PandasAI Howto

WebsiteFreemiumAI Code AssistantLarge Language Models (LLMs)
PandasAI is an open-source Python library that integrates generative AI capabilities into pandas, enabling conversational data analysis and insights generation through natural language queries.
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Profile of PandasAI
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What is PandasAI
Features of PandasAI & Use Cases

How to Use PandasAI

Install PandasAI: Install PandasAI using pip: pip install pandasai
Import required libraries: Import pandas, PandasAI, and the OpenAI LLM: import pandas as pd; from pandasai import PandasAI; from pandasai.llm.openai import OpenAI
Set up OpenAI API key: Set up your OpenAI API key: OPENAI_API_KEY = 'your-api-key-here'
Initialize the LLM: Initialize the OpenAI LLM: llm = OpenAI(api_token=OPENAI_API_KEY)
Create PandasAI instance: Create a PandasAI instance with the LLM: pandas_ai = PandasAI(llm)
Load your data: Load your data into a pandas DataFrame: df = pd.read_csv('your_data.csv')
Ask questions: Use the run method to ask questions about your data: result = pandas_ai.run(df, prompt='Your question here')
Generate visualizations: Ask PandasAI to create charts: pandas_ai.run(df, prompt='Plot a histogram of column X')
Work with multiple dataframes: Pass multiple dataframes to PandasAI for more complex analysis: pandas_ai.run([df1, df2], prompt='Compare data from both dataframes')
Review and interpret results: Examine the output from PandasAI, which may include text responses, data summaries, or visualizations

PandasAI FAQs

PandasAI is an open-source Python library that integrates generative AI capabilities into pandas, allowing users to interact with and analyze dataframes using natural language queries. It translates natural language into Python code and SQL queries to perform data analysis tasks.

PandasAI Monthly Traffic Trends

PandasAI experienced a 15.6% decline in traffic, reaching 42,322 visits. While there are no direct updates for PandasAI, the potential shift towards Java for enterprise AI applications and the availability of faster, more scalable alternatives like Polars and DuckDB might have contributed to this decrease in visits.

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