Interact with Pandas objects via LLMs and LangChain.

YOLOPandas lets you specify commands with natural language and execute them directly on Pandas objects. You can preview the code before executing, or set yolo=True to execute the code straight from the LLM.

Warning: YOLOPandas will execute arbitrary Python code on the machine it runs on. This is a dangerous thing to do.

pip install yolopandas

YOLOPandas adds a llm accessor to Pandas dataframes.

from yolopandas import pd

df = pd.DataFrame(
    [
        {"name": "The Da Vinci Code", "type": "book", "price": 15, "quantity": 300, "rating": 4},
        {"name": "Jurassic Park", "type": "book", "price": 12, "quantity": 400, "rating": 4.5},
        {"name": "Jurassic Park", "type": "film", "price": 8, "quantity": 6, "rating": 5},
        {"name": "Matilda", "type": "book", "price": 5, "quantity": 80, "rating": 4},
        {"name": "Clockwork Orange", "type": None, "price": None, "quantity": 20, "rating": 4},
        {"name": "Walden", "type": None, "price": None, "quantity": 100, "rating": 4.5},
    ],
)

df.llm.query("What item is the least expensive?")

The above will generate Pandas code to answer the question, and prompt the user to accept or reject the proposed code. Accepting it in this case will return a Pandas dataframe containing the result.

Alternatively, you can execute the LLM output without first previewing it:

df.llm.query("What item is the least expensive?", yolo=True)

.query can return the result of the computation, which we do not constrain. For instance, while "Show me products under $10" will return a dataframe, the query "Split the dataframe into two, 1/3 in one, 2/3 in the other. Return (df1, df2)" can return a tuple of two dataframes. You can also chain queries together, for instance:

df.llm.query("Group by type and take the mean of all numeric columns.", yolo=True).llm.query("Make a bar plot of the result and use a log scale.", yolo=True)

See the example notebook for more ideas.

This package uses several LangChain components, making it easy to work with if you are familiar with LangChain. In particular, it utilizes the LLM, Chain, and Memory abstractions.

By working with LangChain's LLM abstraction, it is very easy to plug-and-play different LLM providers into YOLOPandas. You can do this in a few different ways:

1. You can change the default LLM by specifying a config path using the LLPANDAS_LLM_CONFIGURATION environment variable. The file at this path should be in one of the accepted formats.

2. If you have a LangChain LLM wrapper in memory, you can set it as the default LLM to use by doing:

import yolopandas
yolopandas.set_llm(llm)

3. You can set the LLM wrapper to use for a specific dataframe by doing: df.reset_chain(llm=llm)

By working with LangChain's Chain abstraction, it is very easy to plug-and-play different chains into YOLOPandas. This can be useful if you want to customize the prompt, customize the chain, or anything like that.

To use a custom chain for a particular dataframe, you can do:

df.set_chain(chain)

If you ever want to reset the chain to the base chain, you can do:

df.reset_chain()

The default chain used by YOLOPandas utilizes the LangChain concept of memory. This allows for "remembering" of previous commands, making it possible to ask follow up questions or ask for execution of commands that stem from previous interactions.

For example, the query "Make a seaborn plot of price grouped by type" can be followed with "Can you use a dark theme, and pastel colors?" upon viewing the initial result.

By default, memory is turned on. In order to have it turned off by default, you can set the environment variable LLPANDAS_USE_MEMORY=False.

If you are resetting the chain, you can also specify whether to use memory there:

df.reset_chain(use_memory=False)