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As the data grows and scales, there is a serious need for both performance and memory optimization, and this is where the standard library like pandas falls short. A Python library, Polars, addresses these needs, and not only that, but it also provides a much faster alternative to already given solutions. Polars is the perfect candidate for implementing Data science or data engineering s to leverage more speed for cloud computing services such as the Amazon SageMaker notebook that come with immense computing and memory power.
Pandas is horrible when it comes to data that is scaled beyond what it can optimally handle. And given the dynamic nature of the python language, things are already slow. Although there are optimizations taking place behind the scenes (such as cythonized/vectorized operations), it is not enough, and things will be slow or much worse – they will crash. This is where libraries like polars, pyspark, dark, modin, and others have tried to mitigate this problem and they have been largely successful. Among the better contenders are Polars, a fast-performance DataFrame library built in RUST. It is a super-fast wrapper written in Rust that provides 15 times more performance than any other library because there is no overhead and utilizes all the available core on a machine. It has a lazy evaluation system on top of a powerful expression system that can run lightning-fast, which improves both performance and memory demands.
Polars uses two types of evaluation APIs: EAGER and LAZY API. EAGER evaluation is simply results produced within one go, and lazy when only required. EAGER API consists of operations such as Joins, grouping, etc and LAZY API consists mostly of parallel and optimization operations. Polars uses Apache Arrow Columnar Format which also incidentally reduces the compiler bloat that comes with its RUST implementation.
Every query that is being run is stored in something called a “logical plan” and this plan is reordered and optimized. In the end, when the result is requested, the query work is distributed to different executors in the EAGER API to produce the result of the said query. Therefore, we achieve both optimization and parallelism. Polars also meet other important optimization goals such as SIMD vectorization, COW semantics, bitmask optimization, etc. In general, it has these important properties:
- Lazy or eager execution
Eager evaluation means it is evaluated as soon as it is encountered and lazy when only it is needed, which means less stress on the memory or only doing things when required, which is efficient when considering large datasets.
- SIMD for vectorization
Single Instruction/Multiple Data is the ability of a machine or hardware to perform the same operation on multiple operands/components, therefore achieving parallelism. Hence why NumPy vectorization tends to be faster.
- Built-in RUST
RUST is a performance-oriented programming language which faster memory-efficient architecture with zero/no runtime or garbage collection due to its affine-type system. It makes things twice as fast compared to python(even with cython) since Rust is directly compiled into machine code and there is no need for an interpreter or a virtual machine.
It is simply a process or ability of the CPU to run threads of execution concurrently for maximum utilization of CPU power. These threads are like lightweight processes within a process. It is where multiple processes/threads share a common resource pool of CPU. The processing is so fast that it gives the illusion that the computer is handling multiple requests/threads simultaneously when in fact it is switching between the threads.
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Performance Comparisons of Polars with Pandas
Below is a demo on Polars and Pandas and measuring their performance with some common operations to demonstrate their performance effectiveness when it comes to large datasets. All the operations will be done in 1 million rows.
- Import speeds
As we see, Polar import is 90 times faster than the pandas’ import function! This is a significant improvement when it comes to importing large datasets which can hinder performance and clog memory. We can also lazily load the file for much faster and later lazy evaluations:
- Using apply function
Even with apply function, which is usually considered to be cost-heavy, polars perform better. A similar query can be further made faster by using the expression system provided by Polars:
It supports “ufuncs” or NumPy universal functions, which are faster, with its expression system.
- Using groupby and aggregation
Polars groupby uses the lazy evaluation system that pushes the query into the query engine, optimizes it, and caches the intermediate results. Finally, the query is eagerly evaluated and then the result is produced. This is early lightning fast compared to the very slow pandas groupby aggregation method. Under the hood, the query/statement translate from [df.query([‘col1’]).agg([pl.all().is_nan()).sum()] to [df.lazy().groupby([‘col1’]).agg(pl.all().is_nan()).collect()]
The benchmark provided here is, polars performs better than any other libraries for data science/data engineering tasks. When considering processing on a large scale, the use of pandas should be discouraged as it can lead to severe performance and memory usage. Due to RUST and SIMD/parallelism implementation, Polars is an excellent choice.
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Drop a query if you have any questions regarding Polars, Pandas, and I will get back to you quickly.
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1. What is the difference between Pandas DataFrames and Spark DataFrame?
ANS: – Pandas DataFrames and Spark DataFrames are similar in terms of their visual representation as tables, but they are different in how they are stored, implemented and the methods they support. Pandas DataFrames are faster than Spark DataFrames due to parallelization: one runs on single machines and the other runs on multiple nodes/machines (or a cluster).
2. What is the language support for Spark?
ANS: – Spark can be used or leveraged with python, Scale, Java, and R. Compared to Pandas library which is strictly made for python. This also affects the learning rate if you are only accustomed to one programming language.
3. When should we use Spark?
ANS: – Ideally, if you have a large dataset with many features and it needs to be processed, Spark would be the right choice. If the dataset is smaller, there can be additional overhead, which can be slower to process, and in that case, one should use pandas. It should be noted that Spark is majorly used for processing structured and semi-structured dataset.
WRITTEN BY Mohmmad Shahnawaz Ahangar
Shahnawaz is a Research Associate at CloudThat. He is certified as a Microsoft Azure Administrator. He has experience working on Data Analytics, Machine Learning, and AI project migrations on the cloud for clients from various industry domains. He is interested to learn new technologies and write blogs on advanced tech topics.
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