streamlit-dynamic-filters 0.1.5

Dynamic multiselect filters for Streamlit

Dynamic Multi Select Filters for Streamlit

Custom component to create dynamic multiselect filters in Streamlit. The filters apply to a dataframe and adjust their values based on the user selection (similar to Google Sheets slicers or Only Relevant Values in Tableau).

How to install and use the package:

1. Install the package using pip: pip install streamlit-dynamic-filters

2. Import the DynamicFilters class: from streamlit_dynamic_filters import DynamicFilters

3. Create an instance of the DynamicFilters class and pass the dataframe and the list of fields that will serve as filters:

   dynamic_filters = DynamicFilters(df, filters=['col1', 'col2', 'col3', 'col4'])

4. Display the filters in your app: dynamic_filters.display_filters()

5. Display the filtered dataframe: dynamic_filters.display_df()

Sample usage with sidebar filters:

import streamlit as st
import pandas as pd
from streamlit_dynamic_filters import DynamicFilters

data = {
    'region': ['North America', 'North America', 'Europe', 'Oceania',
               'North America', 'North America', 'Europe', 'Oceania',
               'North America', 'North America', 'Europe', 'Oceania'],
    'country': ['USA', 'Canada', 'UK', 'Australia',
                'USA', 'Canada', 'UK', 'Australia',
                'USA', 'Canada', 'UK', 'Australia'],
    'city': ['New York', 'Toronto', 'London', 'Sydney',
             'New York', 'Toronto', 'London', 'Sydney',
             'New York', 'Toronto', 'London', 'Sydney'],
    'district': ['Manhattan', 'Downtown', 'Westminster', 'CBD',
                 'Brooklyn', 'Midtown', 'Kensington', 'Circular Quay',
                 'Queens', 'Uptown', 'Camden', 'Bondi']
}

df = pd.DataFrame(data)

dynamic_filters = DynamicFilters(df, filters=['region', 'country', 'city', 'district'])

with st.sidebar:
    st.write("Apply filters in any order 👇")

dynamic_filters.display_filters(location='sidebar')

dynamic_filters.display_df()

Demo GIF:

Sample usage with columns:

import streamlit as st
import pandas as pd
from streamlit_dynamic_filters import DynamicFilters

data = {
    'region': ['North America', 'North America', 'Europe', 'Oceania',
               'North America', 'North America', 'Europe', 'Oceania',
               'North America', 'North America', 'Europe', 'Oceania'],
    'country': ['USA', 'Canada', 'UK', 'Australia',
                'USA', 'Canada', 'UK', 'Australia',
                'USA', 'Canada', 'UK', 'Australia'],
    'city': ['New York', 'Toronto', 'London', 'Sydney',
             'New York', 'Toronto', 'London', 'Sydney',
             'New York', 'Toronto', 'London', 'Sydney'],
    'district': ['Manhattan', 'Downtown', 'Westminster', 'CBD',
                 'Brooklyn', 'Midtown', 'Kensington', 'Circular Quay',
                 'Queens', 'Uptown', 'Camden', 'Bondi']
}

df = pd.DataFrame(data)

dynamic_filters = DynamicFilters(df, filters=['region', 'country', 'city', 'district'])

st.write("Apply filters in any order 👇")

dynamic_filters.display_filters(location='columns', num_columns=2, gap='large')

dynamic_filters.display_df()

Demo GIF:

0.1.5 - 28th December 2023

Added by kzielins [e-mail]

• Hierarchical filter selectors
• Independent filters with diffrent sessions name

0.1.3 - 28th September

Added

• Ability to specify filter location in display_filters(). The filters can be either displayed in sidebar, main area or columns.
• Error handling of invalid arguments in display_filters().

Changed

• Renamed filter_except() to filter_df(). The function returns a filtered df.

Deprecated

Removed

Fixed

• The StreamlitApiException that occured when selected values did not exist in the dataset.
• Possibility to have more than one filter