# frontmatter
------------------------------------------------------------------------
source
### nb_frontmatter
``` python
def nb_frontmatter(
nb
):
```
*Get frontmatter dict from `nb` without modifying cells*
------------------------------------------------------------------------
source
### FrontmatterProc
``` python
def FrontmatterProc(
nb
):
```
*A YAML and formatted-markdown frontmatter processor*
YAML frontmatter can be added to notebooks in one of two ways:
1. By adding a raw notebook cell with `---` as the first and last
lines, and YAML between them, or
2. A specially formatted markdown cell. The first line should be start
with a single `#` (creating an H1 heading), and becomes the title.
Then, optionally, a line beginning with `>` (creating a quote
block), which becomes the description. Finally, zero or more lines
beginning with `-` (creating a list), each of which contains YAML.
(If you already have “title” defined in frontmatter in a raw cell,
then markdown cells will be ignored.)
For instance, our test notebook contains the following markdown cell:
# a title
> A description
- key1: value1
- key2: value2
- categories: [c1, c2]
It also contains the following raw cell:
---
execute:
echo: false
---
When we process with
[`FrontmatterProc`](https://nbdev.fast.ai/api/frontmatter.html#frontmatterproc),
these will both be removed, and a single raw cell will be added to the
top, containing the combined YAML frontmatter:
``` python
nbp = NBProcessor(_test_file, procs=FrontmatterProc)
nbp.process()
print(nbp.nb.cells[0].source)
```
---
categories:
- c1
- c2
description: A description
execute:
echo: false
key1: value1
key2: value2
output-file: docs_test.html
title: a title
---
In addition, a `frontmatter_` attr will be added to the notebook,
containing this information as a `dict`:
``` python
d = nbp.nb.frontmatter_
d
```
{'execute': {'echo': False},
'title': 'a title',
'description': 'A description',
'key1': 'value1',
'key2': 'value2',
'categories': ['c1', 'c2'],
'output-file': 'docs_test.html'}