vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/vamseeachanta/workspace-hub --skill autoviz-autoviz-with-streamlit命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Write outbound email and external messages in Vamsee Achanta's voice — a subtle offer to help, never bold or rash claims. Load before drafting ANY email, LinkedIn/Collide reply, proposal note, or outreach sent under his name.
Save/publish analysis or computation results from ANY ecosystem repo to Hugging Face as a queryable, viewer-renderable dataset. Use when the user wants to "save results to hugging face", "publish dataset to HF", "hugging face data saving", "save analysis results", "hf dataset", "make results queryable", or "render via datasets-server API". Reshapes nested results into flat parquet tables, writes a dataset card with a viewer `configs:` block and provenance, applies license/public-vs-private routing, enforces a domain data-quality gate (faithful-to-source != correct), publishes to `aceengineer/<repo>-<projection>`, and verifies via the datasets-server API.
Clone, create, fork, configure, and manage GitHub repositories. Manage remotes, secrets, releases, and workflows. Works with gh CLI or falls back to git + GitHub REST API via curl.
正在显示 SKILL.md
基于 SOC 职业分类
| name | autoviz-autoviz-with-streamlit |
| description | Sub-skill of autoviz: AutoViz with Streamlit (+1). |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
import streamlit as st
from autoviz import AutoViz_Class
import pandas as pd
import os
import tempfile
st.set_page_config(page_title="AutoViz EDA Tool", layout="wide")
st.title("AutoViz Exploratory Data Analysis")
# File upload
uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])
if uploaded_file is not None:
df = pd.read_csv(uploaded_file)
st.subheader("Data Preview")
st.dataframe(df.head(100))
col1, col2 = st.columns(2)
with col1:
st.metric("Rows", f"{len(df):,}")
with col2:
st.metric("Columns", len(df.columns))
# Target variable selection
target = st.selectbox(
"Select target variable (optional)",
["None"] + list(df.columns)
)
if st.button("Run AutoViz Analysis"):
with st.spinner("Generating visualizations..."):
# Create temp directory for outputs
with tempfile.TemporaryDirectory() as tmpdir:
AV = AutoViz_Class()
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="" if target == "None" else target,
chart_format="png",
save_plot_dir=tmpdir,
verbose=0
)
# Display generated charts
st.subheader("Generated Visualizations")
for file in os.listdir(tmpdir):
if file.endswith(".png"):
st.image(os.path.join(tmpdir, file))
st.success("Analysis complete!")
from autoviz import AutoViz_Class
import polars as pl
import pandas as pd
def autoviz_polars(lf: pl.LazyFrame, target: str = "", **kwargs) -> pd.DataFrame:
"""
Run AutoViz on Polars LazyFrame.
Args:
lf: Polars LazyFrame
target: Target variable name
**kwargs: Additional AutoViz parameters
Returns:
Analyzed DataFrame
"""
# Collect LazyFrame to DataFrame, then convert to pandas
df_polars = lf.collect()
df_pandas = df_polars.to_pandas()
AV = AutoViz_Class()
return AV.AutoViz(
filename="",
dfte=df_pandas,
depVar=target,
**kwargs
)
# Usage
# lf = pl.scan_csv("data.csv")
# df_analyzed = autoviz_polars(lf, target="revenue", chart_format="png")