Pyracf

23-03-26 15:47:37 - recordtype 0100 -> 23991 records parsed
23-03-26 15:47:37 - recordtype 0505 -> 74802 records parsed
23-03-26 15:47:37 - total parse time: 5.305895 seconds
23-03-26 15:47:37 - Pickle files saved to /tmp/picklepower
>>> r = RACF(pickles='/tmp/picklepower',prefix='OK-')
>>> r.status
{'status': 'Ready', 'input-lines': 98793, 'lines-read': 98793, 'lines-parsed': 98793, 'lines-per-second': 2131548, 'parse-time': 0.046348}


Overall, the latest release of Pyracf offers users greater flexibility and ease-of-use, making it an essential tool for any security or compliance team working with RACF data. We hope you find this update useful, and we welcome your feedback as we continue to improve Pyracf in the future.

To install Pyracf using pip, simply run `pip install pyracf`. You can also install Pyracf from source by cloning the GitHub repository and running python setup.py install. We recommend using Python 3.6 or later, as earlier versions are not supported.

See https://pypi.org/project/pyracf/

Rob van Hoboken's magic, turns a new page.
Parsing speeds soar, selection features sleek,
Empowering analysis, for the data geek.

Faster, smoother, PyRACF races ahead,
Rob's fixes and updates, no stone left unturned.
Pandas by its side, handling data with flair,
A modern duo, for security aware.

Efficient parsing, selection made a breeze,
PyRACF shines bright, with every release.
GRCACC it knows well, safeguarding with might,
PyRACF 0.8.7, security done right.

Install with pip, or GitHub's embrace,
PyRACF 0.8.7, setting the pace.
For insights and safety, in a world of data vast,
PyRACF stands strong, future-proofed and steadfast.


Wiki : https://github.com/wizardofzos/pyracf/wiki
PyPi link: https://pypi.org/project/pyracf/0.8.7/

Pyracf: Now with Pickle Power!

We're excited to announce the latest release of Pyracf, a Python framework that parses IRRDBU00 unloads into Pandas dataframes. With this release, Pyracf now supports saving and loading pickle files, providing users with greater flexibility when working with their RACF data.

One of the key benefits of Pyracf is its integration with Pandas. Using Pandas dataframes, users can easily manipulate and analyze their RACF data, making it simpler and faster to produce detailed reports. With Pyracf's intuitive API, you can quickly generate reports that help you identify potential security risks, monitor user activity, and more.

In addition to its existing features, Pyracf now allows users to save their dataframes as pickle files, which can be loaded at a later time. This can be particularly useful if you're working with large datasets that take a long time to parse. By saving your dataframes as pickle files, you can easily reload them whenever you need them, without having to re-parse the original data.


>>> r = RACF('../doit/NASA_UNLOAD')
>>> r.parse_fancycli(recordtypes=[r.GPBD_RECORDTYPE,r.GRACC_RECORDTYPE],save_pickles='/tmp/picklepower',prefix='OK-')
23-03-26 15:47:31 - parsing ../doit/NASA_UNLOAD
23-03-26 15:47:31 - selected recordtypes: 0100,0505

In the world of mainframes, where security is key,
PyRACF is the tool you need to see.
Parsing and querying RACF with ease,
PyRACF generates DataFrames to please.

With Pandas by its side, it's a powerful combo,
Analyzing security setups, it's the way to go.
Saving and loading pickle files with flair,
Working with large RACF datasets, it's a breeze to pair.

And let's not forget GRCACC, the record that's essential,
Generic resource conditional access, it's truly influential.
PyRACF handles it all, with the fields it knows so well,
Keeping your IBM Z systems safe, PyRACF rings the bell.

So install it with pip, or check out GitHub to see,
PyRACF's the way to go, to keep your security free.

First release of PyRACF.