pandas

Getting Started

1. Install the skill using the command above
2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
3. Reference the skill in your prompt
4. The AI will use the skill's capabilities automatically

Example Prompts

• "Analyze the sales data in revenue.csv and identify trends"
• "Create a visualization comparing Q1 vs Q2 performance metrics"

Overview

Pandas is a Python library for loading, cleaning, transforming, and analyzing tabular data. It provides DataFrames for structured data manipulation, supports CSV, Excel, SQL, JSON, and Parquet formats, and offers powerful groupby aggregation, merge/join operations, time series resampling, and method chaining for building analysis pipelines.

Instructions

• When loading data, use pd.read_parquet() for large datasets (faster, smaller, type-preserving), pd.read_csv() with explicit dtype for CSVs, and pd.read_sql() for database queries.
• When cleaning data, handle missing values with fillna() or dropna(), deduplicate with drop_duplicates(), use string methods (.str.strip(), .str.lower()) for text cleaning, and convert types explicitly with astype() and pd.to_datetime().
• When transforming data, use assign() for computed columns, pipe() for method chaining, melt() and pivot_table() for reshaping, and pd.cut()/pd.qcut() for binning.
• When aggregating, use groupby().agg() with named aggregation for readable column names, transform() to broadcast results back to original shape, and resample() for time-based grouping.
• When merging, use pd.merge() with explicit how and validate parameters to catch data quality issues at merge time, and pd.concat() for stacking DataFrames.
• When optimizing performance, use category dtype for low-cardinality strings, vectorized operations over .apply(), and Parquet for storage; for datasets over 10GB, consider Polars or DuckDB.

Examples

Example 1: Clean and analyze a sales dataset

User request: "Load a messy CSV of sales data, clean it, and generate monthly revenue summaries"

Actions:

1. Load with pd.read_csv() specifying dtype and parse_dates for key columns
2. Clean missing values, deduplicate by order ID, and standardize text fields
3. Add computed columns for revenue and profit margin using assign()
4. Group by month with resample("M").agg() for revenue, order count, and average order value

Output: A clean DataFrame with monthly revenue summaries ready for visualization or reporting.

Example 2: Merge and enrich customer data from multiple sources

User request: "Join customer data from CRM, transactions, and support tickets into a single view"

Actions:

1. Load each dataset and standardize key columns (email, customer ID)
2. Merge CRM and transactions with pd.merge(on="customer_id", how="left", validate="one_to_many")
3. Aggregate support tickets per customer and merge counts
4. Export the enriched dataset to Parquet for downstream analysis

Output: A unified customer DataFrame with CRM info, transaction history, and support metrics.

Guidelines

• Use pd.read_parquet() for intermediate and output files since it is faster, smaller, and preserves types.
• Chain transformations with .pipe() for readable and testable code.
• Use named aggregation in .agg() for self-documenting column names.
• Set dtype explicitly on read_csv() for large files since type inference reads the full file twice.
• Use category dtype for columns with fewer than 1000 unique values for significant memory savings.
• Validate merges with validate="one_to_many" to catch data quality issues at merge time.
• Use query() for complex filters instead of chained boolean indexing for better readability.