Displaying Data Frames in Streamlit

Key Concepts

Displaying data frames in Streamlit involves using functions that allow you to visualize tabular data directly within your web application. The primary functions for this purpose are st.dataframe() and st.table(). Understanding these functions and their use cases is crucial for effectively presenting data in your Streamlit apps.

1. st.dataframe()

st.dataframe() is used to display a dynamic, interactive table. This function is particularly useful when you want users to be able to scroll through large datasets or sort and filter data within the table. It leverages Pandas DataFrames, making it easy to integrate with data processing in Python.

Example:

import streamlit as st
import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}

df = pd.DataFrame(data)
st.dataframe(df)
    

In this example, a Pandas DataFrame is created and displayed using st.dataframe(). The resulting table is interactive, allowing users to scroll, sort, and filter the data.

2. st.table()

st.table() is used to display a static table. Unlike st.dataframe(), this function renders a non-interactive table, which is useful when you want to present a snapshot of data without any dynamic features. It is ideal for small datasets or when the data does not need to be manipulated by the user.

Example:

import streamlit as st
import pandas as pd

data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "Los Angeles", "Chicago"]
}

df = pd.DataFrame(data)
st.table(df)
    

In this example, the same DataFrame is displayed using st.table(). The resulting table is static and does not allow for scrolling, sorting, or filtering.

Analogies

Think of st.dataframe() as a dynamic spreadsheet that users can interact with, similar to Google Sheets. They can scroll through rows and columns, sort data by clicking on headers, and even filter data based on specific criteria. On the other hand, st.table() is like a printed table in a book. It presents the data in a clear, structured format but does not allow for any user interaction.

Conclusion

By understanding and utilizing st.dataframe() and st.table(), you can effectively display tabular data in your Streamlit applications. Whether you need a dynamic, interactive table or a static snapshot of data, these functions provide the flexibility to meet your specific needs.