pandas-pro

Pandas Pro

Overview

Expert pandas API reference providing efficient data manipulation, analysis, and transformation patterns with production-grade performance. Covers DataFrame operations, data cleaning fundamentals, aggregation/groupby, merging/joining, and memory optimization for pandas 2.0+.

Role in the ds plugin: This skill is the canonical pandas API reference for the plugin. It is invoked by /ds:eda for efficient data loading (step 3), structural profiling patterns (step 4), and groupby-based distribution analysis (step 5); by /ds:preprocess for I/O optimization (step 2) and vectorized operation patterns (step 5); by /ds:experiment for feature assembly merge patterns (step 3) and data preparation code scaffolds (step 6); and by /ds:plan for large-dataset handling strategy (step 3). Boundary with data-preprocessing: pandas-pro teaches how to call pandas methods (API syntax, parameters, best practices). data-preprocessing teaches when and how to sequence cleaning operations in a tracked pipeline with error handling and logging. For pipeline-oriented data cleaning (deduplication, imputation, outlier removal, schema validation), use the data-preprocessing skill. Boundary with scikit-learn: For in-model preprocessing inside sklearn Pipelines (scaling, encoding, imputation that participates in cross-validation), use the scikit-learn skill. Boundary with polars: For Polars expression API patterns (lazy evaluation, pl.col() expressions, Arrow-native I/O), use the polars skill. pandas-pro and polars are parallel alternatives -- for large datasets (10M+ rows or >100MB), prefer the polars skill for its lazy evaluation and streaming capabilities. pandas 2.0+ note: Patterns in this skill target pandas 2.0+. On pandas 1.x, nullable types (Int64, string), format='mixed' in pd.to_datetime(), and Arrow-backed types (string[pyarrow]) may not be available.

When to Use This Skill

• Loading, cleaning, and transforming tabular data with pandas API patterns
• Handling missing values and data quality issues at the API level
• Performing groupby aggregations, pivot tables, and crosstab operations
• Merging, joining, and concatenating datasets
• Optimizing pandas code for memory and performance
• Converting between data formats (CSV, Parquet, Excel, JSON)
• Profiling DataFrame structure, dtypes, and memory usage

Core Workflow

1. Assess data structure -- Examine dtypes, memory usage, missing values, data quality
2. Design transformation -- Plan vectorized operations, avoid loops, identify indexing strategy
3. Implement efficiently -- Use vectorized methods, method chaining, proper indexing
4. Validate results -- Check dtypes, shapes, edge cases, null handling
5. Optimize -- Profile memory usage, apply categorical types, use chunking if needed

Reference Guide

Load detailed guidance based on context:

Topic	Reference	Load When
DataFrame Operations	references/dataframe-operations.md	Indexing, selection, filtering, sorting, column operations
Data Cleaning	references/data-cleaning.md	Missing values, type conversion, string cleaning, validation
Aggregation & GroupBy	references/aggregation-groupby.md	GroupBy, pivot tables, crosstab, window functions, transform/apply
Merging & Joining	references/merging-joining.md	Merge, join, concat, combine strategies, anti-joins
Performance Optimization	references/performance-optimization.md	Memory profiling, vectorization, chunking, I/O optimization

Constraints

MUST DO

• Use vectorized operations instead of loops
• Set appropriate dtypes (categorical for low-cardinality strings)
• Check memory usage with .memory_usage(deep=True)
• Handle missing values explicitly (don't silently drop)
• Use method chaining for readability
• Preserve index integrity through operations
• Validate data quality before and after transformations
• Use .copy() when modifying subsets to avoid SettingWithCopyWarning

MUST NOT DO

• Iterate over DataFrame rows with .iterrows() unless absolutely necessary
• Use chained indexing (df['A']['B']) -- use .loc[] or .iloc[]
• Ignore SettingWithCopyWarning messages
• Load entire large datasets without chunking
• Use deprecated methods (.ix, .append() -- use pd.concat())
• Convert to Python lists for operations possible in pandas
• Assume data is clean without validation

Output Patterns

When implementing pandas solutions, provide:

1. Code with vectorized operations and proper indexing
2. Comments explaining complex transformations
3. Memory/performance considerations if dataset is large
4. Data validation checks (dtypes, nulls, shapes)