Rename package from tableqa to statqa

.github/workflows/ci.yml

@@ -29,16 +29,16 @@ jobs:
 
     - name: Lint with ruff
       run: |
-        ruff check tableqa tests
+        ruff check statqa tests
 
     - name: Type check with mypy
       run: |
-        mypy tableqa --ignore-missing-imports
+        mypy statqa --ignore-missing-imports
       continue-on-error: true
 
     - name: Run tests with pytest
       run: |
-        pytest --cov=tableqa --cov-report=xml --cov-report=term-missing
+        pytest --cov=statqa --cov-report=xml --cov-report=term-missing
 
     - name: Upload coverage to Codecov
       uses: codecov/codecov-action@v4

.github/workflows/python-publish.yml

@@ -41,7 +41,7 @@ jobs:
 
     environment:
       name: pypi
-      url: https://pypi.org/p/tableqa
+      url: https://pypi.org/p/statqa
 
     steps:
     - name: Download distribution packages

README.md

@@ -1,12 +1,12 @@
-# TableQA
+# StatQA
 
-[![CI](https://github.com/gojiplus/tableqa/actions/workflows/ci.yml/badge.svg)](https://github.com/gojiplus/tableqa/actions/workflows/ci.yml)
-[![Documentation](https://github.com/gojiplus/tableqa/actions/workflows/docs.yml/badge.svg)](https://gojiplus.github.io/tableqa)
-[![PyPI version](https://badge.fury.io/py/tableqa.svg)](https://pypi.org/project/tableqa/)
+[![CI](https://github.com/gojiplus/statqa/actions/workflows/ci.yml/badge.svg)](https://github.com/gojiplus/statqa/actions/workflows/ci.yml)
+[![Documentation](https://github.com/gojiplus/statqa/actions/workflows/docs.yml/badge.svg)](https://gojiplus.github.io/statqa)
+[![PyPI version](https://badge.fury.io/py/statqa.svg)](https://pypi.org/project/statqa/)
 [![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
-**TableQA** is a modern Python framework for automatically extracting structured facts, statistical insights, and Q/A pairs from tabular datasets. It converts raw columns and values into clear, human-readable statements, enabling rapid knowledge discovery, RAG corpus construction, and LLM training.
+**StatQA** is a modern Python framework for automatically extracting structured facts, statistical insights, and Q/A pairs from tabular datasets. It converts raw columns and values into clear, human-readable statements, enabling rapid knowledge discovery, RAG corpus construction, and LLM training.
 
 ## 🎯 Key Features
 
@@ -29,30 +29,30 @@
 ### Basic Installation
 
 ```bash
-pip install tableqa
+pip install statqa
 ```
 
 ### With Optional Features
 
 ```bash
 # Include LLM support (OpenAI/Anthropic)
-pip install tableqa[llm]
+pip install statqa[llm]
 
 # Include PDF parsing
-pip install tableqa[pdf]
+pip install statqa[pdf]
 
 # Development installation
-pip install tableqa[dev]
+pip install statqa[dev]
 
 # Complete installation
-pip install tableqa[all]
+pip install statqa[all]
 ```
 
 ### From Source
 
 ```bash
-git clone https://github.com/gojiplus/tableqa.git
-cd tableqa
+git clone https://github.com/gojiplus/statqa.git
+cd statqa
 pip install -e ".[dev]"
 ```
 
@@ -61,7 +61,7 @@ pip install -e ".[dev]"
 ### 1. Create a Codebook
 
 ```python
-from tableqa.metadata.parsers import TextParser
+from statqa.metadata.parsers import TextParser
 
 codebook_text = """
 # Variable: age
@@ -90,7 +90,7 @@ codebook = parser.parse(codebook_text)
 
 ```python
 import pandas as pd
-from tableqa.analysis import UnivariateAnalyzer, BivariateAnalyzer
+from statqa.analysis import UnivariateAnalyzer, BivariateAnalyzer
 
 # Load your data
 data = pd.read_csv("survey_data.csv")
@@ -114,7 +114,7 @@ result = biv_analyzer.analyze(
 ### 3. Generate Natural Language Insights
 
 ```python
-from tableqa.interpretation import InsightFormatter
+from statqa.interpretation import InsightFormatter
 
 formatter = InsightFormatter()
 insight = formatter.format_univariate(result)
@@ -126,7 +126,7 @@ print(insight)
 ### 4. Create Q/A Pairs for LLM Training
 
 ```python
-from tableqa.qa import QAGenerator
+from statqa.qa import QAGenerator
 
 qa_gen = QAGenerator(use_llm=False)  # Template-based
 qa_pairs = qa_gen.generate_qa_pairs(result, insight)
@@ -139,19 +139,19 @@ for qa in qa_pairs:
 
 Each Q/A pair includes **provenance metadata** tracking:
 - **When** the answer was generated (timestamp)
-- **What tool** was used (tableqa version)
+- **What tool** was used (statqa version)
 - **What compute** was performed (analysis type, analyzer)
 - **How** it was generated (template vs. LLM paraphrase)
 - **Which LLM** was used (if applicable)
 
 ## 🎨 Complete Pipeline Example
 
 ```python
-from tableqa import Codebook, UnivariateAnalyzer
-from tableqa.metadata.parsers import CSVParser
-from tableqa.interpretation import InsightFormatter
-from tableqa.qa import QAGenerator
-from tableqa.utils.io import load_data, save_json
+from statqa import Codebook, UnivariateAnalyzer
+from statqa.metadata.parsers import CSVParser
+from statqa.interpretation import InsightFormatter
+from statqa.qa import QAGenerator
+from statqa.utils.io import load_data, save_json
 
 # 1. Parse codebook
 parser = CSVParser()
@@ -184,7 +184,7 @@ with open("training_data.jsonl", "w") as f:
 
 ## 📝 Q/A Provenance Tracking
 
-Every Q/A pair generated by TableQA includes detailed **provenance metadata** to ensure reproducibility and traceability:
+Every Q/A pair generated by StatQA includes detailed **provenance metadata** to ensure reproducibility and traceability:
 
 ```json
 {
@@ -193,7 +193,7 @@ Every Q/A pair generated by TableQA includes detailed **provenance metadata** to
   "type": "descriptive",
   "provenance": {
     "generated_at": "2025-11-19T10:30:45.123456+00:00",
-    "tool": "tableqa",
+    "tool": "statqa",
     "tool_version": "0.1.0",
     "generation_method": "template",
     "analysis_type": "univariate",
@@ -207,8 +207,8 @@ Every Q/A pair generated by TableQA includes detailed **provenance metadata** to
 | Field | Description | Example Values |
 |-------|-------------|----------------|
 | `generated_at` | ISO 8601 timestamp (UTC) | `2025-11-19T10:30:45+00:00` |
-| `tool` | Software used for generation | `tableqa` |
-| `tool_version` | Version of tableqa | `0.1.0` |
+| `tool` | Software used for generation | `statqa` |
+| `tool_version` | Version of statqa | `0.1.0` |
 | `generation_method` | How the Q/A was created | `template`, `llm_paraphrase` |
 | `analysis_type` | Statistical analysis performed | `univariate`, `bivariate`, `temporal`, `causal` |
 | `analyzer` | Specific analyzer class used | `UnivariateAnalyzer`, `BivariateAnalyzer` |
@@ -222,20 +222,20 @@ This provenance tracking enables:
 
 ## 🖥️ Command-Line Interface
 
-TableQA provides a powerful CLI for common workflows:
+StatQA provides a powerful CLI for common workflows:
 
 ```bash
 # Parse a codebook
-tableqa parse-codebook codebook.csv --output codebook.json --enrich
+statqa parse-codebook codebook.csv --output codebook.json --enrich
 
 # Run full analysis pipeline
-tableqa analyze data.csv codebook.json --output-dir results/ --plots
+statqa analyze data.csv codebook.json --output-dir results/ --plots
 
 # Generate Q/A pairs
-tableqa generate-qa results/all_insights.json --output qa_pairs.jsonl --llm
+statqa generate-qa results/all_insights.json --output qa_pairs.jsonl --llm
 
 # Complete pipeline
-tableqa pipeline data.csv codebook.csv --output-dir output/ --enrich --qa
+statqa pipeline data.csv codebook.csv --output-dir output/ --enrich --qa
 ```
 
 ## 📊 Supported Analyses
@@ -268,7 +268,7 @@ tableqa pipeline data.csv codebook.csv --output-dir output/ --enrich --qa
 ### LLM-Powered Metadata Enrichment
 
 ```python
-from tableqa.metadata import MetadataEnricher
+from statqa.metadata import MetadataEnricher
 
 enricher = MetadataEnricher(provider="openai", api_key="your-key")
 enriched_codebook = enricher.enrich_codebook(codebook)
@@ -279,7 +279,7 @@ enriched_codebook = enricher.enrich_codebook(codebook)
 ### Multiple Testing Correction
 
 ```python
-from tableqa.utils.stats import correct_multiple_testing
+from statqa.utils.stats import correct_multiple_testing
 
 p_values = [0.03, 0.01, 0.15, 0.002]
 reject, corrected_p = correct_multiple_testing(p_values, method="fdr_bh")
@@ -288,37 +288,37 @@ reject, corrected_p = correct_multiple_testing(p_values, method="fdr_bh")
 ### Custom Visualizations
 
 ```python
-from tableqa.visualization import PlotFactory
+from statqa.visualization import PlotFactory
 
 plotter = PlotFactory(style="publication", figsize=(10, 6))
 fig = plotter.plot_bivariate(data, var1, var2, output_path="plot.png")
 ```
 
 ## 📚 Documentation
 
-- **Full Documentation**: [https://gojiplus.github.io/tableqa](https://gojiplus.github.io/tableqa)
-- **API Reference**: [API Docs](https://gojiplus.github.io/tableqa/api/)
+- **Full Documentation**: [https://gojiplus.github.io/statqa](https://gojiplus.github.io/statqa)
+- **API Reference**: [API Docs](https://gojiplus.github.io/statqa/api/)
 - **Examples**: See [examples/](examples/) directory
 
 ## 🧪 Development
 
 ### Running Tests
 
 ```bash
-pytest --cov=tableqa --cov-report=html
+pytest --cov=statqa --cov-report=html
 ```
 
 ### Code Quality
 
 ```bash
 # Linting
-ruff check tableqa tests
+ruff check statqa tests
 
 # Type checking
-mypy tableqa
+mypy statqa
 
 # Formatting
-black tableqa tests
+black statqa tests
 ```
 
 ### Building Documentation
@@ -354,9 +354,9 @@ This project is licensed under the MIT License - see the [LICENSE](LICENSE) file
 
 ## 📬 Contact & Support
 
-- **Issues**: [GitHub Issues](https://github.com/gojiplus/tableqa/issues)
-- **Discussions**: [GitHub Discussions](https://github.com/gojiplus/tableqa/discussions)
-- **Email**: maintainers@tableqa.org
+- **Issues**: [GitHub Issues](https://github.com/gojiplus/statqa/issues)
+- **Discussions**: [GitHub Discussions](https://github.com/gojiplus/statqa/discussions)
+- **Email**: maintainers@statqa.org
 
 ## 🗺️ Roadmap
 

docs/QA_DATASET_GENERATION_PIPELINE.md

@@ -2,7 +2,7 @@
 
 ## Overview
 
-This document describes TableQA's pipeline for generating Q/A-style datasets from tabular data with metadata. The pipeline converts raw tables into structured question-answer pairs suitable for training or fine-tuning LLMs on data analysis tasks.
+This document describes StatQA's pipeline for generating Q/A-style datasets from tabular data with metadata. The pipeline converts raw tables into structured question-answer pairs suitable for training or fine-tuning LLMs on data analysis tasks.
 
 ## Pipeline Architecture
 
@@ -52,7 +52,7 @@ Output: Q/A Dataset for LLM Training
 
 **Example**:
 ```python
-from tableqa.metadata.parsers import CSVParser
+from statqa.metadata.parsers import CSVParser
 
 parser = CSVParser()
 codebook = parser.parse('codebook.csv')
@@ -84,7 +84,7 @@ codebook = parser.parse('codebook.csv')
 
 **Example**:
 ```python
-from tableqa.analysis import UnivariateAnalyzer, BivariateAnalyzer
+from statqa.analysis import UnivariateAnalyzer, BivariateAnalyzer
 
 # Univariate
 analyzer = UnivariateAnalyzer()
@@ -116,7 +116,7 @@ range=[25,000.00, 133,843.00]. N=500 [non-normal distribution].
 
 **Example**:
 ```python
-from tableqa.interpretation import InsightFormatter
+from statqa.interpretation import InsightFormatter
 
 formatter = InsightFormatter()
 insight = formatter.format_univariate(result)
@@ -143,7 +143,7 @@ insight = formatter.format_univariate(result)
 
 **Example**:
 ```python
-from tableqa.qa import QAGenerator
+from statqa.qa import QAGenerator
 
 # Template-based only
 qa_gen = QAGenerator(use_llm=False)
@@ -325,10 +325,10 @@ A: **Survived**: most common category is '0' (60.2%), N=400.
 
 ```python
 from pathlib import Path
-from tableqa.metadata.parsers import CSVParser
-from tableqa.analysis import UnivariateAnalyzer, BivariateAnalyzer
-from tableqa.interpretation import InsightFormatter
-from tableqa.qa import QAGenerator
+from statqa.metadata.parsers import CSVParser
+from statqa.analysis import UnivariateAnalyzer, BivariateAnalyzer
+from statqa.interpretation import InsightFormatter
+from statqa.qa import QAGenerator
 
 # 1. Parse codebook
 parser = CSVParser()
@@ -366,13 +366,13 @@ with open('qa_dataset.jsonl', 'w') as f:
 
 ```bash
 # Complete pipeline
-tableqa pipeline data.csv codebook.csv \
+statqa pipeline data.csv codebook.csv \
     --output-dir output/ \
     --qa \
     --enrich
 
 # Just Q/A generation from existing insights
-tableqa generate-qa insights.json \
+statqa generate-qa insights.json \
     --output qa_pairs.jsonl \
     --format openai \
     --llm

docs/source/conf.py

@@ -1,18 +1,18 @@
-"""Sphinx configuration for tableqa documentation."""
+"""Sphinx configuration for statqa documentation."""
 
 import sys
 from pathlib import Path
 
-from tableqa import __version__
+from statqa import __version__
 
 
 # Add project root to path
 sys.path.insert(0, str(Path(__file__).parents[2]))
 
 # Project information
-project = "tableqa"
-copyright = "2025, TableQA Contributors"
-author = "TableQA Contributors"
+project = "statqa"
+copyright = "2025, StatQA Contributors"
+author = "StatQA Contributors"
 
 # Version
 version = __version__