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|
%global _empty_manifest_terminate_build 0
Name: python-papermill
Version: 2.4.0
Release: 1
Summary: Parametrize and run Jupyter and nteract Notebooks
License: BSD
URL: https://github.com/nteract/papermill
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b8/c9/56294fddb4b19b074fece0c277cbe9159501d7bb2e143f477e6d776342ae/papermill-2.4.0.tar.gz
BuildArch: noarch
Requires: python3-ansiwrap
Requires: python3-click
Requires: python3-pyyaml
Requires: python3-nbformat
Requires: python3-nbclient
Requires: python3-tqdm
Requires: python3-requests
Requires: python3-entrypoints
Requires: python3-tenacity
Requires: python3-boto3
Requires: python3-azure-datalake-store
Requires: python3-azure-storage-blob
Requires: python3-requests
Requires: python3-gcsfs
Requires: python3-pyarrow
Requires: python3-black
Requires: python3-azure-datalake-store
Requires: python3-azure-storage-blob
Requires: python3-requests
Requires: python3-black
Requires: python3-boto3
Requires: python3-botocore
Requires: python3-codecov
Requires: python3-coverage
Requires: python3-google-compute-engine
Requires: python3-ipython
Requires: python3-ipywidgets
Requires: python3-notebook
Requires: python3-moto
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-mock
Requires: python3-pytest-env
Requires: python3-requests
Requires: python3-check-manifest
Requires: python3-attrs
Requires: python3-pre-commit
Requires: python3-flake8
Requires: python3-tox
Requires: python3-bumpversion
Requires: python3-recommonmark
Requires: python3-pip
Requires: python3-wheel
Requires: python3-setuptools
Requires: python3-twine
Requires: python3-azure-datalake-store
Requires: python3-azure-storage-blob
Requires: python3-gcsfs
Requires: python3-pyarrow
Requires: python3-black
Requires: python3-gcsfs
Requires: python3-PyGithub
Requires: python3-pyarrow
Requires: python3-boto3
Requires: python3-boto3
Requires: python3-botocore
Requires: python3-codecov
Requires: python3-coverage
Requires: python3-google-compute-engine
Requires: python3-ipython
Requires: python3-ipywidgets
Requires: python3-notebook
Requires: python3-moto
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-mock
Requires: python3-pytest-env
Requires: python3-requests
Requires: python3-check-manifest
Requires: python3-attrs
Requires: python3-pre-commit
Requires: python3-flake8
Requires: python3-tox
Requires: python3-bumpversion
Requires: python3-recommonmark
Requires: python3-pip
Requires: python3-wheel
Requires: python3-setuptools
Requires: python3-twine
Requires: python3-azure-datalake-store
Requires: python3-azure-storage-blob
Requires: python3-gcsfs
Requires: python3-pyarrow
Requires: python3-black
%description
<!---(binder links generated at https://mybinder.readthedocs.io/en/latest/howto/badges.html and compressed at https://tinyurl.com) -->
[](https://github.com/nteract/papermill/actions/workflows/ci.yml)
[](https://github.com/nteract/papermill/actions/workflows/ci.yml)
[](https://codecov.io/github/nteract/papermill?branch=main)
[](http://papermill.readthedocs.io/en/latest/?badge=latest)
[](https://mybinder.org/v2/gh/nteract/papermill/main?filepath=binder%2Fprocess_highlight_dates.ipynb)
[](https://mybinder.org/v2/gh/nteract/papermill/main?)
[](https://pypi.org/project/papermill/)
[](https://github.com/ambv/black)
[](https://snyk.io/advisor/python/papermill)
[](https://anaconda.org/conda-forge/papermill)
**papermill** is a tool for parameterizing, executing, and analyzing
Jupyter Notebooks.
Papermill lets you:
- **parameterize** notebooks
- **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.
## Installation
From the command line:
``` {.sourceCode .bash}
pip install papermill
```
For all optional io dependencies, you can specify individual bundles
like `s3`, or `azure` -- or use `all`. To use Black to format parameters you can add as an extra requires ['black'].
``` {.sourceCode .bash}
pip install papermill[all]
```
## Python Version Support
This library currently supports Python 3.7+ versions. As minor Python
versions are officially sunset by the Python org papermill will similarly
drop support in the future.
## Usage
### Parameterizing a Notebook
To parameterize your notebook designate a cell with the tag ``parameters``.
Papermill looks for the ``parameters`` cell and treats this cell as defaults for the parameters passed in at execution time. Papermill will add a new cell tagged with ``injected-parameters`` with input parameters in order to overwrite the values in ``parameters``. If no cell is tagged with ``parameters`` the injected cell will be inserted at the top of the notebook.
Additionally, if you rerun notebooks through papermill and it will reuse the ``injected-parameters`` cell from the prior run. In this case Papermill will replace the old ``injected-parameters`` cell with the new run's inputs.
### Executing a Notebook
The two ways to execute the notebook with parameters are: (1) through
the Python API and (2) through the command line interface.
#### Execute via the Python API
``` {.sourceCode .python}
import papermill as pm
pm.execute_notebook(
'path/to/input.ipynb',
'path/to/output.ipynb',
parameters = dict(alpha=0.6, ratio=0.1)
)
```
#### Execute via CLI
Here's an example of a local notebook being executed and output to an
Amazon S3 account:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
**NOTE:**
If you use multiple AWS accounts, and you have [properly configured your AWS credentials](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html), then you can specify which account to use by setting the `AWS_PROFILE` environment variable at the command-line. For example:
``` {.sourceCode .bash}
$ AWS_PROFILE=dev_account papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
In the above example, two parameters are set: ``alpha`` and ``l1_ratio`` using ``-p`` (``--parameters`` also works). Parameter values that look like booleans or numbers will be interpreted as such. Here are the different ways users may set parameters:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -r version 1.0
```
Using ``-r`` or ``--parameters_raw``, users can set parameters one by one. However, unlike ``-p``, the parameter will remain a string, even if it may be interpreted as a number or boolean.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -f parameters.yaml
```
Using ``-f`` or ``--parameters_file``, users can provide a YAML file from which parameter values should be read.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
alpha: 0.6
l1_ratio: 0.1"
```
Using ``-y`` or ``--parameters_yaml``, users can directly provide a YAML string containing parameter values.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -b YWxwaGE6IDAuNgpsMV9yYXRpbzogMC4xCg==
```
Using ``-b`` or ``--parameters_base64``, users can provide a YAML string, base64-encoded, containing parameter values.
When using YAML to pass arguments, through ``-y``, ``-b`` or ``-f``, parameter values can be arrays or dictionaries:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
x:
- 0.0
- 1.0
- 2.0
- 3.0
linear_function:
slope: 3.0
intercept: 1.0"
```
#### Supported Name Handlers
Papermill supports the following name handlers for input and output paths during execution:
* Local file system: `local`
* HTTP, HTTPS protocol: `http://, https://`
* Amazon Web Services: [AWS S3](https://aws.amazon.com/s3/) `s3://`
* Azure: [Azure DataLake Store](https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-overview), [Azure Blob Store](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-overview) `adl://, abs://`
* Google Cloud: [Google Cloud Storage](https://cloud.google.com/storage/) `gs://`
%package -n python3-papermill
Summary: Parametrize and run Jupyter and nteract Notebooks
Provides: python-papermill
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-papermill
<!---(binder links generated at https://mybinder.readthedocs.io/en/latest/howto/badges.html and compressed at https://tinyurl.com) -->
[](https://github.com/nteract/papermill/actions/workflows/ci.yml)
[](https://github.com/nteract/papermill/actions/workflows/ci.yml)
[](https://codecov.io/github/nteract/papermill?branch=main)
[](http://papermill.readthedocs.io/en/latest/?badge=latest)
[](https://mybinder.org/v2/gh/nteract/papermill/main?filepath=binder%2Fprocess_highlight_dates.ipynb)
[](https://mybinder.org/v2/gh/nteract/papermill/main?)
[](https://pypi.org/project/papermill/)
[](https://github.com/ambv/black)
[](https://snyk.io/advisor/python/papermill)
[](https://anaconda.org/conda-forge/papermill)
**papermill** is a tool for parameterizing, executing, and analyzing
Jupyter Notebooks.
Papermill lets you:
- **parameterize** notebooks
- **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.
## Installation
From the command line:
``` {.sourceCode .bash}
pip install papermill
```
For all optional io dependencies, you can specify individual bundles
like `s3`, or `azure` -- or use `all`. To use Black to format parameters you can add as an extra requires ['black'].
``` {.sourceCode .bash}
pip install papermill[all]
```
## Python Version Support
This library currently supports Python 3.7+ versions. As minor Python
versions are officially sunset by the Python org papermill will similarly
drop support in the future.
## Usage
### Parameterizing a Notebook
To parameterize your notebook designate a cell with the tag ``parameters``.
Papermill looks for the ``parameters`` cell and treats this cell as defaults for the parameters passed in at execution time. Papermill will add a new cell tagged with ``injected-parameters`` with input parameters in order to overwrite the values in ``parameters``. If no cell is tagged with ``parameters`` the injected cell will be inserted at the top of the notebook.
Additionally, if you rerun notebooks through papermill and it will reuse the ``injected-parameters`` cell from the prior run. In this case Papermill will replace the old ``injected-parameters`` cell with the new run's inputs.
### Executing a Notebook
The two ways to execute the notebook with parameters are: (1) through
the Python API and (2) through the command line interface.
#### Execute via the Python API
``` {.sourceCode .python}
import papermill as pm
pm.execute_notebook(
'path/to/input.ipynb',
'path/to/output.ipynb',
parameters = dict(alpha=0.6, ratio=0.1)
)
```
#### Execute via CLI
Here's an example of a local notebook being executed and output to an
Amazon S3 account:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
**NOTE:**
If you use multiple AWS accounts, and you have [properly configured your AWS credentials](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html), then you can specify which account to use by setting the `AWS_PROFILE` environment variable at the command-line. For example:
``` {.sourceCode .bash}
$ AWS_PROFILE=dev_account papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
In the above example, two parameters are set: ``alpha`` and ``l1_ratio`` using ``-p`` (``--parameters`` also works). Parameter values that look like booleans or numbers will be interpreted as such. Here are the different ways users may set parameters:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -r version 1.0
```
Using ``-r`` or ``--parameters_raw``, users can set parameters one by one. However, unlike ``-p``, the parameter will remain a string, even if it may be interpreted as a number or boolean.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -f parameters.yaml
```
Using ``-f`` or ``--parameters_file``, users can provide a YAML file from which parameter values should be read.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
alpha: 0.6
l1_ratio: 0.1"
```
Using ``-y`` or ``--parameters_yaml``, users can directly provide a YAML string containing parameter values.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -b YWxwaGE6IDAuNgpsMV9yYXRpbzogMC4xCg==
```
Using ``-b`` or ``--parameters_base64``, users can provide a YAML string, base64-encoded, containing parameter values.
When using YAML to pass arguments, through ``-y``, ``-b`` or ``-f``, parameter values can be arrays or dictionaries:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
x:
- 0.0
- 1.0
- 2.0
- 3.0
linear_function:
slope: 3.0
intercept: 1.0"
```
#### Supported Name Handlers
Papermill supports the following name handlers for input and output paths during execution:
* Local file system: `local`
* HTTP, HTTPS protocol: `http://, https://`
* Amazon Web Services: [AWS S3](https://aws.amazon.com/s3/) `s3://`
* Azure: [Azure DataLake Store](https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-overview), [Azure Blob Store](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-overview) `adl://, abs://`
* Google Cloud: [Google Cloud Storage](https://cloud.google.com/storage/) `gs://`
%package help
Summary: Development documents and examples for papermill
Provides: python3-papermill-doc
%description help
<!---(binder links generated at https://mybinder.readthedocs.io/en/latest/howto/badges.html and compressed at https://tinyurl.com) -->
[](https://github.com/nteract/papermill/actions/workflows/ci.yml)
[](https://github.com/nteract/papermill/actions/workflows/ci.yml)
[](https://codecov.io/github/nteract/papermill?branch=main)
[](http://papermill.readthedocs.io/en/latest/?badge=latest)
[](https://mybinder.org/v2/gh/nteract/papermill/main?filepath=binder%2Fprocess_highlight_dates.ipynb)
[](https://mybinder.org/v2/gh/nteract/papermill/main?)
[](https://pypi.org/project/papermill/)
[](https://github.com/ambv/black)
[](https://snyk.io/advisor/python/papermill)
[](https://anaconda.org/conda-forge/papermill)
**papermill** is a tool for parameterizing, executing, and analyzing
Jupyter Notebooks.
Papermill lets you:
- **parameterize** notebooks
- **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.
## Installation
From the command line:
``` {.sourceCode .bash}
pip install papermill
```
For all optional io dependencies, you can specify individual bundles
like `s3`, or `azure` -- or use `all`. To use Black to format parameters you can add as an extra requires ['black'].
``` {.sourceCode .bash}
pip install papermill[all]
```
## Python Version Support
This library currently supports Python 3.7+ versions. As minor Python
versions are officially sunset by the Python org papermill will similarly
drop support in the future.
## Usage
### Parameterizing a Notebook
To parameterize your notebook designate a cell with the tag ``parameters``.
Papermill looks for the ``parameters`` cell and treats this cell as defaults for the parameters passed in at execution time. Papermill will add a new cell tagged with ``injected-parameters`` with input parameters in order to overwrite the values in ``parameters``. If no cell is tagged with ``parameters`` the injected cell will be inserted at the top of the notebook.
Additionally, if you rerun notebooks through papermill and it will reuse the ``injected-parameters`` cell from the prior run. In this case Papermill will replace the old ``injected-parameters`` cell with the new run's inputs.
### Executing a Notebook
The two ways to execute the notebook with parameters are: (1) through
the Python API and (2) through the command line interface.
#### Execute via the Python API
``` {.sourceCode .python}
import papermill as pm
pm.execute_notebook(
'path/to/input.ipynb',
'path/to/output.ipynb',
parameters = dict(alpha=0.6, ratio=0.1)
)
```
#### Execute via CLI
Here's an example of a local notebook being executed and output to an
Amazon S3 account:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
**NOTE:**
If you use multiple AWS accounts, and you have [properly configured your AWS credentials](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html), then you can specify which account to use by setting the `AWS_PROFILE` environment variable at the command-line. For example:
``` {.sourceCode .bash}
$ AWS_PROFILE=dev_account papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1
```
In the above example, two parameters are set: ``alpha`` and ``l1_ratio`` using ``-p`` (``--parameters`` also works). Parameter values that look like booleans or numbers will be interpreted as such. Here are the different ways users may set parameters:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -r version 1.0
```
Using ``-r`` or ``--parameters_raw``, users can set parameters one by one. However, unlike ``-p``, the parameter will remain a string, even if it may be interpreted as a number or boolean.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -f parameters.yaml
```
Using ``-f`` or ``--parameters_file``, users can provide a YAML file from which parameter values should be read.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
alpha: 0.6
l1_ratio: 0.1"
```
Using ``-y`` or ``--parameters_yaml``, users can directly provide a YAML string containing parameter values.
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -b YWxwaGE6IDAuNgpsMV9yYXRpbzogMC4xCg==
```
Using ``-b`` or ``--parameters_base64``, users can provide a YAML string, base64-encoded, containing parameter values.
When using YAML to pass arguments, through ``-y``, ``-b`` or ``-f``, parameter values can be arrays or dictionaries:
``` {.sourceCode .bash}
$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
x:
- 0.0
- 1.0
- 2.0
- 3.0
linear_function:
slope: 3.0
intercept: 1.0"
```
#### Supported Name Handlers
Papermill supports the following name handlers for input and output paths during execution:
* Local file system: `local`
* HTTP, HTTPS protocol: `http://, https://`
* Amazon Web Services: [AWS S3](https://aws.amazon.com/s3/) `s3://`
* Azure: [Azure DataLake Store](https://docs.microsoft.com/en-us/azure/data-lake-store/data-lake-store-overview), [Azure Blob Store](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-overview) `adl://, abs://`
* Google Cloud: [Google Cloud Storage](https://cloud.google.com/storage/) `gs://`
%prep
%autosetup -n papermill-2.4.0
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-papermill -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 2.4.0-1
- Package Spec generated
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