Overview of Pandas <\/h2>\n\n\n\n
Pandas<\/strong> is an open-source software library for Pandas<\/strong> data analysis and manipulation created for Python. Pandas<\/strong> provides essential data structures: DataFrame and Series. This allows structuring, filtering, sorting, aggregating, and visualizing information with ease. With its mature ecosystem, Pandas<\/strong> offers functionalities like filtering, grouping, aggregating, and visualizing, making it the go-to library for scientists and analysts. Through its simple interface and general use, Pandas<\/strong> has become the standard for performing data manipulation tasks in Python.<\/p>\n\n\n\n Fire Ducks is a new Python library intended as an alternative to Pandas. It has a similar API to Pandas, so all users familiar with Pandas can easily adopt FireDucks without requiring a major learning curve. <\/p>\n\n\n\n The key difference with FireDucks will be its performance optimization by taking advantage of the modern hardware architecture to afford faster computation times. <\/p>\n\n\n\n It does this via lazy execution, along with being designed for parallel processing, which makes it beneficial especially with large datasets. It is still growing in popularity but had smaller community support and fewer tools than Pandas.<\/p>\n\n\n\n One thing that characterizes Fire Ducks is that its API closely resembles that of Pandas. This means that as soon as you get comfortable with Pandas, switching to Fire Ducks for the performance gains becomes pretty easy-a change that doesn’t require you to rewrite your existing code completely.<\/p>\n\n\n\n The execution model of a library addresses when and how its operations will be calculated. There is a wide dissimilarity between the execution models of both Pandas and Fire Ducks.<\/p>\n\n\n\n Pandas, by principle, operates on an eager execution, meaning that immediately when an operation is called, the operation is produced. For instance, once the data has been filtered and sorted through a series of chaining, it will immediately change the output space, namely DataFrame or Series. Although it provides immediate feedback and is easy to use, it may have performance issues and slow it down when large datasets are involved, especially when chaining several operations.<\/p>\n\n\n\n On the contrary, FireDucks follows a lazy execution model whereby computation is deferred until absolutely necessary. Instead of performing an operation immediately, FireDucks builds up a series of transformation steps, which are actually carried out only once requested for the output by you (by calling .compute(), for example). <\/p>\n\n\n\n This can achieve performance optimization, especially with large datasets, as it allows Fire Ducks to combine operations and avoid unnecessary computations.<\/p>\n\n\n\n To a large extent, this translates into a performance difference: Pandas performs an eager execution and may be slower when a set of operations has to be applied to large datasets.<\/p>\n\n\n\n On the other hand, Fire Ducks, through lazy execution, results in an optimized approach to incorporate different functions, especially in larger and complex data manipulation exercises.<\/p>\n\n\n\n To fully understand the performance differences between Pandas and FireDucks, we carried out benchmarking tests using a large dataset. The dataset that we adopted for the tests had 1 million rows of numerical data and 500,000 rows of categorical data.<\/strong> The following tasks were put to performance testing:<\/p>\n\n\n\n Pandas and Fire Ducks, for each of the tasks, were tested on the same hardware and timing measurements were recorded with Python\u2019s time module.<\/p>\n\n\n\n The benchmark tests display the considerable difference between both Pandas and Fire Ducks.<\/p>\n\n\n\n \/<\/p>\n\n\n\n The benchmark results show that, at all tasks, Fire Ducks is always faster than Pandas<\/strong> by a range from 1.74x to 2.29x. This makes Fire Ducks favorable for large datasets, especially when performance is critical.<\/p>\n\n\n\n Isn\u2019t that amazing?<\/p>\n\n\n\nFireDucks<\/h2>\n\n\n\n
Main similarity between Pandas vs. Fire Duck: The common API<\/h2>\n\n\n\n
Execution Model Differences<\/h2>\n\n\n\n
Eager Execution (Pandas)<\/h2>\n\n\n\n
Lazy Execution (Fire Ducks)<\/h2>\n\n\n\n
Benchmarking Methodology<\/h2>\n\n\n\n
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Performance Comparison of Pandas vs. Fire Duck<\/h2>\n\n\n\n
Task<\/th> Pandas Time (s)<\/th> FireDucks Time (s)<\/th> Speedup<\/th><\/tr><\/thead><\/table><\/figure>\n\n\n\n Filtering<\/strong><\/td> 3.2<\/td> 1.4<\/td> 2.29x<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Grouping & Aggregation<\/strong><\/td> 4.5<\/td> 2.0<\/td> 2.25x<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Sorting<\/strong><\/td> 5.0<\/td> 2.3<\/td> 2.17x<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Merging<\/strong><\/td> 6.1<\/td> 3.5<\/td> 1.74x<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n ![\"\"](\"https:\/\/geeksgrow.com\/blog\/wp-content\/uploads\/2024\/12\/025888-1024x848.jpg\")
<\/figure>\n\n\n\nAnalysis of Pandas vs. Fire Duck Results<\/h2>\n\n\n\n
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Pros and Cons<\/h2>\n\n\n\n
FireDucks:<\/h2>\n\n\n\n
Pros:<\/h2>\n\n\n\n
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Cons:<\/h2>\n\n\n\n
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Pandas:<\/h2>\n\n\n\n
Pros:<\/h2>\n\n\n\n
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Cons:<\/h2>\n\n\n\n
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You can use either Fire Ducks or Pandas depending on the use case. Here’s a quick view:<\/h2>\n\n\n\n
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How-to-use-fire ducks:<\/h2>\n\n\n\n
Installation<\/h3>\n\n\n\n
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