Polars Crushes Pandas on Large-Scale Data.
Polars Crushes Pandas on Large-Scale Data.
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Polars Crushes Pandas on Large-Scale Data.
Polars vs. Pandas: 4x Faster on 90M Rows with 73% Less Memory.
In the world of data engineering and analytics, handling massive datasets efficiently is a constant challenge. Many data professionals rely on Pandas for its intuitive API, but when data scales beyond memory limits, performance bottlenecks become apparent. Enter Polars—a Rust-based DataFrame library designed for speed and efficiency. Here’s a direct comparison of Pandas and Polars using a 90-million-row COVID-19 dataset:
- Dataset: Synthetic COVID-19 time-series data (90M rows, 4 columns).
- Operations:
- Read Parquet files.
- Compute case fatality rates.
- Extract years from dates.
- Group by country and year for aggregations.
We run in local machine it with 8 Core(s), 16 Logical Processor(s)
- Metrics: Memory usage and runtime.
## 📊 Key Results
Polars was 4x faster and used 73% less memory!
## ⚡ Why Polars Dominates with Large Data\
For datasets exceeding memory limits or requiring complex aggregations, Polars is a game-changer.
Its architecture is built for scale, As data grows, adopting Polars can save costs, time, and infrastructure resources.
1. Core Execution Model: Eager vs. Lazy
This is the most significant architectural difference.
*Pandas (Eager Execution):** Executes operations immediately and line-by-line. Each command (e.g., df['new_col'] = df['a'] + df['b']) creates a temporary intermediate result in memory. For a complex chain of operations, this leads to massive memory overhead as multiple copies of your data can exist at once.
*Polars (Lazy Execution):** Instead of executing immediately, it builds a query plan. When you call .collect(), the optimizer:
*Prunes unnecessary columns** (Projection Pushdown).
*Filters data as early as possible** (Predicate Pushdown).
*Combines operations** to minimize overhead.
*Executes the entire optimized plan in a single pass.**
*Result:** Drastically reduced memory footprint and the ability to process datasets larger than your available RAM.
2. Native Multithreading & Concurrency
*Pandas:** Is largely single-threaded due to the Python Global Interpreter Lock (GIL). While some operations (like read_parquet) can use multiple cores, most transformations and aggregations run on a single CPU core, leaving your hardware underutilized.
*Polars:** Written in Rust, it is free from the GIL. Its algorithms are designed from the ground up to be parallel. Group-bys, joins, and aggregations are automatically distributed across all available CPU cores, leading to a massive speedup.
3. Memory Efficiency with Apache Arrow
*Pandas:** Uses its own internal data structures, which can be inefficient. For example, using object dtype for strings has significant overhead.
*Polars:** Uses Apache Arrow as its foundational memory format.
*Columnar Layout:** Data is stored by column (all values of new_cases together), not by row. This is incredibly efficient for analytical queries that need to scan and aggregate specific columns.
*Zero-Copy Reads:** Reading from Arrow-based formats like Parquet and IPC is extremely fast because the data doesn't need to be deserialized into a different format—it's already in Arrow.
*Compact Memory:** Arrow provides a very efficient, compressed in-memory representation, reducing the overall memory footprint compared to Pandas.
4. No Index Overhead
*Pandas:** The index is a powerful feature but comes with a cost. It adds memory overhead and complexity for operations like reset_index() and set_index(), which can be expensive on large DataFrames.
*Polars:** Has no index. Data is processed based on its physical order. This eliminates the associated memory and computational overhead, simplifying operations and making everything faster.
5. Query Optimization
The lazy evaluator in Polars is a sophisticated query optimizer, similar to those found in modern databases like Spark or DuckDB.
* It can reorder operations for efficiency.
* It can cache intermediate results to avoid recomputation.
* It can identify and eliminate redundant operations.
Pandas has no such optimizer; it does exactly what you tell it to do, in the order you tell it, which is often suboptimal.
6. Language Foundation: Rust vs. Python
*Pandas (Python/Cython):** While core parts are written in Cython for speed, much of the logic still runs in Python, which is an interpreted language with high overhead per operation.
*Polars (Rust):** Is written entirely in Rust, a compiled language that provides fine-grained control over memory without a garbage collector. This results in predictable performance, no garbage collection pauses, and execution speed that is consistently closer to native hardware performance.
Refer to code :
https://github.com/ARBINDA765/polars/blob/main/polars_vs_pandas.ipynb
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