Metadata-Version: 2.1
Name: data-vault
Version: 0.4.2
Summary: IPython magic for simple, organized, compressed and encrypted: storage & transfer of files between notebooks
Home-page: https://github.com/krassowski/data-vault
Author: Michal Krassowski
Author-email: krassowski.michal+pypi@gmail.com
License: MIT
Description: IPython data-vault
        ==================
        
        |Build Status| |codecov| |CodeQL| |MIT License| |Binder| |DOI|
        
        IPython magic for simple, organized, compressed and encrypted storage &
        transfer of files between notebooks.
        
        Background and demo
        -------------------
        
        Right tool for a simple job
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        The ``%vault`` magic provides a reproducible caching mechanism for
        variables exchange between notebooks. The cache is compressed,
        persistent and safe.
        
        Differently to the builtin ``%store`` magic, the variables are stored in
        plain sight, in a zipped archive, so that they can be easily accessed
        for manual inspection, or for the use by other tools.
        
        Demonstration by usage:
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        Let’s open the vault (it will be created if not here yet):
        
        .. code:: python
        
           %open_vault -p data/storage.zip
        
        Generate some dummy dataset:
        
        .. code:: python
        
           from pandas import DataFrame
           from random import choice, randint
           cities = ['London', 'Delhi', 'Tokyo', 'Lagos', 'Warsaw', 'Chongqing']
           salaries = DataFrame([
               {'salary': randint(0, 100), 'city': choice(cities)}
               for i in range(10000)
           ])
        
        Store variable in a module
        ^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        And store it in the vault:
        
        .. code:: python
        
           %vault store salaries in datasets
        
        ..
        
           Stored salaries (None → 40CA7812) at Sunday, 08. Dec 2019 11:58
        
        A short description is printed out (including a CRC32 hashsum and a
        timestamp) by default, but can be disabled by passing
        ``--timestamp False`` to ``%open_vault`` magic. Even more information
        enhancing the reproducibility is `stored in the cell
        metadata <#metadata-for-storage-operations>`__.
        
        Import variable from a module
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        We can now load the stored DataFrame in another (or the same) notebook:
        
        .. code:: python
        
           %vault import salaries from datasets
        
        ..
        
           Imported salaries (40CA7812) at Sunday, 08. Dec 2019 12:02
        
        Thanks to (optional) `memory optimizations <#memory-optimizations>`__ we
        saved some RAM (87% as compared to unoptimized ``pd.read_csv()``
        result). To track how many MB were saved use ``--report_memory_gain``
        setting which will display memory optimization results below imports,
        for example:
        
           Reduced memory usage by 87.28%, from 0.79 MB to 0.10 MB.
        
        Import variable as something else
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        If we already have the salaries variable, we can use ``as``, just like
        in the Python import system.
        
        .. code:: python
        
           %vault import salaries from datasets as salaries_dataset
        
        Store or import with a custom function
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code:: python
        
           from pandas import read_csv
           to_csv = lambda df: df.to_csv()
           %vault store salaries in datasets with to_csv as salaries_csv
           %vault import salaries_csv from datasets with read_csv
        
        Import an arbitrary file
        ^^^^^^^^^^^^^^^^^^^^^^^^
        
        .. code:: python
        
           from pandas import read_excel
           %vault import 'cars.xlsx' as cars_dataset with read_excel
        
        More examples are available in the
        `Examples.ipynb <https://github.com/krassowski/data-vault/blob/master/Example.ipynb>`__
        notebook, which can be `run interactively in the
        browser <https://mybinder.org/v2/gh/krassowski/data-vault/master?filepath=Example.ipynb>`__.
        
        Goals
        ~~~~~
        
        Syntax: - easy to understand in plain language (avoid abbreviations when
        possible), - while intuitive for Python developers, - …but sufficiently
        different so that it would not be mistaken with Python constructs - for
        example, we could have ``%from x import y``, but this looks very like
        normal Python; having ``%vault from x import y`` makes it sufficiently
        easy to distinguish - star imports are better avoided, thus not
        supported - as imports may be confusing if there is more than one
        
        Reproducibility: - promote good reproducible and traceable organization
        of files: - promote storage in plain text files and the use of DataFrame
        > pickling is often an easy solution, but it can cause hurtful problems
        in prototyping phase (which is what notebooks are often used for): if
        you pickle you objects, then change the class definition and attempt to
        load your data again you are likely to fail severly; this is why the
        plain text files are the default option in this package (but pickling is
        supported too!). - print out a short hashsum and human-readable datetime
        (always in UTC), - while providing even more details in cell metadata -
        allow to trace instances of the code being modified post execution
        
        Security:
        
        -  think of it as a tool to minimize the damage in case of accidental
           ``git add`` of data files (even if those should have been elsewhere
           and ``.gitignore``\ d in the first place),
        -  or, as an additional layer of security for already anonymized data,
        -  but this tool is **not** aimed at facilitating the storage of highly
           sensitive data
        -  you have to set a password, or explicitly set ``--secure False`` to
           get rid of a security warning
        
        Features overview
        -----------------
        
        Metadata for storage operations
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Each operation will print out the timestamp and the CRC32 short checksum
        of the files involved. The timestamp of the operation is reported in the
        UTC timezone in a human-readable format.
        
        This can be disabled by setting ``-t False`` or ``--timestamp False``,
        however for the sake of reproducibility it is encouraged to keep this
        information visible in the notebook.
        
        More precise information including the SHA256 cheksum (with a lower
        probability of collisions), and a full timestamp (to detect potential
        race condition errors in file write operations) are embedded in the
        metadata of the cell. You can disable this by setting –metadata False.
        
        The exact command line is also stored in the metadata, so that if you
        accidentally modify the code cell without re-running the code, the
        change can be tracked down.
        
        Storage
        ~~~~~~~
        
        In order to enforce interoperability plain text files are used for
        pandas DataFrame and Series objects. Other variables are stores as
        pickle objects. The location of the storage archive on the disk defaults
        to ``storage.zip`` in the current directory, and can changed using
        ``%open_vault`` magic:
        
        .. code:: python
        
           %open_vault -p custom_storage.zip
        
        Encryption
        ^^^^^^^^^^
        
           **The encryption is not intended as a high security mechanism, but
           only as an additional layer of protection for already anonymized
           data.**
        
        The password to encrypt the storage archive is retrieved from the
        environmental variable, using a name provided in ``encryption_variable``
        during the setup.
        
        .. code:: python
        
           %open_vault -e ENV_STORAGE_KEY
        
        Memory optimizations
        ~~~~~~~~~~~~~~~~~~~~
        
        Pandas DataFrames are by-default memory optimized by conversion of
        string variables to (ordered) categorical columns (pandas equivalent of
        R’s factors/levels). Each string column will be tested for the memory
        improvement and the optimization will be only applied if it does reduce
        the memory usage.
        
        Why ZIP and not HDF?
        ~~~~~~~~~~~~~~~~~~~~
        
        The storage archive is conceptually similar to Hierarchical Data Format
        (e.g. HDF5) object - it contains: - a hierarchy of files, and - a
        metadata files
        
        I believe that HDF may be the future, but this future is not here yet -
        numerous issues with the packages handling the HDF files, as well as low
        performance and compression rate prompted me to stay with a simple zip
        format now.
        
        ZIP is a popular file format with known features and limitations - files
        can be password encrypted, while the file list is always accessible.
        This is okay given that the code of the project is assumed to be public,
        and only the files in the storage area are assumed to be of encrypted,
        increasing the security in case of unauthorized access.
        
        As the limitations of the ZIP encryption are assumed to be a common
        knowledge, I hope that managing expectations of the level of security
        offered by this package will be easier.
        
        Installation and requirements
        -----------------------------
        
        Pre-requirements: - Python 3.6+ - 7zip (16.02+) (see
        `below <#installing-7-zip>`__ for Ubuntu and Mac commands)
        
        Installation:
        ~~~~~~~~~~~~~
        
        .. code:: bash
        
           pip3 install data_vault
        
        Installing 7-zip
        ~~~~~~~~~~~~~~~~
        
        Installers for Windows can be downloaded from the `7-zip
        website <https://www.7-zip.org/download.html>`__.
        
        For other systems you can use packages from the default repositories:
        
        Ubuntu
        ^^^^^^
        
        .. code:: bash
        
           sudo apt-get install -y p7zip-full
        
        Mac
        ^^^
        
        .. code:: bash
        
           brew install p7zip
        
        .. |Build Status| image:: https://travis-ci.org/krassowski/data-vault.svg?branch=master
           :target: https://travis-ci.org/krassowski/data-vault
        .. |codecov| image:: https://codecov.io/gh/krassowski/data-vault/branch/master/graph/badge.svg
           :target: https://codecov.io/gh/krassowski/data-vault
        .. |CodeQL| image:: https://github.com/krassowski/data-vault/workflows/CodeQL/badge.svg
        .. |MIT License| image:: https://img.shields.io/badge/license-MIT-blue.svg?style=flat
           :target: http://choosealicense.com/licenses/mit/
        .. |Binder| image:: https://mybinder.org/badge_logo.svg
           :target: https://mybinder.org/v2/gh/krassowski/data-vault/master?filepath=Example.ipynb
        .. |DOI| image:: https://zenodo.org/badge/226589892.svg
           :target: https://zenodo.org/badge/latestdoi/226589892
        
Keywords: jupyter,jupyterlab,notebook,ipython,storage,store,magic,vault
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Framework :: IPython
Classifier: Framework :: Jupyter
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Topic :: Utilities
Classifier: Topic :: Database
Classifier: Topic :: System :: Archiving
Classifier: Topic :: System :: Archiving :: Compression
Classifier: Topic :: Software Development :: User Interfaces
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
