Install Polars DataFrame Library and with Local LLMs Using Ollama

 This video installs Polars and demonstrates how to integrate with Ollama local models to do data analysis.

Code:

conda create -n polarsdf python=3.11 -y && conda activate polarsdf

conda install jupyter -y

jupyter notebook

!pip install polars pyarrow pandas ollama

import polars as pl

from datetime import datetime

df = pl.DataFrame(

    {

        "date": [

            datetime(2024, 7, 1),

            datetime(2024, 7, 2),

            datetime(2024, 7, 3),

            datetime(2024, 7, 4),

            datetime(2024, 7, 5),

        ],

        "revenue": [1000.0, 1200.0, 1100.0, 1300.0, 1400.0],

        "expenses": [500, 600, 550, 650, 700]

    }

)

print(df)

df.write_csv("dummyfinance.csv")

df_csv = pl.read_csv("dummyfinance.csv")

print(df_csv)

df.select(pl.col("*"))

df.select(pl.col("revenue", "date"))

df.filter(

    pl.col("date").is_between(datetime(2024, 7, 2), datetime(2024, 7, 4)),

)

import ollama

prompt = "Analyze the financial data: "

for row in df.itertuples(index=True):

    prompt += f"Date: {row.date.strftime('%Y-%m-%d')}, Revenue: ${row.revenue}, Expenses: ${row.expenses}; "

prompt += "Predict the Revenue and Expenses for the next date."

response = ollama.generate(model='llama3', prompt=prompt)

predicted_revenue = response['response'].split('Revenue: $')[-1].split('\n')[0]

predicted_expenses = response['response'].split('Expenses: $')[-1].split('\n')[0]

print(predicted_revenue)

print(predicted_expenses)