Powerful, extensible, and feature-packed frontend toolkit. Build and customize with Sass, utilize prebuilt grid system and components, and bring projects to life with powerful JavaScript plugins.| fireducks-dev.github.io
FireDucks has the same API as in pandas, so you can refer to the pandas documentation and articles to get started, or you can use LLM to output pandas code and run it in FireDucks. This user guide is intended for those who want to learn more about FireDucks, explaining its inner workings and what is useful to know about it. In particular, the following points may seem strange to those who are familiar with pandas. Execution modelFor those who are used to pandas, the difference between the exe...| FireDucks – Docs
There are two mechanisms to accelerate FireDucks. The first is compiler optimization on the IR, intermediate representation, and the second is multithreading on the backend. Compiler Optimization FireDucks uses a runtime compiler mechanism to convert Python programs into an intermediate language before execution. Optimization on the intermediate language means that the Python program is converted to an intermediate language that can be executed faster without changing the meaning of the progr...| FireDucks – Docs
FireDucks provides the same API (class names, method names, and attribute names) as pandas, and aims for compatibility in terms of being able to use it simply by changing import statements. Compatibility Concept We do not aim for compatibility in the following aspects. Complete consistency of class names FireDucks provides a pandas-compatible API in the module fireducks.pandas. The complete class names, including module names, are different from those of pandas. For example, the data frame ty...| FireDucks – Docs
FireDucks has its own API that some pandas do not have. Here are some of them. pandas conversion FireDucks DataFrame/Series has a to_pandas method that allows conversion to pandas data. This is useful, for example, when using an external library that accepts pandas data. Also, fireducks.pandas.from_pandas can be used to convert pandas DataFrames/Series to FireDucks. Explicit intermediate language execution FireDucks provides lazy execution. Lazy execution allows multiple APIs to be executed a...| FireDucks – Docs
The know-how of pandas, such as avoiding loops and apply, is also useful for FireDucks. Here are some tips to improve performance in FireDucks. Avoid loops Looping out data from a DataFrame is slow, so it is better to use the DataFrame API as much as possible (this is also true for pandas). For example, the following loop processes the elements of a Series one by one. s =0for i in range(len(df)): if df["A"][i] >2: s += df["B"][i] Using the API, you can write the following. s = df[df["A"] >2][...| FireDucks – Docs
2024-12-06 Server specification (AWS EC2 m7i.8xlarge): cpu: Intel(R) Xeon(R) Platinum 8488C (32cores) main memory: 128GB source code of the benchmark The following graph compares four data frame libraries (pandas, modin, polars, and fireducks) on 22 different queries included in the benchmark. The vertical axis shows how many times faster compared to pandas on a logarithmic scale, where anything greater than 1 indicates that it is faster than pandas. The Scale Factor, which represents the dat...| FireDucks – Docs
As a data scientist, you want to minimize the complexity and latency of working with large data sets and increase your productivity. FireDucks can help you maximize your value as a data scientist. Time is Money Data preprocessing and analysis work can take up to 500 hours of waiting time each year. You can turn this time into time you can invest in other important tasks or new projects. Speedy hypothesis testing Quick analysis cycles allow you to make decisions efficiently. The speed of busin...| FireDucks – Docs
Automobile Industry In recent years, cars have been equipped with technology that records information such as our driving history and brake strength in real time. This information is called “probe data”, and FireDucks and these data can be used to Weather Prediction: Build a prediction model using wiper and outside temperature data. This will revolutionize the data business in the automotive industry. Marketing: Helps you find the best locations for new store openings based on nearby park...| FireDucks – Docs
What is the data format FireDucks internally uses. FireDucks multithreaded CPU backend uses Apache Arrow. What is the lincense of FireDucks: The 3-Clause BSD License About supported platforms FireDucks is currently available for Linux (manylinux) on the x86_64 architecture. We will support Windows etc. if many requests. About support Is there support for FireDucks? As far as we know, no organization offers paid support for FireDucks. We have a Slack workspace where the FireDucks development t...| FireDucks – Docs
This is Osamu Daido from the FireDucks development team. In today’s developers’ blog, I would like to introduce the import hook feature of FireDucks. This feature enables you to use FireDucks without modifying your existing programs at all. I’ll explain how to use hooks when running Python files on the command line and how to enable hooks in IPython or Jupyter Notebook. What is an import hook? FireDucks behaves in the same way as the original pandas, so it’s easy to get started by sim...| FireDucks – FireDucks
pandas is a library that provides functions to support data analysis in the Python programming language. NEC Research Laboratories has developed a library called FireDucks, a faster version of pandas. Data Preparation The analysis is performed on the data of passenger history of cabs in New York City. The source of the data is as follows: https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page To analyze large data sets, we downloaded and merged the “Yellow Taxi Trip Records” data fr...| fireducks-dev.github.io