Data Analysis Skill

Comprehensive data analysis toolkit using Polars - a blazingly fast DataFrame library. This skill provides instructions, reference documentation, and ready-to-use scripts for common data analysis tasks.

Iteration Checkpoints

Step	What to Present	User Input Type
Data Loading	Shape, columns, sample rows	"Is this the right data?"
Data Exploration	Summary stats, data quality issues	"Any columns to focus on?"
Transformation	Before/after comparison	"Does this transformation look correct?"
Analysis	Key findings, charts	"Should I dig deeper into anything?"
Export	Output preview	"Ready to save, or any changes?"

Quick Start

import polars as pl
from polars import col

# Load data
df = pl.read_csv("data.csv")

# Explore
print(df.shape, df.schema)
df.describe()

# Transform and analyze
result = (
    df.filter(col("value") > 0)
    .group_by("category")
    .agg(col("value").sum().alias("total"))
    .sort("total", descending=True)
)

# Export
result.write_csv("output.csv")

When to Use This Skill

• Loading datasets (CSV, JSON, Parquet, Excel, databases)
• Data cleaning, filtering, and transformation
• Aggregations, grouping, and pivot tables
• Statistical analysis and summary statistics
• Time series analysis and resampling
• Joining and merging multiple datasets
• Creating visualizations and charts
• Exporting results to various formats

Skill Contents

Reference Documentation

Detailed API reference and patterns for specific operations: