Metadata-Version: 2.4
Name: databubble
Version: 0.2.0
Summary: Statistical Intelligence as a Service — rigorous analysis skills via API
Author: DataBubble AI
License: MIT
Project-URL: Homepage, https://databubble.ai
Project-URL: Documentation, https://api.databubble.ai
Project-URL: Repository, https://github.com/sss828412/databubble-python
Project-URL: Issues, https://github.com/sss828412/databubble-python/issues
Keywords: statistics,data analysis,machine learning,api
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Provides-Extra: recommended
Requires-Dist: httpx>=0.24; extra == "recommended"
Requires-Dist: pandas>=1.5; extra == "recommended"
Provides-Extra: dev
Requires-Dist: httpx>=0.24; extra == "dev"
Requires-Dist: pandas>=1.5; extra == "dev"
Requires-Dist: pytest>=7; extra == "dev"
Requires-Dist: pytest-mock; extra == "dev"

# DataBubble SDK

Statistical Intelligence as a Service.

```bash
pip install databubble httpx pandas
```

## Quick start

```python
from databubble import DataBubble
import pandas as pd

db = DataBubble(api_key="dbk_...")

# Univariate analysis
result = db.skills.univariate(df["price"])
print(result.summary)
print(result.warnings)

# Missing value profiling
result = db.skills.missing_values(df)

# Export session memory for next session
mem = db.memory.export(df, label="POS data June 2026")
mem.save("pos_memory.json")
```

## Available skills

| Skill | Input | What it does |
|---|---|---|
| `univariate` | Series or DataFrame + column= | Distribution analysis, skewness, bounded ordinal detection, MNAR flags |
| `outliers` | Series or DataFrame + column= | IQR + Z-score outlier detection |
| `missing_values` | DataFrame | MCAR/MAR/MNAR profiling, treatment recommendations |
| `leakage` | DataFrame + outcome= | Post-outcome timing detection, correlation proxy check |
| `bivariate` | DataFrame + x= + y= | Relationship analysis, linearity check |
| `correlation` | DataFrame + x= + y= | Pearson + Spearman, non-linearity flag |

## Tiers

| Tier | Price | Calls/month | Skills |
|---|---|---|---|
| Developer | Free | 500 | Core analysis skills |
| Pro | $49/month | 10,000 | All skills |
| Business | $299/month | 100,000 | All skills + journey endpoints |
| Enterprise | Custom | Unlimited | Everything |

Get a key at [databubble.ai](https://databubble.ai).
