Dynamic multiselect filters for Streamlit
Project description
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).
Basic documentation is available at https://arsentievalex.github.io/streamlit-dynamic-filters/
How to install and use the package:
-
Install the package using pip:
pip install streamlit-dynamic-filters
-
Import the
DynamicFilters
class:from streamlit_dynamic_filters import DynamicFilters
-
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'])
-
Display the filters in your app:
dynamic_filters.display_filters()
-
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:
0.1.7 - 1st August 2024
Added
- Key for each elements in filters, so that multiple DynamicFilters can be used in one app (contribution by @vikashgraja)
- reset_filters function (contribution by @ragchuck)
0.1.6 - 27th March 2024
Added
- Sorted alphabetically filter labels
0.1.5 - 28th December 2023
Added by kzielins kzislinsk@gmail.com
- Hierarchical filter selectors
- Independent filters with diffrent sessions name
0.1.3 - 28th September 2023
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.
Fixed
- The StreamlitApiException that occured when selected values did not exist in the dataset.
- Possibility to have more than one filter
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