Displaying Tables in Streamlit

Displaying a Table in Streamlit

Streamlit provides the st.table() function to display static, non-interactive tables. This is useful for presenting small datasets where interactivity is not required.

Basic Example

import streamlit as st
import pandas as pd

# Sample DataFrame
data = pd.DataFrame({
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Age': [25, 30, 35],
    'Score': [85, 90, 78]
})

st.table(data)

Using st.dataframe() for Interactive Tables

If you need a scrollable and interactive table, use st.dataframe() instead:

st.dataframe(data)

This allows users to sort, filter, and scroll through large datasets.

Displaying Tables in Evidence

For structured data visualization and reporting, Evidence provides a SQL-driven approach using its <DataTable/> component. Instead of manually loading DataFrames in Python, Evidence connects directly to a SQL database and renders tables dynamically.

Data Table from SQL Query

Display a table from data returned from a SQL query.

```sql my_query 
SELECT name, age, score 
FROM users
```

<DataTable data={my_query} />

This ensures that tables remain query-driven and dynamically updated, allowing users to explore real-time data without needing Python scripts.

Pick Specific Columns

Choose which columns to display in the table.

```sql my_query 
SELECT name, age, score 
FROM users 
```

<DataTable data={my_query}>
	<Column id=name />
	<Column id=score />
</DataTable>

Conditional Formatting

Apply conditional formatting to a column.

<DataTable data={my_query}>
	<Column id=name />
	<Column id=score colorScale=default />
</DataTable>

Streamlit Tables vs. Evidence Data Tables

For interactive data visualization and reporting, Evidence’s <DataTable/> provides a structured, SQL-native approach that eliminates the need for scripting.

Check out more features in the Evidence documentation.