Condition Tree Builder for Streamlit
Project description
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)
Project details
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