tableai 0.2.1

AI toolkit for tabular data — auto EDA, data profiling, anomaly detection, and smart transformations on DataFrames.

tableai

AI toolkit for your DataFrames

AI toolkit for tabular data -- auto EDA, data profiling, anomaly detection, and smart transformations on DataFrames. The pandas AI companion.

Runs completely offline. All processing uses local pandas/numpy/scikit-learn. No cloud APIs or internet connection required.

Built by Viet-Anh Nguyen | GitHub

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Installation

pip install tableai

With optional dependencies:

pip install tableai[ml]    # scikit-learn for Isolation Forest anomaly detection
pip install tableai[viz]   # matplotlib + seaborn for visualizations
pip install tableai[all]   # everything

Quick Start

import pandas as pd
import tableai

df = pd.read_csv("your_data.csv")

# Comprehensive data profile
report = tableai.profile(df)
print(report)

Output:

=== Data Profile ===
Shape: (1000, 8)
Memory usage: 62.6 KB

Column Profiles:
  age (int64)
    Non-null: 950 / 1000 (95.0%)  |  Unique: 60
    Mean: 35.2  |  Std: 12.1  |  Min: 18.0  |  Max: 75.0

  city (object)
    Non-null: 980 / 1000 (98.0%)  |  Unique: 25
    Top values: New York (120), London (98), Tokyo (85)
...

Auto-Clean

cleaned_df = tableai.clean(df)
# Fills missing values, removes duplicates, fixes dtypes, handles outliers

Anomaly Detection

result = tableai.anomalies(df)
print(result)

Output:

=== Anomaly Report ===
Method: iqr
Total rows flagged: 42 / 1000 (4.2%)

  salary: 18 anomalies (min_bound=20000, max_bound=150000)
  age: 7 anomalies (min_bound=5, max_bound=80)
...

Auto Insights

insights = tableai.insights(df)
for insight in insights:
    print(f"- {insight}")

Output:

- Column 'age' has 5.0% missing values
- Strong positive correlation (0.87) between 'hours_worked' and 'salary'
- Column 'income' is highly skewed (skewness=2.31)
- Column 'city' has high cardinality (250 unique values out of 1000 rows)

DataFrame Comparison

diff = tableai.compare(df_old, df_new)
print(diff)

Smart Transformations

transformed_df = tableai.transform(df)
# Auto-encodes categoricals, scales numerics, extracts datetime features

Comparison with Alternatives

Feature	tableai	pandas-profiling	sweetviz
Zero-config profiling	Yes	Yes	Yes
Auto-cleaning	Yes	No	No
Anomaly detection	Yes	No	No
Smart transforms	Yes	No	No
Auto insights (text)	Yes	No	No
DataFrame comparison	Yes	No	Yes
Lightweight (no heavy deps)	Yes	No	No
Notebook-friendly output	Yes	Yes	Yes

API Reference

• tableai.profile(df) -- Comprehensive data profile
• tableai.clean(df, **kwargs) -- Auto-clean DataFrame
• tableai.anomalies(df, method="iqr") -- Detect anomalies
• tableai.transform(df, **kwargs) -- Smart feature engineering
• tableai.insights(df) -- Auto-generate text insights
• tableai.compare(df1, df2) -- Compare two DataFrames

Local-First / Edge AI

This package is designed to work completely offline. All data profiling, cleaning, anomaly detection, and transformations run locally using pandas, numpy, and scikit-learn. No internet connection or cloud APIs are required.

License

MIT License. See LICENSE for details.

Links

• Website: nrl.ai
• Repository: github.com/vietanhdev/tableai
• Author: Viet-Anh Nguyen