Simplify README to only document existing script

Remove basic-stats.py documentation (file doesn't exist yet)

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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

README.md

@@ -3,250 +3,27 @@ viewer: false
 tags:
   - uv-script
   - dataset-statistics
-  - data-quality
-  - text-analysis
+  - polars
+  - temporal-analysis
 license: apache-2.0
 ---
 
 # Dataset Statistics
 
-Calculate essential text statistics for HuggingFace datasets using streaming mode. No ML models, pure Python, works on datasets of any size.
+UV scripts for analyzing HuggingFace datasets using streaming mode.
 
 ## Scripts
 
-| Script | Description |
-|--------|-------------|
-| `basic-stats.py` | Essential text statistics (chars, words, sentences) for any dataset |
-| `finepdfs-stats.py` | Temporal analysis of educational quality in finepdfs-edu |
-
----
-
-### `basic-stats.py` - Essential Text Statistics
-
-Calculate fundamental text statistics using pure Python (no ML dependencies). Uses streaming mode by default, so it works on datasets of any size without downloading the full dataset.
-
-**Statistics calculated:**
-- Character, word, line, sentence counts (per sample and total)
-- Streaming mean and standard deviation using Welford's algorithm
-- Character type distributions (alphanumeric, digits, punctuation, whitespace, special characters)
-- Length statistics (min, max)
-- Derived metrics (words per line, chars per word, words per sentence)
-
-**Features:**
-- ✅ Pure Python (no ML models required)
-- ✅ Streaming mode (constant memory usage)
-- ✅ Progress tracking with tqdm
-- ✅ Optional per-sample CSV output
-- ✅ Works on datasets of any size
-- ✅ Fast: ~10k-50k samples/sec on CPU
-
-## Installation
-
-No installation needed! Just use `uv run`:
-
-```bash
-# Run directly with uv
-uv run https://huggingface.co/datasets/uv-scripts/dataset-stats/raw/main/basic-stats.py --help
-```
-
-## Usage Examples
-
-### Quick Test (10k samples)
-
-```bash
-uv run basic-stats.py HuggingFaceFW/fineweb-edu --max-samples 10000
-```
-
-### Full Dataset Statistics
-
-```bash
-uv run basic-stats.py allenai/c4 --split train
-```
-
-### Different Text Column
-
-```bash
-uv run basic-stats.py username/dataset --text-column content
-```
-
-### Save Per-Sample Statistics
-
-```bash
-uv run basic-stats.py username/dataset --per-sample --output-file my-stats.csv
-```
-
-### Using HF Jobs (for large datasets)
-
-```bash
-hf jobs uv run \
-    -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \
-    https://huggingface.co/datasets/uv-scripts/dataset-stats/raw/main/basic-stats.py \
-    username/very-large-dataset --max-samples 100000
-```
-
-## Example Output
-
-```json
-{
-  "dataset": "HuggingFaceFW/fineweb-edu",
-  "split": "train",
-  "text_column": "text",
-  "total_samples": 10000,
-  "statistics": {
-    "character_count": {
-      "count": 10000,
-      "mean": 3542.18,
-      "std": 2134.52,
-      "min": 120.0,
-      "max": 45231.0
-    },
-    "word_count": {
-      "count": 10000,
-      "mean": 642.34,
-      "std": 387.21,
-      "min": 18.0,
-      "max": 8234.0
-    },
-    "line_count": {
-      "count": 10000,
-      "mean": 28.5,
-      "std": 16.3,
-      "min": 2.0,
-      "max": 234.0
-    },
-    "sentence_count": {
-      "count": 10000,
-      "mean": 24.7,
-      "std": 14.2,
-      "min": 1.0,
-      "max": 187.0
-    },
-    "mean_word_length": {
-      "count": 10000,
-      "mean": 5.52,
-      "std": 0.87,
-      "min": 2.1,
-      "max": 12.4
-    }
-  },
-  "character_type_distribution": {
-    "alphanumeric": 0.8234,
-    "alphabetic": 0.7891,
-    "digit": 0.0343,
-    "uppercase": 0.0456,
-    "lowercase": 0.9544,
-    "whitespace": 0.1523,
-    "punctuation": 0.0187,
-    "special": 0.0056
-  },
-  "derived_metrics": {
-    "avg_words_per_line": 22.54,
-    "avg_chars_per_word": 5.52,
-    "avg_words_per_sentence": 26.01
-  }
-}
-```
-
-## Performance
-
-- **Speed**: ~10,000-50,000 samples/sec on CPU (depending on text length)
-- **Memory**: Constant O(1) memory usage (streaming statistics)
-- **Dependencies**: Pure Python + datasets library
-- **GPU**: Not needed
-
-## Use Cases
-
-### Understanding Dataset Characteristics
-
-Get a quick overview of your dataset's basic properties:
-```bash
-uv run basic-stats.py username/my-dataset --max-samples 10000
-```
-
-### Comparing Datasets
-
-Generate statistics for multiple datasets to compare their characteristics:
-```bash
-for dataset in "allenai/c4" "HuggingFaceFW/fineweb" "cerebras/SlimPajama-627B"; do
-    uv run basic-stats.py $dataset --max-samples 50000
-done
-```
-
-### Quality Checking
-
-Check if your dataset has reasonable statistics before training:
-- Are word counts within expected range?
-- Is the character distribution reasonable?
-- Are there too many special characters (potential quality issues)?
-
-### Setting Filter Thresholds
-
-Use the statistics to inform filtering decisions:
-- If mean word count is 500, you might filter out samples < 50 or > 10,000 words
-- If punctuation ratio is very low, might indicate low-quality text
-- Character type distributions can reveal encoding issues
-
-## Command-Line Options
-
-```
-usage: basic-stats.py [-h] [--split SPLIT] [--text-column TEXT_COLUMN]
-                      [--max-samples MAX_SAMPLES] [--per-sample]
-                      [--output-file OUTPUT_FILE] [--streaming]
-                      dataset
-
-positional arguments:
-  dataset               Dataset name (e.g., 'HuggingFaceFW/fineweb-edu') or local path
-
-optional arguments:
-  -h, --help            show this help message and exit
-  --split SPLIT         Dataset split to process (default: train)
-  --text-column TEXT_COLUMN
-                        Name of the text column (default: text)
-  --max-samples MAX_SAMPLES
-                        Maximum number of samples to process (for testing)
-  --per-sample          Save per-sample statistics to CSV file
-  --output-file OUTPUT_FILE
-                        Output file for per-sample stats (default: dataset-stats.csv)
-  --streaming           Use streaming mode (default: True)
-```
-
-## Technical Details
-
-### Welford's Algorithm
-
-The script uses Welford's algorithm for calculating streaming mean and variance. This provides:
-- Numerical stability (no catastrophic cancellation)
-- Constant memory usage (O(1))
-- Single-pass computation
-- Accurate results even for very large datasets
-
-### Character Type Classification
-
-Character types are classified as:
-- **Alphanumeric**: Letters + digits
-- **Alphabetic**: Letters only
-- **Digit**: Numbers (0-9)
-- **Uppercase/Lowercase**: Case ratios (relative to total letters)
-- **Whitespace**: Spaces, tabs, newlines
-- **Punctuation**: Standard ASCII punctuation
-- **Special**: Everything else (emojis, symbols, etc.)
-
-### Sentence Counting
-
-Simple heuristic-based sentence boundary detection using `.!?` as terminators. This is fast but not as accurate as NLP-based sentence tokenization. Good enough for statistical analysis.
-
----
-
 ### `finepdfs-stats.py` - Temporal Educational Quality Analysis
 
 Analyze educational quality trends across CommonCrawl dumps using Polars streaming. Answers: **"Is the web getting more educational over time?"**
 
 **Features:**
-- ✅ Polars streaming (no download of 300GB+ dataset)
-- ✅ Temporal analysis across 106 CommonCrawl dumps (2013-2025)
-- ✅ ASCII chart visualizations
-- ✅ Uploads results to HF Hub with auto-generated dataset card
-- ✅ Supports single language or all 70+ languages
+- Polars streaming (no download of 300GB+ dataset)
+- Temporal analysis across 106 CommonCrawl dumps (2013-2025)
+- ASCII chart visualizations
+- Uploads results to HF Hub with auto-generated dataset card
+- Supports single language or all 70+ languages
 
 **Quick Examples:**
 
@@ -281,34 +58,22 @@ hf jobs uv run \
 - Single scan using Polars streaming
 - Works on HF Jobs CPU instances
 
----
-
 ## Related Scripts
 
-Check out other scripts in the `uv-scripts` organization:
+Check out other scripts in the [uv-scripts organization](https://huggingface.co/uv-scripts):
 - **dataset-creation**: Create datasets from PDFs and other formats
 - **vllm**: GPU-accelerated classification and inference
 - **ocr**: Document OCR using vision-language models
 
-## Contributing
-
-Have ideas for additional statistics or improvements? Feel free to:
-1. Fork this repository
-2. Add your script or improvements
-3. Submit a pull request
-
-Or open an issue on the [uv-scripts organization](https://huggingface.co/uv-scripts).
-
-## License
-
-Apache 2.0
-
 ## Why UV Scripts?
 
 UV scripts are self-contained Python scripts that:
 - Run with a single `uv run` command (no setup required)
 - Include all dependencies in PEP 723 inline metadata
 - Work seamlessly on both local machines and HF Jobs
-- Serve as educational examples of best practices
 
 Learn more about UV: https://docs.astral.sh/uv/
+
+## License
+
+Apache 2.0