of column names to assign to the DataFrame. |
Parameters
This node has no exposed UI parameters.
Outputs
| Socket | Type | Description |
|---|---|---|
| DataFrame | SvStringsSocket | A Pandas DataFrame created from the input data and column labels. |
Example
Create a simple table
Input: - Data → [[10, 20, 30], [40, 50, 60]] - Columns → ["A", "B", "C"]
Output DataFrame:
| A | B | C |
|---|---|---|
| 10 | 20 | 30 |
| 40 | 50 | 60 |
Create a single-column DataFrame
Input: - Data → [100, 200, 300] - Columns → ["Height"]
Output:
| Height |
|---|
| 100 |
| 200 |
| 300 |
Typical uses
- Convert lists from:
- CSV
- GIS attributes
- API results
- Prepare data for:
- Filtering nodes
- Grouping and aggregation
- Statistical analysis
- Mapping attributes to geometry
Notes
- If Columns is not provided, Pandas assigns default column names (
0,1,2, …). - Works with:
- Flat lists
- Nested lists
- Structured records
- Output is a full Pandas DataFrame object that can be passed to other Mega-Polis nodes expecting tabular input.