Agent skill
great-tables-great-tables-with-streamlit
Sub-skill of great-tables: Great Tables with Streamlit (+1).
Install this agent skill to your Project
npx add-skill https://github.com/vamseeachanta/workspace-hub/tree/main/.claude/skills/_archive/data/analysis/great-tables/great-tables-with-streamlit
SKILL.md
Great Tables with Streamlit (+1)
Great Tables with Streamlit
import streamlit as st
from great_tables import GT
import pandas as pd
st.set_page_config(page_title="Table Demo", layout="wide")
st.title("Great Tables in Streamlit")
# Sample data
df = pd.DataFrame({
"Product": ["Widget A", "Widget B", "Gadget X"],
"Price": [29.99, 49.99, 19.99],
"Stock": [150, 85, 200],
"Rating": [4.5, 4.2, 4.8]
})
# Create table
table = (
GT(df)
.tab_header(title="Product Catalog")
.fmt_currency(columns="Price", currency="USD")
.fmt_number(columns="Rating", decimals=1)
)
# Display in Streamlit
st.html(table.as_raw_html())
Great Tables with Polars
from great_tables import GT
import polars as pl
# Create Polars DataFrame
df_polars = pl.DataFrame({
"name": ["Alice", "Bob", "Charlie"],
"score": [95, 87, 92],
"grade": ["A", "B+", "A-"]
})
# Convert to pandas for Great Tables
df_pandas = df_polars.to_pandas()
# Create table
table = (
GT(df_pandas)
.tab_header(title="Student Scores")
.cols_label(
name="Student",
score="Score",
grade="Grade"
)
)
table.save("polars_table.html")
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
gsd-complete-milestone
Archive completed milestone and prepare for next version
gsd-reapply-patches
Reapply local modifications after a GSD update
gsd-verify-work
Validate built features through conversational UAT
gsd-thread
Manage persistent context threads for cross-session work
clinical-trial-protocol
Generate clinical trial protocols for medical devices or drugs through a modular, waypoint-based architecture with research-only and full protocol modes.
single-cell-rna-qc
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations.
Didn't find tool you were looking for?