Query dataframes, find issue with your notebook snippets as if a professional data scientist was pair coding with you.
Currently just a thin wrapper around an amazing library called pandas-ai by sinaptik-ai!
from date_a_scientist import DateAScientist
import pandas as pd
df = pd.DataFrame(
[
{"name": "Alice", "age": 25, "city": "New York"},
{"name": "Bob", "age": 30, "city": "Los Angeles"},
{"name": "Charlie", "age": 35, "city": "Chicago"},
]
)
ds = DateAScientist(
df=df,
llm_openai_api_token=..., # your OpenAI API token goes here
llm_model_name="gpt-3.5-turbo", # by default, it uses "gpt-4o"
)
# should return "Alice"
ds.chat("What is the name of the first person?")Additionally we can pass a description of fields, so that more meaningful questions can be asked:
ds = DateAScientist(
df=df,
llm_openai_api_token=..., # your OpenAI API token goes here
llm_model_name="gpt-3.5-turbo", # by default, it uses "gpt-4o"
column_descriptions={
"name": "The name of the person",
"age": "The age of the person",
"city": "The city where the person lives",
},
)
ds = DateAScientist(
df=df,
llm_openai_api_token=..., # your OpenAI API token goes here
llm_model_name="gpt-3.5-turbo", # by default, it uses "gpt-4o"
)
# should return DataFrame with Chicago rows
ds.chat("Who lives in Chicago?")Finally if you want to get the code that was generated, you can use ds.code():
ds.code("Who lives in Chicago?")which will return monokai styled code. If you want to return plain code, you can use:
ds.code("Who lives in Chicago?", return_as_string=True)