pandas-analysis

name	pandas-analysis
description	Data manipulation and analysis with pandas. Use when cleaning data, performing aggregations, merging datasets, or building analysis pipelines.

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