Package: papermill
papermillis a tool for parameterizing, executing, and analyzing
Jupyter Notebooks.Papermill lets you:parameterizenotebooksexecutenotebooksThis 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 parametersmakes 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 programmaticallyexecute a workflowwithout having to copy and paste from
notebook to notebook manually.Papermill takes anopinionatedapproach to notebook parameterization and
execution based on our experiences using notebooks at scale in data
pipelines.InstallationFrom the command line:pip install papermillFor all optional io dependencies, you can specify individual bundles
likes3, orazure-- or useall. To use Black to format parameters you can add as an extra requires ['black'].pip install papermill[all]Python Version SupportThis library currently supports Python 3.8+ versions. As minor Python
versions are officially sunset by the Python org papermill will similarly
drop support in the future.UsageParameterizing a NotebookTo parameterize your notebook designate a cell with the tagparameters.Papermill looks for theparameterscell and treats this cell as defaults for the parameters passed in at execution time. Papermill will add a new cell tagged withinjected-parameterswith input parameters in order to overwrite the values inparameters. If no cell is tagged withparametersthe injected cell will be inserted at the top of the notebook.Additionally, if you rerun notebooks through papermill and it will reuse theinjected-parameterscell from the prior run. In this case Papermill will replace the oldinjected-parameterscell with the new run's inputs.Executing a NotebookThe 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 APIimport papermill as pm

pm.execute_notebook(
   'path/to/input.ipynb',
   'path/to/output.ipynb',
   parameters = dict(alpha=0.6, ratio=0.1)
)Execute via CLIHere's an example of a local notebook being executed and output to an
Amazon S3 account:$ papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1NOTE:If you use multiple AWS accounts, and you haveproperly configured your AWS  credentials, then you can specify which account to use by setting theAWS_PROFILEenvironment variable at the command-line. For example:$ AWS_PROFILE=dev_account papermill local/input.ipynb s3://bkt/output.ipynb -p alpha 0.6 -p l1_ratio 0.1In the above example, two parameters are set:alphaandl1_ratiousing-p(--parametersalso works). Parameter values that look like booleans or numbers will be interpreted as such. Here are the different ways users may set parameters:$ papermill local/input.ipynb s3://bkt/output.ipynb -r version 1.0Using-ror--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.$ papermill local/input.ipynb s3://bkt/output.ipynb -f parameters.yamlUsing-for--parameters_file, users can provide a YAML file from which parameter values should be read.$ papermill local/input.ipynb s3://bkt/output.ipynb -y "
alpha: 0.6
l1_ratio: 0.1"Using-yor--parameters_yaml, users can directly provide a YAML string containing parameter values.$ papermill local/input.ipynb s3://bkt/output.ipynb -b YWxwaGE6IDAuNgpsMV9yYXRpbzogMC4xCg==Using-bor--parameters_base64, users can provide a YAML string, base64-encoded, containing parameter values.When using YAML to pass arguments, through-y,-bor-f, parameter values can be arrays or dictionaries:$ 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 HandlersPapermill supports the following name handlers for input and output paths during execution:Local file system:localHTTP, HTTPS protocol:http://, https://Amazon Web Services:AWS S3s3://Azure:Azure DataLake Store,Azure Blob Storeadl://, abs://Google Cloud:Google Cloud Storagegs://Development GuideReadCONTRIBUTING.mdfor guidelines on how to setup a local development environment and make code changes back to Papermill.For development guidelines look in theDEVELOPMENT_GUIDE.mdfile. This should inform you on how to make particular additions to the code base.DocumentationWe host thePapermill documentationon ReadTheDocs.