<jupyter_start><jupyter_text>Value of a NotebookA notebook is source code and narrative. Both humans and machines read linearly.--- Readability 🤓One Machine Reusability 👩‍💻      👨‍💻A Few Machines Reproducibility [👩‍🚀](https://github.com/chrislgarry/Apollo-11)[👨‍🚀](http://vimeo.com/28199826)Many Machines<jupyter_code>%reload_ext literacy.template
# The notebook is data.

    from nbformat import reads
    from pandas import *
    from pathlib import Path
    
    df = concat({
        path: DataFrame(reads(path.read_text(), 4).cells)
        for path in Path('.').glob('*.ipynb')
    })
    df
# Output Formats

# nbconvert

Jupyter renderers
# Restart and Run All
# Importing Notebook

## Using nbconvert

## Using run<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11671087533156499, 'fraction_numerical': 0.014588859416445624, 'mean_word_length': 3.870967741935484, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 1, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '596662', 'license': 'BSD-3-Clause', 'n_tokens_mistral': 287, 'n_tokens_neox': 271, 'n_words': 68, 'path': 'literacy/value.ipynb', 'repo_name': 'tonyfast/literacy', 'size': '754'}