Pandas Analysis

Pandas Analysis

Activate this skill when working with tabular data using pandas.

When to Use

• Cleaning and preprocessing datasets
• Performing groupby aggregations
• Merging and joining multiple DataFrames
• Time series manipulation
• Exploratory data analysis

Patterns

import pandas as pd

# Method chaining for clean pipelines
result = (
    df.pipe(clean_column_names)
    .query("revenue > 0")
    .assign(margin=lambda x: x.revenue - x.cost)
    .groupby("category")
    .agg(total_margin=("margin", "sum"), count=("margin", "size"))
    .sort_values("total_margin", ascending=False)
)

Performance Tips

• Use category dtype for low-cardinality strings
• Prefer vectorized operations over apply()
• Use read_csv(usecols=...) to load only needed columns
• Consider pyarrow backend for large datasets
• Use query() over boolean indexing for readability

Rules

• Always inspect data shape and dtypes first
• Handle missing values explicitly (never ignore NaN)
• Validate assumptions about uniqueness and cardinality
• Use .copy() to avoid SettingWithCopyWarning
• Document data transformations with comments

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ihatesea69
• Source: ihatesea69/kiro-kit
• License: MIT
• Homepage: https://www.npmjs.com/package/kiro-kit

Install and usage instructions live in the source repository linked above.