Introduction to Pandas
DataFrame operations
Grouping and Aggregation
Joining and Merging
Different Data Types
Advanced Transformations
Writing Data
Alternatives to Pandas
Practice
Assignment
Gotchas & Pitfalls
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8. Alternatives to Pandas
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Suggested Topics
- Why consider alternatives: Pandas limitations (performance, memory, parallelization)
- Industry context: Pandas remains the standard, but alternatives are gaining traction
Polars: The Modern DataFrame Library
- What is Polars: Rust-based DataFrame library with Python API
- Key advantages: 10-100x faster, lazy evaluation, better memory efficiency, parallel by default
- When to use: Large datasets (>1GB), performance-critical pipelines, new projects
- Basic syntax comparison: groupby, filtering, aggregation vs Pandas
- Lazy vs eager evaluation: scan_csv vs read_csv, building query plans, collect()
- Key differences: no index, proper null types, immutable by default, expression syntax
- Converting between Polars and Pandas
DuckDB: SQL on DataFrames
- What is DuckDB: Embedded analytical database (like SQLite) for data analysis
- Key advantages: SQL interface, no server, fast columnar engine, Parquet native
- When to use: SQL analytics, querying Parquet files, joining multiple sources
- Querying DataFrames: using SQL on Pandas DataFrames without conversion
- Querying files directly: CSV and Parquet without loading into memory
- Joining multiple data sources: mixing CSV, Parquet, and DataFrames
- Converting results back to Pandas
Dask: Parallel Pandas for Large Datasets
- What is Dask: Parallel computing library with Pandas-like API
- Key advantages: Handles out-of-memory data, familiar API, parallel execution
- When to use: Datasets larger than RAM, distributed computing
- Basic operations: read_csv with blocksize, lazy evaluation, compute()
- Partitions: how Dask splits data for parallel processing
- Limitations: not all Pandas operations supported, overhead for small datasets
- Working with multiple files: processing sales_*.csv patterns
Comparison and Decision Guide
- Comparison table: speed, memory limits, API style, learning curve, use cases
- Decision framework: when to stick with Pandas vs choosing an alternative
- Conversion patterns: moving data between Pandas, Polars, DuckDB, Dask
- Practical example: same task (filter, group, export) in all four tools
- Installation and setup: pip install commands
- Key takeaway: Learn Pandas first, add alternatives when needed
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