aillmcleaner 0.2.1

An AI-powered Python library for context-aware data cleaning using local LLMs

AILLMCleaner

An AI-powered Python library for intelligent, context-aware automated data cleaning using both Local LLMs (Ollama) and Cloud AI APIs (Google Gemini, OpenAI, Groq).

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What Does It Do?

Real-world datasets are messy — names in wrong case, duplicate city names, missing values, invalid emails. aillmcleaner uses Large Language Models to understand and fix your data intelligently, not just with simple rules.

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Requirements

• Python 3.8 or higher
• pandas
• requests
• openai
• google-genai
• One AI provider (Ollama, Gemini, OpenAI, or Groq)

Install all dependencies:

pip install aillmcleaner

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AI Provider Options

Ollama — Free, runs locally on your computer. No API key needed. Download from: https://ollama.com

Google Gemini — Free tier available. Get API key from: https://aistudio.google.com

OpenAI (ChatGPT) — Paid service. Get API key from: https://platform.openai.com

Groq — Free tier available, very fast. Get API key from: https://console.groq.com

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Installation

pip install aillmcleaner

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Quick Start

Using Ollama (Free, No API Key Needed)

First start Ollama on your computer:

ollama serve
ollama pull llama3.2

Then use in Python:

import pandas as pd
from aillmcleaner import clean_column, standardize_column, fill_missing, detect_anomalies

data = {
    "name":    ["alice johnson", "BOB SMITH", "Charlie  Brown"],
    "city":    ["new york", "newyork", "LA"],
    "country": [None, "USA", "United States"]
}

df = pd.DataFrame(data)

# Fix name casing
df["name"] = clean_column(df, "name",
    instruction="Convert to proper title case. Return only the name.")

# Standardize city names
df["city"] = standardize_column(df, "city",
    categories=["New York", "Los Angeles", "Chicago"])

# Fill missing country values
df["country"] = fill_missing(df, "country", context_columns=["city"])

print(df)

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Using Google Gemini

from aillmcleaner import clean_column

df["name"] = clean_column(df, "name",
    provider="gemini",
    api_key="YOUR_GEMINI_API_KEY")

Or set as environment variable:

export GEMINI_API_KEY="your_key_here"

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Using OpenAI

from aillmcleaner import clean_column

df["name"] = clean_column(df, "name",
    provider="openai",
    model="gpt-4o-mini",
    api_key="YOUR_OPENAI_API_KEY")

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Using Groq

from aillmcleaner import clean_column

df["name"] = clean_column(df, "name",
    provider="groq",
    model="llama3-8b-8192",
    api_key="YOUR_GROQ_API_KEY")

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All Available Functions

clean_text — Clean a single text value.

from aillmcleaner import clean_text
result = clean_text("helo wrold", provider="gemini", api_key="YOUR_KEY")

clean_column — Clean an entire DataFrame column.

df["name"] = clean_column(df, "name",
    instruction="Fix spelling and capitalize properly.")

standardize_column — Map column values to a fixed list of categories.

df["city"] = standardize_column(df, "city",
    categories=["New York", "Los Angeles", "Chicago"])

fill_missing — Fill missing values using context from other columns.

df["country"] = fill_missing(df, "country",
    context_columns=["city", "state"])

detect_anomalies — Find suspicious or invalid values in a column.

bad_values = detect_anomalies(df, "email")
print(bad_values)

clean_dataframe — Clean all text columns in a DataFrame at once.

from aillmcleaner import clean_dataframe
cleaned_df = clean_dataframe(df, provider="groq", api_key="YOUR_KEY")

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Provider and Model Options

provider can be: ollama (default), gemini, openai, groq model is optional — uses the best default for each provider api_key is required only for cloud providers (gemini, openai, groq)

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License

MIT License — free to use, modify, and distribute.

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Author

Sujoy Panigrahi (spanigrahidev) GitHub: https://github.com/spanigrahidev PyPI: https://pypi.org/project/aillmcleaner/