Get dataframe row-selections from users

Streamlit offers two commands for rendering beautiful, interactive dataframes in your app. If you need users to edit data, add rows, or delete rows, use st.data_editor. If you don't want users to change the data in your dataframe, use st.dataframe. Users can sort and search through data rendered with st.dataframe. Additionally, you can activate selections to work with users' row and column selections.

This tutorial uses row selections, which were introduced in Streamlit version 1.35.0. For an older workaround using st.data_editor, see Get dataframe row-selections (streamlit<1.35.0).

Applied concepts

Use dataframe row selections to filter a dataframe.

Prerequisites

The following must be installed in your Python environment:

streamlit>=1.35.0
You should have a clean working directory called your-repository.
You should have a basic understanding of caching and st.dataframe.

Summary

In this example, you'll build an app that displays a table of members and their activity for an imaginary organization. Within the table, a user can select one or more rows to create a filtered view. Your app will show a combined chart that compares the selected employees.

Here's a look at what you'll build:

Complete codeexpand_more

import numpy as np
import pandas as pd
import streamlit as st

from faker import Faker

@st.cache_data
def get_profile_dataset(number_of_items: int = 20, seed: int = 0) -> pd.DataFrame:
    new_data = []

    fake = Faker()
    np.random.seed(seed)
    Faker.seed(seed)

    for i in range(number_of_items):
        profile = fake.profile()
        new_data.append(
            {
                "name": profile["name"],
                "daily_activity": np.random.rand(25),
                "activity": np.random.randint(2, 90, size=12),
            }
        )

    profile_df = pd.DataFrame(new_data)
    return profile_df


column_configuration = {
    "name": st.column_config.TextColumn(
        "Name", help="The name of the user", max_chars=100, width="medium"
    ),
    "activity": st.column_config.LineChartColumn(
        "Activity (1 year)",
        help="The user's activity over the last 1 year",
        width="large",
        y_min=0,
        y_max=100,
    ),
    "daily_activity": st.column_config.BarChartColumn(
        "Activity (daily)",
        help="The user's activity in the last 25 days",
        width="medium",
        y_min=0,
        y_max=1,
    ),
}

select, compare = st.tabs(["Select members", "Compare selected"])

with select:
    st.header("All members")

    df = get_profile_dataset()

    event = st.dataframe(
        df,
        column_config=column_configuration,
        use_container_width=True,
        hide_index=True,
        on_select="rerun",
        selection_mode="multi-row",
    )

    st.header("Selected members")
    people = event.selection.rows
    filtered_df = df.iloc[people]
    st.dataframe(
        filtered_df,
        column_config=column_configuration,
        use_container_width=True,
    )

with compare:
    activity_df = {}
    for person in people:
        activity_df[df.iloc[person]["name"]] = df.iloc[person]["activity"]
    activity_df = pd.DataFrame(activity_df)

    daily_activity_df = {}
    for person in people:
        daily_activity_df[df.iloc[person]["name"]] = df.iloc[person]["daily_activity"]
    daily_activity_df = pd.DataFrame(daily_activity_df)

    if len(people) > 0:
        st.header("Daily activity comparison")
        st.bar_chart(daily_activity_df)
        st.header("Yearly activity comparison")
        st.line_chart(activity_df)
    else:
        st.markdown("No members selected.")

Built with Streamlit 🎈

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Build the example

Initialize your app

In your_repository, create a file named app.py.
In a terminal, change directories to your_repository and start your app.

streamlit run app.py

Your app will be blank since you still need to add code.
In app.py, write the following:

import numpy as np import pandas as pd import streamlit as st from faker import Faker

You'll be using these libraries as follows:
- You'll generate random member names with faker.
- You'll generate random activity data with numpy.
- You'll manipulate the data with pandas.
Save your app.py file and view your running app.
Click "Always rerun" or hit your "A" key in your running app.

Your running preview will automatically update as you save changes to app.py. Your preview will still be blank. Return to your code.

Build a function to create random member data

To begin with, you'll define a function to randomly generate some member data. It's okay to skip this section if you just want to copy the function.

Complete function to randomly generate member dataexpand_more

@st.cache_data
def get_profile_dataset(number_of_items: int = 20, seed: int = 0) -> pd.DataFrame:
    new_data = []

    fake = Faker()
    np.random.seed(seed)
    Faker.seed(seed)

    for i in range(number_of_items):
        profile = fake.profile()
        new_data.append(
            {
                "name": profile["name"],
                "daily_activity": np.random.rand(25),
                "activity": np.random.randint(2, 90, size=12),
            }
        )

    profile_df = pd.DataFrame(new_data)
    return profile_df

Use an @st.cache_data decorator and start your function definition.

@st.cache_data def get_profile_dataset(number_of_items: int = 20, seed: int = 0) -> pd.DataFrame:

The @st.cache_data decorator turns get_profile_dataset() into a cached function. Streamlit saves the output of a cached function to reuse when the cached function is called again with the same inputs. This keeps your app performant when rerunning as part of Streamlit's execution model. For more information, see Caching.

The get_profile_dataset function has two parameters to configure the size of the data set and the seed for random generation. This example will use the default values (20 members in the set with a seed of 0). The function will return a pandas.DataFrame.
Initialize an empty list to store data.

new_data = []
Initialize the random generators.

fake = Faker() random.seed(seed) Faker.seed(seed)
Iterate through a range to generate new member data as a dictionary and append it to your list.

for i in range(number_of_items): profile = fake.profile() new_data.append( { "name": profile["name"], "daily_activity": np.random.rand(25), "activity": np.random.randint(2, 90, size=12), } )

For daily_activity, you're generating an array of length 25. These values are floats in the interval [0,1). For activity, you're generating an array of length 12. These values are integers in the interval [2,90).
Convert your list of dictionaries to a single pandas.DataFrame and return it.

profile_df = pd.DataFrame(new_data) return profile_df
(Optional) Test out your function by calling it and displaying the data.

st.dataframe(get_profile_dataset())

Save your app.py file to see the preview. Delete this line before you continue.

Display your data with multi-row selections enabled

Define your column configuration to format your data.

column_configuration = { "name": st.column_config.TextColumn( "Name", help="The name of the user", max_chars=100, width="medium" ), "activity": st.column_config.LineChartColumn( "Activity (1 year)", help="The user's activity over the last 1 year", width="large", y_min=0, y_max=100, ), "daily_activity": st.column_config.BarChartColumn( "Activity (daily)", help="The user's activity in the last 25 days", width="medium", y_min=0, y_max=1, ), }

For each column of your dataframe, this defines nicely formatted column name, tooltip, and column width. You'll use a line chart to show yearly activity, and a bar chart to show daily activity.
Insert a header to identify the data you will display.

st.header("All members")
Store your data in a convenient variable.

df = get_profile_dataset()
Display your dataframe with selections activated.

event = st.dataframe( df, column_config=column_configuration, use_container_width=True, hide_index=True, on_select="rerun", selection_mode="multi-row", )

By setting on_selection="rerun", you've activated selections for the dataframe. selection_mode="multi_row" specifies the type of selections allowed (multiple rows, no columns). event stores the selection data from the user. Selections can be accessed from the event.selection attribute.

Display the selected data

Insert a header to identify the subset of data you will display.

st.header("Selected members")
Get the list of selected rows and filter your dataframe.

people = event.selection.rows filtered_df = df.iloc[people]

Row selections are returned by positional index. You should use pandas methods .iloc[] or .iat[] to retrieve rows.
Display the selected rows in a new dataframe.

st.dataframe( filtered_df, column_config=column_configuration, use_container_width=True, )

For consistency, reuse the existing column configuration.
(Optional) Save your file and test it out. Try selecting some rows in your app, and then return to your code.

Combine activity data for the selected rows

Create an empty dictionary to store (yearly) activity data.

activity_df = {}
Iterate through selected rows and save each member's activity in the dictionary indexed by their name.

for person in people: activity_df[df.iloc[person]["name"]] = df.iloc[person]["activity"]
Convert the activity dictionary into a pandas.DataFrame.

activity_df = pd.DataFrame(activity_df)
Repeat the previous three steps similarly for daily activity.

daily_activity_df = {} for person in people: daily_activity_df[df.iloc[person]["name"]] = df.iloc[person]["daily_activity"] daily_activity_df = pd.DataFrame(daily_activity_df)
(Optional) Test out your combined data by displaying it.

st.dataframe(activity_df) st.dataframe(daily_activity_df)

Save your app.py file to see the preview. Delete these two lines before you continue.

Use charts to visualize the activity comparison

Start a conditional block to check if anyone is currently selected.

if len(people) > 0:

Since no members are selected when the app loads, this check will prevent empty charts from being displayed.
Add a header to identify your first chart.

st.header("Daily activity comparison")
Show the daily activity comparison in a bar chart.

st.bar_chart(daily_activity_df)
Similarly, for yearly activity, add a header and line chart.

st.header("Yearly activity comparison") st.line_chart(activity_df)
Complete the conditional block with a default message to show when no members are selected.

else: st.markdown("No members selected.")

Make it pretty

You should have a functioning app at this point. Now you can beautify it. In this section, you'll separate the selection UI from the comparison by using st.tabs.

Immediately after the column configuration definition, insert two tabs.

select, compare = st.tabs(["Select members", "Compare selected"])
Indent the code following the line in the previous step and group it into the two new tabs.

with select: # Add select tab ############################################# st.header("All members") df = get_profile_dataset() event = st.dataframe( df, column_config=column_configuration, use_container_width=True, hide_index=True, on_select="rerun", selection_mode="multi-row", ) st.header("Selected members") people = event.selection.rows filtered_df = df.iloc[people] st.dataframe( filtered_df, column_config=column_configuration, use_container_width=True, ) with compare: # Add compare tab ########################################### activity_df = {} for person in people: activity_df[df.iloc[person]["name"]] = df.iloc[person]["activity"] activity_df = pd.DataFrame(activity_df) daily_activity_df = {} for person in people: daily_activity_df[df.iloc[person]["name"]] = df.iloc[person]["daily_activity"] daily_activity_df = pd.DataFrame(daily_activity_df) if len(people) > 0: st.header("Daily activity comparison") st.bar_chart(daily_activity_df) st.header("Yearly activity comparison") st.line_chart(activity_df) else: st.markdown("No members selected.")
Save your file and try out your completed example.

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