Based on react-awesome-query-builder

Check out live demo !

This component allows users to build complex condition trees that can be used to filter a dataframe or build a query.

Install

pip install streamlit-condition-tree

Features

• Highly configurable
• Fields can be of type:
  • simple (string, number, bool, date/time/datetime, list)
  • structs (will be displayed in selectbox as tree)
• Comparison operators can be:
  • binary (== != < > ..)
  • unary (is empty, is null)
  • 'between' (for numbers, dates, times)
  • complex operators like 'proximity'
• RHS can be:
  • values
  • another fields (of same type)
  • functions (arguments also can be values/fields/funcs)
• LHS can be field or function
• Reordering (drag-n-drop) support for rules and groups of rules
• Export to MongoDb, SQL, JsonLogic, SpEL or ElasticSearch

Basic usage

Filter a dataframe

import pandas as pd
from streamlit_condition_tree import condition_tree, config_from_dataframe

# Initial dataframe
df = pd.DataFrame({
    'First Name': ['Georges', 'Alfred'],
    'Age': [45, 98],
    'Favorite Color': ['Green', 'Red'],
    'Like Tomatoes': [True, False]
})

# Basic field configuration from dataframe
config = config_from_dataframe(df)

# Condition tree
query_string = condition_tree(config)

# Filtered dataframe
df = df.query(query_string)

Build a query

import streamlit as st
from streamlit_condition_tree import condition_tree

# Build a custom configuration
config = {
    'fields': {
        'name': {
            'label': 'Name',
            'type': 'text',
        },
        'qty': {
            'label': 'Age',
            'type': 'number',
            'fieldSettings': {
                'min': 0
            },
        },
        'like_tomatoes': {
            'label': 'Likes tomatoes',
            'type': 'boolean',
        }
    }
}

# Condition tree
return_val = condition_tree(
    config,
    return_type='sql'
)

# Generated SQL
st.write(return_val)

API

Parameters

def condition_tree(
    config: dict,
    return_type: str [Optional],
    tree: dict [Optional],
    min_height: int [Optional],
    placeholder: str [Optional],
    always_show_buttons: bool [Optional],
    key: str [Optional]
)

• config: Python dictionary (mostly used to define the fields) that resembles the JSON counterpart of the React component.

A basic configuration can be built from a DataFrame with config_from_dataframe.
For a more advanced configuration, see the component doc and demo.
Note: Javascript functions (ex: validators) are not yet supported.

• return_type: Format of the returned value :

  • queryString
  • mongodb
  • sql
  • spel
  • elasticSearch
  • jsonLogic

  Default : queryString (can be used to filter a pandas DataFrame using DataFrame.query)

• tree: Input condition tree (see section below)

  Default : None

• min_height: Minimum height of the component frame

  Default : 400

• placeholder: Text displayed when the condition tree is empty

  Default : None

• always_show_buttons: Show buttons (create rule, etc.) even when they are not hovered

  Default: False

• key: Fixed identity if you want to change its arguments over time and not have it be re-created.
  Can also be used to access the generated condition tree (see section below).

  Default : None

Export & import a condition tree

When a key is defined for the component, the condition tree generated is accessible through st.session_state[key] as a dictionary.
It can be loaded as an input tree through the tree parameter.

Potential future improvements

• Javascript support: allow injection of javascript code in the configuration (e.g. validators)