import pandas as pd
import numpy as np
from intugle_tools import DataCleanerAI

# Initialize the AI-powered data cleaner
cleaner = DataCleanerAI(model='gpt-4')

# Load your messy spreadsheet
df = pd.read_csv('your_messy_data.csv')

# Let AI detect and clean common data issues
def clean_spreadsheet(df):
    # Auto-detect column types and fix inconsistencies
    df = cleaner.auto_detect_types(df)
    
    # Fix missing values using context-aware imputation
    df = cleaner.smart_impute(df)
    
    # Standardize inconsistent entries (e.g., 'NY', 'New York', 'N.Y.')
    df = cleaner.standardize_entries(df)
    
    # Extract structured data from unstructured text columns
    df = cleaner.extract_entities(df, columns=['notes', 'comments'])
    
    return df

# Run the cleaning process
cleaned_df = clean_spreadsheet(df)

# Save the cleaned data
cleaned_df.to_csv('cleaned_data.csv', index=False)
print(f"Cleaned {len(df)} rows. Common issues fixed: inconsistent formats, missing values, unstructured text.")

The Eternal Quest to Automate the Boring Stuff (We Keep Creating)

Remember when we were promised that computers would give us a four-day workweek? Instead, they gave us Excel, and now we need AI to understand the eldritch horrors we've created within it. Intugle/data-tools enters the arena with the classic value proposition: use AI to do the work you don't want to do. It's the digital equivalent of hiring a very expensive, slightly confused intern who speaks exclusively in probabilistic language models.

What's Actually in the Box?

According to the sparse GitHub page—because why document things when you can just say "AI-powered"—this is a toolkit built in Jupyter Notebook. Its stated purpose is "automated data intelligence," which could mean anything from generating a basic bar chart to writing a haiku about your quarterly EBITDA. The beauty of modern tech marketing is that vagueness is a feature, not a bug. It allows every CTO to project their deepest data anxieties onto the product and imagine it solved.

The 146 stars are telling. It's in that sweet spot of being just popular enough to seem legit, but not so popular that you have to deal with actual scaling issues or user complaints. It's the digital product equivalent of a cozy neighborhood bistro that hasn't been ruined by a TikTok review yet.

The 'Automated Intelligence' Paradox

Here's the hilarious contradiction at the heart of tools like this: they're designed to bring order to data chaos, but they require you to have your data in a somewhat orderly state to begin with. It's like selling a self-cleaning oven that first requires you to degrease it manually. The promise is that the AI will "understand" your data. But anyone who's worked with real-world data knows it's less about understanding and more about negotiating with a hostile entity.

Will the AI know that the column labeled "Revnue" is actually "Revenue" and that the entries "NY," "New York," and "Big Apple" refer to the same place? Or will it, in true AI fashion, confidently present you with an analysis showing your top-performing sales region is a fruit? The tool's success hinges on it being smarter than the humans who created the mess, which is a high bar indeed.

Another Tool for the 'Insight-Driven' Theater

Let's be real. The primary customer for "automated data intelligence" isn't the overworked data scientist. It's the middle manager who needs to generate a "data-driven" slide for a Friday leadership meeting that everyone will ignore. This tool provides plausible deniability. "The AI generated these insights," you can say, shrugging, when the VP asks why the forecast was off by 300%. It wasn't your fault; it was the machine's. We've outsourced blame along with the analysis.

It joins the prestigious pantheon of tools that facilitate corporate performance art: the sentiment analyzer for all-hands meetings, the engagement tracker for mandatory fun, and now, the AI that makes pretty graphs from garbage data.

The Real Test: Does It Save You From Tedium?

Beneath the sarcasm, there's a genuine need. Data preparation consumes an estimated 80% of a data analyst's time. It's soul-crushing work. If an open-source AI toolkit can shave even 10% off that by auto-suggesting data types, spotting outliers, or drafting common SQL queries, that's a win. The question is whether Intugle/data-tools delivers that, or if it's just a fancy wrapper around some basic pandas functions and an OpenAI API call.

Being built on Jupyter Notebook is a double-edged sword. It's accessible and familiar to the data crowd, but it also immediately limits it to a technical audience. So much for "democratizing data." The true democratization would be a tool your marketing director could use without setting the AWS bill on fire.

The lack of detailed documentation or a clear feature list on its trending page suggests it's early days. It's likely a cool side project that caught a wave. In the tech industry, that's all you need to be declared the "future of X."