Introduction
Pandas and FireDucks are two libraries that are often mentioned in the context of data analysis and manipulation. Where Pandas has remained a perennial tool within the Python ecosystem, Fire Ducks is perhaps an incoming challenger alternative with a more or less similar API.
This article seeks to compare the execution speed of both of the above libraries, especially for big datasets. It provides guidance on when to use each of them. By discussing execution models, benchmarking results, and real-world cases, we will guide you towards the tool that best suits your project needs.
What are Pandas?
Pandas is an open-source software library for data analysis and manipulation created for Python. It provides evanescent data structures: DataFrame and Series. This allows structuring, filtering, sorting, aggregating, and visualizing information with ease. With its mature ecosystem, it allows functionalities like filtering, grouping, aggregating, and visualizing, realized as the go-to library practically among scientists and analysts. Through its simple interface and general use, the Pandas Library has become the standard for performing data manipulation tasks in Python.
FireDucks
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.
The key difference with FireDucks will be its performance optimization by taking advantage of the modern hardware architecture to afford faster computation times.
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.
Main similarity: The common API
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.
Execution Model Differences
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.
Eager Execution (Pandas)
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.
Lazy Execution (Fire Ducks)
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).
This can achieve performance optimization, especially with large datasets, as it allows Fire Ducks to combine operations and avoid unnecessary computations.
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.
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.
Benchmarking Methodology
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. The following tasks were put to performance testing:
- Filtering: Selecting subsets of data chosen from specific conditions.
- Group By and aggregation: Grouping by some column and calculating summary statistics.
- Sorting: Ordering the dataset based on one or more columns.
- Merging: Combining two or more datasets based on a common key.
Pandas and Fire Ducks, for each of the tasks, were tested on the same hardware and timing measurements were recorded with Python’s time module.
Performance Comparison
The benchmark tests display the considerable difference between both Pandas and Fire Ducks.
| Task | Pandas Time (s) | FireDucks Time (s) | Speedup |
|---|
| Filtering | 3.2 | 1.4 | 2.29x |
| Grouping & Aggregation | 4.5 | 2.0 | 2.25x |
| Sorting | 5.0 | 2.3 | 2.17x |
| Merging | 6.1 | 3.5 | 1.74x |
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The benchmark results show that, at all tasks, Fire Ducks is always faster than Pandas by a range from 1.74x to 2.29x. This makes Fire Ducks favorable for large datasets, especially when performance is critical.
Isn’t that amazing?
Analysis of Results
- Filtering: Fire Ducks does indeed significantly better than filtering large datasets upon which data analysis largely relies.
- Grouping & Aggregation: Fire Ducks just surpasses in the grouping and aggregation tasks, so important for summarizing data.
- Sorting: Fire Ducks is rather strong at sorting large datasets; it proves faster than Pandas.
- Merging: Fire Ducks might be slightly faster but to a lesser extent during merging operations; this is suggested to be due to the complex nature of joining large datasets.
Pros and Cons
FireDucks:
Pros:
- Running time: At its best when working with big datasets.
- Gear to modern hardware: Runs on parallel processing and everything else for efficient operation.
- Pandas API mimicking: User-friendly in making a switch because of its similarity with Pandas.
Cons:
- Less mature: Compared to Pandas, FireDucks is somewhat younger.
- Smaller community: Less available resources, tutorials, and community support than Pandas.
- Limited ecosystem: Much so dedicated developments and third-party libraries don’t yet exist for FireDucks.
Pandas:
Pros:
- Pandas is an established: A time-tested, feature-rich library with one of the best balances in data manipulation features.
- Widespread ecosystem: Engages with a vast array of other libraries and frameworks, hence versatility.
- Quite a solid community: Being older, Pandas has, thus far, developed a large user community along with resources supporting various learning methods.
Cons:
- Slow for larger datasets: Due to eager execution and lower optimization for modern hardware, it works slower on big data with Pandas.
- Memory definitely seems to be a bottleneck: Since, again, in itself, Pandas can eat memory if fed large samples.
You can use either Fire Ducks or Pandas depending on the use case. Here’s a quick view:
- Use FireDucks When:
- Speed is an advantage, especially if you are working on large data sets (millions of rows) e.g. in cases when execution time is necessary.
- You need parallel processing; FireDucks is built on modern hardware and optimized to use multiple cores, which makes it great for computationally intensive needs.
- Use Pandas Whenever:
- When you need to use a mature and feature-rich library; if your data sets are small, but with a whole lot of features, or help from a wider community, then this is a better choice.
- If you require integration with many other tools due to the small yet useful ecosystem it has, therefore you’d integrate into other tools and other libraries.
How-to-use-fire ducks:
Installation
To install FireDucks, you can use pip:
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Next, unlock Fire Ducks with one of these three methods:
1. If interested in IPython or Jupyter Notebook, load the extension as follows:
2. Further, FireDucks provides a pandas-like module (fireducks.pandas) which allows you to import it instead of Pandas, thus enabling you to replace the standard import statement with this:
3. Lastly, if you have a Python script, executing it with the following command will help you conveniently switch the Pandas import statement with that of FireDucks:
4. Done!
Conclusion:
In this comparison of Performance-Pandas vs FireDucks, we have observed that FireDucks outmatches Pandas in the area of speed, especially for larger datasets. FireDucks, then, executes quickly in a modern optimized way, while Pandas enjoys its image as a mature, feature-rich library supported by an extensive community.
We encourage you to try Fire Ducks out for your next major data analysis project and let us know what you think!