streamlit-merge-tables 1.0.2

Visual table merge tool for Streamlit

streamlit-merge-tables is a Streamlit custom component that allows users to visually define merge (join) logic across multiple tables using an interactive UI.

The component does not merge data directly. Instead, it returns a merge plan (dictionary) describing how tables should be joined, leaving execution fully under developer control.

Features

Visual table merge builder

Chain and pairwise merge modes

Multiple join types: INNER, LEFT, RIGHT, OUTER

Column-level join key selection

Built-in validation

Optional DAG visualization of merge flow

Framework-agnostic merge execution (pandas, SQL, backend APIs)

Installation From PyPI pip install streamlit-merge-tables

From GitHub git clone https://github.com/linhnt-hub/streamlit-merge-tables.git cd streamlit-merge-tables pip install .

Quick Start (5-minute example) import streamlit as st import pandas as pd from streamlit_component import merge_tables

Example data

df_interfaces = pd.DataFrame({ "ifname": ["ge-0/0/0", "ge-0/0/1"], "speed": [1000, 1000], "status": ["up", "down"], })

df_traffic = pd.DataFrame({ "ifname": ["ge-0/0/0"], "bps": [1234], })

tables = [ { "id": "interfaces", "name": "Interfaces", "columns": list(df_interfaces.columns), }, { "id": "traffic", "name": "Traffic", "columns": list(df_traffic.columns), }, ]

merge_plan = merge_tables(tables=tables, dag=True)

st.subheader("Merge plan") st.json(merge_plan)

At this point:

Users configure merge logic in the UI

merge_plan updates automatically

You decide how and when to execute the merge

Tables Schema tables = [ { "id": "interfaces", "name": "Interfaces", "columns": ["ifname", "speed", "status"], } ]

Field Description id Unique internal identifier name Display name in UI columns Column names Merge Plan Output { "mode": "chain", "steps": [ { "leftTableId": "interfaces", "rightTableId": "traffic", "leftKeys": ["ifname"], "rightKeys": ["ifname"], "joinType": "inner" } ] }

Developer Notes

The component never touches your DataFrames

It only emits merge logic

Perfect for:

pandas merges

SQL JOIN builders

Backend-driven pipelines

No-code / low-code tools

Example: Execute Merge with pandas result = pd.merge( df_interfaces, df_traffic, left_on=["ifname"], right_on=["ifname"], how="inner", )

Screenshots & Demo

Add screenshots or GIFs here for GitHub:

Merge UI overview

Join key selection

DAG visualization

Recommended format:

docs/images/merge-ui.png docs/images/merge-dag.gif

License

MIT License