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97 lines
2.6 KiB
Markdown
97 lines
2.6 KiB
Markdown
---
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title: Pandas-AI
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weight: 2900
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---
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# Pandas-AI
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Pandas-AI is a Python library that uses a generative AI model to interpret natural language queries and translate them into Python code to interact with pandas data frames and return the final results to the user.
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## Installation
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```console
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pip install pandasai[qdrant]
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```
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## Usage
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You can begin a conversation by instantiating an `Agent` instance based on your Pandas data frame. The default Pandas-AI LLM requires an [API key](https://pandabi.ai).
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You can find the list of all supported LLMs [here](https://docs.pandas-ai.com/en/latest/LLMs/llms/)
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```python
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import os
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import pandas as pd
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from pandasai import Agent
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# Sample DataFrame
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sales_by_country = pd.DataFrame(
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{
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"country": [
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"United States",
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"United Kingdom",
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"France",
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"Germany",
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"Italy",
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"Spain",
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"Canada",
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"Australia",
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"Japan",
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"China",
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],
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"sales": [5000, 3200, 2900, 4100, 2300, 2100, 2500, 2600, 4500, 7000],
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}
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)
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os.environ["PANDASAI_API_KEY"] = "YOUR_API_KEY"
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agent = Agent(sales_by_country)
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agent.chat("Which are the top 5 countries by sales?")
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# OUTPUT: China, United States, Japan, Germany, Australia
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```
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## Qdrant support
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You can train Pandas-AI to understand your data better and improve the quality of the results.
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Qdrant can be configured as a vector store to ingest training data and retrieve semantically relevant content.
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```python
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from pandasai.ee.vectorstores.qdrant import Qdrant
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qdrant = Qdrant(
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collection_name="<SOME_COLLECTION>",
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embedding_model="sentence-transformers/all-MiniLM-L6-v2",
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url="http://localhost:6333",
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grpc_port=6334,
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prefer_grpc=True
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)
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agent = Agent(df, vector_store=qdrant)
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# Train with custom information
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agent.train(docs="The fiscal year starts in April")
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# Train the q/a pairs of code snippets
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query = "What are the total sales for the current fiscal year?"
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response = """
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import pandas as pd
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df = dfs[0]
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# Calculate the total sales for the current fiscal year
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total_sales = df[df['date'] >= pd.to_datetime('today').replace(month=4, day=1)]['sales'].sum()
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result = { "type": "number", "value": total_sales }
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"""
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agent.train(queries=[query], codes=[response])
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# # The model will use the information provided in the training to generate a response
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```
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## Further reading
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- [Getting Started with Pandas-AI](https://pandasai-docs.readthedocs.io/en/latest/getting-started/)
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- [Pandas-AI Reference](https://pandasai-docs.readthedocs.io/en/latest/)
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- [Source Code](https://github.com/Sinaptik-AI/pandas-ai/blob/main/pandasai/ee/vectorstores/qdrant.py)
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