data-analysis

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:

• reference/loading.md - Loading data from all supported formats
• reference/transformations.md - Column operations, filtering, sorting, type casting
• reference/aggregations.md - Group by, window functions, running totals
• reference/time_series.md - Date parsing, resampling, lag features
• reference/statistics.md - Correlations, distributions, hypothesis testing setup
• reference/visualization.md - Creating charts with matplotlib/plotly

Ready-to-Use Scripts

Executable Python scripts for common tasks:

• scripts/explore_data.py - Quick dataset exploration and profiling
• scripts/summary_stats.py - Generate comprehensive statistics report

Core Patterns

Loading Data

# CSV (most common)
df = pl.read_csv("data.csv")

# Lazy loading for large files
df = pl.scan_csv("large.csv").filter(col("x") > 0).collect()

# Parquet (recommended for large datasets)
df = pl.read_parquet("data.parquet")

# JSON
df = pl.read_json("data.json")
df = pl.read_ndjson("data.ndjson")  # Newline-delimited

Filtering and Selection

# Select columns
df.select("col1", "col2")
df.select(col("name"), col("value") * 2)

# Filter rows
df.filter(col("age") > 25)
df.filter((col("status") == "active") & (col("value") > 100))
df.filter(col("name").str.contains("Smith"))

Transformations

# Add/modify columns
df = df.with_columns(
    (col("price") * col("qty")).alias("total"),
    col("date_str").str.to_date("%Y-%m-%d").alias("date"),
)

# Conditional values
df = df.with_columns(
    pl.when(col("score") >= 90).then(pl.lit("A"))
    .when(col("score") >= 80).then(pl.lit("B"))
    .otherwise(pl.lit("C"))
    .alias("grade")
)

Aggregations

# Group by
df.group_by("category").agg(
    col("value").sum().alias("total"),
    col("value").mean().alias("avg"),
    pl.len().alias("count"),
)

# Window functions
df.with_columns(
    col("value").sum().over("group").alias("group_total"),
    col("value").rank().over("group").alias("rank_in_group"),
)

Exporting

df.write_csv("output.csv")
df.write_parquet("output.parquet")
df.write_json("output.json", row_oriented=True)

Best Practices

1. Use lazy evaluation for large datasets: pl.scan_csv() + .collect()
2. Filter early to reduce data volume before expensive operations
3. Select only needed columns to minimize memory usage
4. Prefer Parquet for storage - faster I/O, better compression
5. Use .explain() to understand and optimize query plans