DuckDB is a columnar SQL database optimized for analytical queries on large datasets, while Pandas is an in-memory DataFrame library best for data manipulation and exploration. DuckDB excels at querying gigabytes of data efficiently; Pandas dominates for interactive data wrangling and statistical operations on datasets under 10GB.
DuckDB vs Pandas — Side-by-side comparison on A Versus B
In-process columnar SQL database for analytical queries and OLAP workloads.
Python library for data manipulation, cleaning, and exploratory analysis on in-memory DataFrames.
Memory Model. Query Performance (1GB CSV). Max Dataset Size (practical). SQL Support. Ease of Learning. Ecosystem Integration. Statistical Functions Built-in
Choose DuckDB if you need to query large datasets (>10GB) efficiently with SQL, perform aggregations at scale, or want fast analytical workloads without distributed infrastructure. Choose Pandas if you're doing exploratory data analysis, statistical modeling, machine learning preprocessing, working with smaller datasets, or prefer a mature ecosystem with extensive third-party library support.
DuckDB: 1 (single machine) | Pandas: N/A
DuckDB: 10-50 million rows/sec | Pandas: N/A
DuckDB: Partial (batch insert-optimized) | Pandas: N/A
DuckDB: Single-threaded writes only | Pandas: N/A
DuckDB: 5 years (since 2019) | Pandas: N/A
DuckDB: Parquet, CSV, JSON, Iceberg, Hugging Face | Pandas: N/A
Query Latency (100M rows, simple aggregation)
DuckDB: Python, SQL, C++, R, Julia, Node.js | Pandas: N/A
DuckDB: 2-5 (local install) | Pandas: N/A
DuckDB: No (single machine) | Pandas: N/A
DuckDB: C++ (Rust bindings available) | Pandas: N/A
DuckDB: 5 (Python, R, Java, Node.js, Go) | Pandas: N/A
Total Cost of Ownership (Annual, 100TB dataset)
Query Latency (10GB dataset, simple aggregate)
Query Latency (1TB dataset, complex join)
DuckDB: No (requires external integration) | Pandas: N/A
DuckDB: Batch-focused only | Pandas: N/A
DuckDB: Limited by single machine | Pandas: N/A
DuckDB: Python, R, Java, C++, Node.js, Go | Pandas: N/A
Annual Infrastructure Cost (1TB dataset)
Query Performance on 10GB Parquet File (GROUP BY aggregation)
DuckDB: 1.2 seconds | Pandas: 18-25 seconds (load + compute)
DuckDB: 2.1 GB (with compression) | Pandas: 10.0 GB (full load)
DuckDB: 45ms (lightweight binary) | Pandas: 120-200ms (library import)
Number of Built-in Data Transformation Methods
DuckDB: 65 SQL functions + standard | Pandas: 150+ DataFrame methods
DuckDB: 8.2K questions | Pandas: 425K+ questions
Maximum Dataset Size (without disk streaming)
DuckDB: 1000+ GB (out-of-core) | Pandas: 4-8 GB (system dependent)
DuckDB: Yes (ROW_NUMBER, LAG, LEAD, RANK, etc.) | Pandas: No (requires pandas.groupby().shift())
DuckDB: 3.8 seconds | Pandas: 2.1 seconds (Python familiarity advantage)
DuckDB: Unlimited (disk-dependent) | Pandas: N/A
DuckDB: 7 (Python, Node.js, Go, Rust, R, Java, C++) | Pandas: N/A
DuckDB: No (embedded library) | Pandas: N/A
DuckDB: No (single-node only) | Pandas: N/A
DuckDB: 1.2 seconds | Pandas: 12 seconds
DuckDB: 200 MB (compressed) | Pandas: 1200 MB (uncompressed)
DuckDB: Full ANSI SQL + extensions | Pandas: None (Python only)
DuckDB: 7 (requires SQL) | Pandas: 3 (Python-native)
DuckDB vs Pandas — Structured Comparison Data
Machine-readable comparison dataset for DuckDB vs Pandas. Contains 85 attribute comparisons, 5 FAQ pairs.
DuckDB vs Pandas — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for DuckDB vs Pandas.
For analytical queries on large datasets, yes—DuckDB is 10-100x faster. However, Pandas remains superior for exploratory data analysis, statistical modeling, and machine learning pipelines where you need the rich Python ecosystem. Many teams use both: DuckDB for data querying/ETL, Pandas for feature engineering.
Yes, significantly. DuckDB processes 1GB of CSV data in ~1.2 seconds with aggregations, while Pandas takes ~12 seconds. The gap widens with larger datasets. DuckDB achieves this through columnar storage and compression, while Pandas loads everything into RAM row-wise.
Yes. DuckDB has a Python API and integrates seamlessly—you can query CSV/Parquet files directly from Python, even export results to Pandas DataFrames if needed. This makes DuckDB a drop-in performance upgrade for data loading workflows.
Pandas is better if your dataset fits in RAM (<10GB), you need statistical/ML preprocessing, work in Jupyter notebooks, or want access to scikit-learn/statsmodels. Pandas has a gentler learning curve and 20,000+ answered questions online vs 3,200 for DuckDB.
DuckDB natively supports CSV, Parquet, JSON, and databases. Pandas supports CSV, Excel, JSON, SQL databases, and HDF5. DuckDB is faster on Parquet; Pandas is more flexible with edge-case formats. DuckDB's Parquet performance is ~5x faster than Pandas.
Memory Model: DuckDB has Columnar, out-of-core processing, while Pandas has Row-based, in-memory only.
Query Performance (1GB CSV): DuckDB has ~0.5-2 seconds, while Pandas has ~5-15 seconds.
Max Dataset Size (practical): DuckDB has 100GB+ (disk-based), while Pandas has 10GB (RAM-limited).
SQL Support: DuckDB has Full ANSI SQL with extensions, while Pandas has No native SQL.
Ease of Learning: DuckDB has Requires SQL knowledge, while Pandas has Python-first, intuitive API.
Ecosystem Integration: DuckDB has Limited (newer tool), while Pandas has Extensive (10+ years, NumPy/SciPy stack).
Consider: Statistical Functions Built-in
Statistical Functions Built-in: DuckDB has 200+ aggregate functions, while Pandas has 2000+ statistical methods.