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Sub-skill of data-analysis: Polars High-Performance Processing (+5).
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
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。
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npx skills add https://github.com/vamseeachanta/workspace-hub --skill data-analysis-polars-high-performance-processing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.
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基于 SOC 职业分类
| name | data-analysis-polars-high-performance-processing |
| description | Sub-skill of data-analysis: Polars High-Performance Processing (+5). |
| version | 1.0.0 |
| category | data |
| type | reference |
| scripts_exempt | true |
import polars as pl
# Read large CSV with lazy evaluation
df = pl.scan_csv("large_data.csv")
# Chain operations efficiently
result = (
df
.filter(pl.col("date") >= "2025-01-01")
*See sub-skills for full details.*
## Streamlit Data App
```python
import streamlit as st
import polars as pl
import plotly.express as px
st.set_page_config(page_title="Sales Dashboard", layout="wide")
st.title("Sales Analytics Dashboard")
# Sidebar filters
st.sidebar.header("Filters")
*See sub-skills for full details.*
## Dash Production Dashboard
```python
from dash import Dash, html, dcc, callback, Output, Input
import plotly.express as px
import polars as pl
app = Dash(__name__)
# Layout
app.layout = html.Div([
html.H1("Analytics Dashboard"),
*See sub-skills for full details.*
## YData Profiling Report
```python
from ydata_profiling import ProfileReport
import polars as pl
# Load data
df = pl.read_csv("dataset.csv").to_pandas()
# Generate comprehensive report
profile = ProfileReport(
df,
*See sub-skills for full details.*
## Great Tables Publication Output
```python
from great_tables import GT, md, html
import polars as pl
# Prepare summary data
summary = (
pl.read_parquet("sales.parquet")
.group_by("product_category")
.agg([
pl.col("revenue").sum().alias("total_revenue"),
*See sub-skills for full details.*
## Sweetviz Comparison Report
```python
import sweetviz as sv
import polars as pl
# Load datasets
train_df = pl.read_csv("train.csv").to_pandas()
test_df = pl.read_csv("test.csv").to_pandas()
# Compare train vs test
comparison_report = sv.compare([train_df, "Training"], [test_df, "Test"])
*See sub-skills for full details.*