Treating notebooks as data

In [111]:
    from toolz.curried import *
    from pandas import *
    from pathlib import Path
    from nbformat import reads
    from collections import UserDict
    from nbformat.v4 import *
    from nbformat import v4
    from functools import partialmethod
    from operator import methodcaller, ne
    from nbconvert import exporters
    from traitlets import import_item, config
    import json
    from nbformat import NotebookNode
    from IPython.display import *
    from markupsafe import escape
In [96]:
    class Loader(UserDict):
        def __missing__(self, path):
            return None
        def __getitem__(self, path):
            return super().__getitem__(path.suffix)
        def __call__(self, path):
            return self[path](path)
In [97]:
    class Notebook(DataFrame):
        def _constructor(self):
            return type(self)
        _metadata = ['loader', 'meta']
        loader = Loader({
            '.ipynb': compose(reads, Path.read_text)
        def _constructor_sliced(self): 
            return Series
        def __init__(self, data=None, index=None, columns=None, dtype=None, copy=False):
            self.meta, path = {}, None
            if isinstance(data, Path): 
                path, data = data, self.loader(data)
            if isinstance(data, NotebookNode):
                self.meta, data = get(['metadata', 'cells'], data)
            super().__init__(data, index, columns, dtype, copy)
        def __finalize__(self, other=None, method=None):
            if method == 'merge': 
                self.meta.update(**getattr(other.left, 'meta', {}))
            if method == 'concat': 
                for object in other.objs: self.meta.update(**getattr(object, 'meta', {}))
            return self

        def to_notebook(self, to=None):
            nb = new_notebook(
                cells=compose(list, map(self.cellify), pluck(1))(self.iterrows()),
            to and Path(to).write_text(writes(nb))
            return nb

        def raw(self, type='raw'): 
            return df[df.cell_type.eq(type)]
        code, markdown = partialmethod(raw, 'code'), partialmethod(raw, 'markdown')
        def find(self, query=''):
            return self[self.source.str.contains(query)]
        def cellify(dict):
            if isinstance(dict, Series):
                dict = dict.to_dict()
            cell = dict['cell_type']
            if cell == 'code':
                dict['execution_count'] = (
                    if dict['execution_count'] is None or np.isnan(dict['execution_count']) or ne(*[dict['execution_count']]*2)
                    else int(dict['execution_count']))
            if cell == 'markdown':
                del dict['execution_count'], dict['outputs']
            return methodcaller('_'.join(('new', cell, 'cell')), **dict)(v4)
        def preprocess(self, preprocessors=[], resources={}, ):
            nb = self.to_notebook()
            for pre in preprocessors:
                if isinstance(pre, str):
                    pre = import_item(pre)
                if callable(pre): 
                    pre = pre()
                nb, resources = pre.preprocess(nb, resources)
            return Notebook(nb)
        def append(self, source, type='code', **kwargs):
            return super().append(
                methodcaller('_'.join(('new', type, 'cell')), source, **kwargs)(v4), 
        def to_export(self, name='html', **kwargs):
            return compose(first, exporters.export)(
                self.to_notebook(), **kwargs)
for name in exporters.get_export_names(): 'to_'+name not in dir(Notebook) and setattr(
    Notebook, 'to_'+name, partialmethod(Notebook.to_export, name))
In [98]:
    def read_notebooks(paths=['.'], suffixes=['.ipynb']):
        if not isiterable(paths): paths = [paths]
        return concat({
            notebook: excepts(
                lambda e: Series({})
            for path in map(Path, paths) for notebook in (
                    *(path.glob('*'+suffix) for suffix in suffixes)
                ) if path.is_dir() else path if isiterable(path)
                else [path])})

Create a Notebook from a DataFrame

In [99]:
        .append("a = 42")

Load the Notebook

In [100]:
    nb = read_notebooks([Path('part.ipynb')])

Manually append source code to it

In [101]:
    new = (
cell_type execution_count metadata outputs source
0 code None {} [] a = 42
1 code None {} [] print(a)
2 code None {} [] __import__("IPython").display.HTML(str(a))

Execute the code

In [102]:
cell_type execution_count metadata outputs source
0 code 1 {} [] a = 42
1 code 2 {} [{'output_type': 'stream', 'text': '42 ', 'nam... print(a)
2 code 3 {} [{'metadata': {}, 'output_type': 'execute_resu... __import__("IPython").display.HTML(str(a))

Modify the source and rerun the code

In [118]:
new2 = new.copy()
new2.source = new.source.str.replace('42', '100')
newest = new2.preprocess(['nbconvert.preprocessors.execute.ExecutePreprocessor'])

Creating Slides

In [119]:
def make_slide(object):
    return object.update(slideshow=dict(slide_type='slide')) or object
In [120]:
0    {'slideshow': {'slide_type': 'slide'}}
1    {'slideshow': {'slide_type': 'slide'}}
2    {'slideshow': {'slide_type': 'slide'}}
Name: metadata, dtype: object

Export the slides with a specific configuration.

In [121]:
slides = newest.to_slides(config=traitlets.config.Config(
    SlidesExporter=dict(reveal_url_prefix="[email protected]")))

Use srcdoc to embed an IFramed presentation.

In [122]:
    """<iframe srcdoc="{}" height="400" width="600"></iframe>""".format, markupsafe.escape