Name: Pandas is a powerful data analysis and manipulation library for Python! NVIDIA just made Pandas 150x faster with zero code changes🔥 All you have to add is just a couple of lines of code: %load_ext cudf.pandas import pandas as pd
Uploaded: 2024-05-16T14:13:28.000Z
Duration: PT19.686S
Channel: Sumanth
Description: Sumanth shorts video about Pandas is a powerful data analysis and manipulation library for Python! NVIDIA just made Pandas 150x faster with zero code changes🔥 All you have to add is just a couple of lines of code: %load_ext cudf.pandas import pandas as pd

Pandas is a powerful data analysis and manipulation library... show more

Sumanth

180,800 次观看 • 2 年前

Makes Pandas 20x Faster using FireDucks... ...by changing JUST... ONE LINE of code. Pandas has a few limitations: - a single-core computation. - creates bulky DataFrames. - always follows an eager execution mode (every op triggers immediate computation), which is why it cannot prepare a smart execution plan that optimizes the entire sequence of operations. FireDucks is a heavily optimized alternative with exactly the same API as Pandas’ that addresses these. There are three ways to use it: 1) Load the extension: %𝐥𝐨𝐚𝐝_𝐞𝐱𝐭 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝐩𝐚𝐧𝐝𝐚𝐬; 𝗶𝗺𝗽𝗼𝗿𝘁 𝗽𝗮𝗻𝗱𝗮𝘀 𝗮𝘀 𝗽𝗱 2) Import FireDucks instead of Pandas: 𝐢𝐦𝐩𝐨𝐫𝐭 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝐩𝐚𝐧𝐝𝐚𝐬 𝐚𝐬 𝐩𝐝 3) If you have a Python script, execute is as follows: 𝗽𝘆𝘁𝗵𝗼𝗻3 -𝗺 𝗳𝗶𝗿𝗲𝗱𝘂𝗰𝗸𝘀.𝗽𝗮𝗻𝗱𝗮𝘀 𝗰𝗼𝗱𝗲.𝗽𝘆 Done! ✅ Check this out👇show more

Akshay 🚀

73,967 次观看 • 1 年前

100x faster alternative to Pandas! (It can even beat... GPU dataframe libraries) While Pandas is the most popular DataFrame library, it has some major limitations: - Pandas only uses a single CPU core. - It often creates bulky DataFrames - Its eager (immediate) execution prevents global optimization Introducing FireDucks a highly optimized, drop-in replacement for Pandas. Just change one line of code and you're good to go: ↳ import fireducks.pandas as pd The video below shows a comparison of FireDucks with cuDF—a GPU DataFrame library. In this case, it's even faster than cuDF. That said, the query in the video has chaining operations and it uses all columns. By manually optimizing and working only with the necessary columns, the run-time decreased to: - Pandas: 14 seconds (down from 48 seconds) - FireDucks: 0.8 seconds (unchanged) [SIMILAR] - cuDF: 0.9 seconds (down from 2.6 seconds) This demonstrates that FireDucks' compiler automatically performs the same optimizations that you would need to explicitly implement in cuDF and Pandas. Most importantly, the optimization does not affect the final result. I've shared a link to the Colab notebook in the next tweet. _____ Find me → Akshay 🚀 ✔️ For more insights and tutorials in AI and Machine Learning.show more

Akshay 🚀

79,631 次观看 • 1 年前