Pandas AI is a Python library that uses generative AI models to enhance pandas capabilities for data analysis and manipulation. It allows users to summarize data frames using natural language, create complex visualizations, manipulate dataframes, and derive business insights. The library is designed to be beginner-friendly, enabling individuals with little technical background to perform complex data analytics tasks.

Pandas AI

PandasAI

Large Language Models have proven quite helpful for managing tabular data, revolutionizing how researchers and businesses utilize tools like Excel and SQL databases. 

PandasAI combines Pandas with LLMs, offering a tool for intuitive data interaction and cleaning, allowing users to command data more naturally. OpenAI's API powers PandasAI, simplifying dataset analysis within notebooks like Kaggle's Churn. 

The blend of LLM's contextual understanding with the structured data analysis makes this integration particularly insightful, paving the way for deeper insights and enhanced efficiency.  

Let’s discover how LLMs and conversational AI can revolutionize data projects:

https://lnkd.in/esd7X224

How PandasAI and LLM Models Transform Financial Data Analysis #pandasai #LLMs #OpenAI #DataAnalytics 

https://lnkd.in/gyhQ9ZpE

Fascinating insight into the potential of Pandas AI for transforming data analysis! 

With generative AI capabilities, Pandas opens up countless opportunities from automated report generation to easily understandable data summaries. 

The comprehensive step-by-step guide ensures accessibility for individuals at all levels, whether they are beginners or experienced analysts! 

#DataAnalysis #AI #PandasAI #Automation #ReportGeneration #datascience 
https://lnkd.in/dP7DfByV

Would you like to chat with your data?

I developed a Streamlit app for csv,excel files using PandasAI, Langchain and gpt-3.5-turbo

#llms #generatieveai #analyzedata#largelanguagemodels #genai#csv#excel#exploredata

Check out the repo:
https://lnkd.in/d_M-eXAh

So, over the Christmas break, I got a bit carried away with PandasAI and its Yahoo Finance connector – went down the rabbit hole of portfolio strategies.

The goal was to explore portfolio strategies, specifically a rotation-based approach to potentially outperform the S&P500.

After a bunch of trial and error, the results blew my mind! 🤯 Did a 10-year backtest, factored in taxes, and let me tell you – it's pretty darn impressive (check out the chart below!).

It got me thinking about the future of AI in finance, especially with GenAI.
This stuff is set to shake up the game, and I’m glad that tools like PandasAI can hopefully be like the pioneers in this adventure.

Just wanted to share this little holiday experiment – who knew diving into numbers during Christmas could be this exciting? 🚀

P.s. I'm gonna write a more detailed blog post about it. If you want to make sure you don't miss it, subscribe the PandasAI newsletter: https://lnkd.in/evsRDK6n

#PandasAI #GenAI #Fintech #Portfolio

LLMs are like Super Mario 🍄 They skills up

As large language models (LLMs) advance rapidly, researchers are pioneering creative ways to extract even more capabilities without extra training. The latest innovation - model merging - allows combining multiple specialized LLMs into one versatile model through techniques like parameter averaging.

A new method called Drop And REscale (DARE) enables efficient merging by pruning redundant fine-tuned parameters. Alibaba researchers set an impressive precedent by merging the distinct skills of WizardLM and WizardMath into a 7B model that outperformed WizardMath on mathematical tests. The hybrid showed 66.3% accuracy on GSM8K - inheriting WizardLM’s language facility and WizardMath’s numerical reasoning.

This demonstrates model merging as a simple yet powerful paradigm for creating multi-skilled LLMs. It also provides clues into how fine-tuning stimulates inherent model strengths.

As LLMs grow massive, merging specialized models is poised to become a dominant trend for efficiently scaling capabilities. The Hugging Face leaderboard already shows blended 7B models outperforming their parents.

Model merging unlocks the next level for LLMs - absorbing distinct skills much like Mario powering up. This could emerge as one of the most impactful advances for efficiently imparting new abilities.

👉 Link to the original paper: https://lnkd.in/dhxyM45z
👉 Link the the HuggingFace leaderboard: https://lnkd.in/dYawxf7M

What do you think? How can we fully leverage model merging capabilities? What types of blended models could be most potent? Keen to know your outlook!

I'm excited to announce the first annual PandasAI Awards celebrating the top contributors to PandasAI!

Our top 3 contributors for 2023 are… Drum roll… 🥁
1. Massimiliano Pronesti
2. Ihor (nautics889)
3. Arslan Saleem

Please join me in thanking Massimiliano, Ihor and Arslan for their valuable contributions this year! PandasAI would not be where it is today without the hard work of our the community.

I also want to give a big thank you to all other contributors for their efforts in making PandasAI better - whether through code, documentation, bug reports, feature requests or community support. You are what makes PandasAI a thriving open source project!

Looking ahead, PandasAI is open source and anyone can contribute! If you're interested in getting involved by fixing bugs, enhancing functions, improving documentation or submitting new ideas, we welcome new contributors of all skill levels.

👉 Get started by checking it out on GitHub repository: https://lnkd.in/ewJwWMHD

What a Year! 🎆

Reflecting on PandasAI's Incredible 2023 Journey
I can hardly believe it's already 2024!

This time last year, I was still exploring new ways to positively impact the world with AI. Flash forward 12 months and so much has changed thanks to the amazing traction of PandasAI.

When we launched PandasAI back in April, I had no idea how enthusiastically the open source community would embrace our vision for transforming data analysis with generative AI. But the response exceeded even my wildest dreams.

I'm sincerely grateful to the incredible PandasAI contributors, users and supporters who believed in us from day one. It's been beyond rewarding to work side-by-side with all of you to shape the future of both data science and generative AI.

Of course, this is only the beginning! As we enter 2024, the PandasAI team is laser focused on accelerating innovation in AI-powered data analytics even further:
✅ We're putting the finishing touches on our own lightning-fast LLMs tailored specifically for data tasks (coming soooon!)
✅ Dramatically improving speed and maintainability to push PandasAI's capabilities to the next level
✅ Expanding beyond descriptive analytics into diagnostic, predictive and prescriptive insights.

And much, much more! I can't wait to see what the next year brings, both for PandasAI and the broader data/AI landscape. Thank you again for being part of this exhilarating ride!

I'd love to hear your thoughts and suggestions in the comments. 2023 was incredible...but I predict 2024 will blow us all away! 🚀

#Pandas #PandasAI #NewYear

PandasAI Integrates Google's Gemini Pro!

🚀 I'm thrilled to announce that the PandasAI team swiftly integrated Google DeepMind's latest multi-modal model, Gemini Pro, into the library! Be among the first to experience its cutting-edge capabilities on PandasAI!

In a head-to-head showdown against GPT-3.5, Gemini Pro showcased impressive performance, trailing just 3% behind in our Panda100 benchmark!

Stay tuned for more exciting developments!

Try it out 👉 https://lnkd.in/ewJwWMHD

Woohoo! 🥳 I'm thrilled to announce the latest release of PandasAI v1.5! This is a major milestone for the project. 🚀

The PandasAI crew has been hard at work listening to users and making big improvements.

✅ 2x Faster and Cheaper - Through optimizing prompts, PandasAI now runs queries 2x faster and with 2x smaller prompts, reducing cost.
✅ New Pipelines - Arslan Saleem introduced pipelines that leverage large language models to power core features like .chat() and synthetic data generation.
✅ Synthetic Data - Arslan Saleem also added a pipeline to easily create synthetic datasets from just a few samples. This can be used for training models or generating dev data.
✅ Refactored Core Features - Milind Lalwani refactored the Smart Data Frame pipeline to streamline the core query process.

There's a bunch more changes too - check the GitHub repo for details: https://lnkd.in/ewJwWMHD

Big shoutout to all the contributors (Massimiliano, Arslan, Milind, Yassin, to name a few) who made this release possible through long hours of heads-down coding. Ya'll are amazing!

For real, I'm pumped about how much faster and cheaper PandasAI got with this release. Go give it a spin and see for yourself! 💪

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