1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
|
Source: python-papermill
Maintainer: Debian Python Team <team+python@tracker.debian.org>
Uploaders:
Debian PaN Maintainers <debian-pan-maintainers@alioth-lists.debian.net>,
Roland Mas <lolando@debian.org>,
Section: python
Priority: optional
Build-Depends:
debhelper-compat (= 13),
dh-sequence-python3,
help2man,
pybuild-plugin-pyproject,
python3-aiohttp <!nocheck>,
python3-all,
python3-azure <!nocheck>,
python3-azure-datalake-store <!nocheck>,
python3-boto3 <!nocheck>,
python3-click <!nocheck>,
python3-colors <!nocheck>,
python3-dateutil <!nocheck>,
python3-ipykernel <!nocheck>,
python3-moto <!nocheck>,
python3-nbclient <!nocheck>,
python3-nbformat <!nocheck>,
python3-pytest <!nocheck>,
python3-requests <!nocheck>,
python3-tenacity <!nocheck>,
python3-tqdm <!nocheck>,
Standards-Version: 4.7.0
Testsuite: autopkgtest-pkg-pybuild
Homepage: https://github.com/nteract/papermill/
Vcs-Browser: https://salsa.debian.org/python-team/packages/python-papermill
Vcs-Git: https://salsa.debian.org/python-team/packages/python-papermill.git
Package: python3-papermill
Architecture: all
Depends:
${misc:Depends},
${python3:Depends},
Description: Parameterize, execute, and analyze notebooks
Papermill is a tool for parameterizing, executing, and analyzing
Jupyter Notebooks. It lets you parameterize notebooks and execute notebooks.
.
This opens up new opportunities for how notebooks can be used. For example:
.
* Perhaps you have a financial report that you wish to run with
different values on the first or last day of a month or at the
beginning or end of the year, using parameters makes this task
easier.
.
* Do you want to run a notebook and depending on its results, choose
a particular notebook to run next? You can now programmatically
execute a workflow without having to copy and paste from notebook to
notebook manually.
.
Papermill takes an opinionated approach to notebook parameterization
and execution based on our experiences using notebooks at scale in
data pipelines.
|
