# Data Analysis Polars High Performance Processing

> Sub-skill of data-analysis: Polars High-Performance Processing (+5).

- Skill: `vamseeachanta/data-analysis-polars-high-performance-processing` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/data-analysis-polars-high-performance-processing`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/data-analysis-polars-high-performance-processing/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/data-analysis-polars-high-performance-processing

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# Polars High-Performance Processing (+5)

## Polars High-Performance Processing


```python
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.*