#183 Need a beautiful database editor? Look to the Bees!
Python Bytes - Podcast tekijän mukaan Michael Kennedy and Brian Okken - Maanantaisin
Kategoriat:
Sponsored by DigitalOcean: pythonbytes.fm/digitalocean
Special guest: Calvin Hendryx-Parker @calvinhp
Brian #1: fastpages: An easy to use blogging platform, with enhanced support for Jupyter Notebooks.
- Uses GH actions to Jekyll blog posts on GitHub Pages.
- Create posts with code, output of code, formatted text, directory from Jupyter Notebooks.
- Altair interactive visualizations
- Collapsible code cells that can be open or closed by default.
- Metadata like title, summary, in special markdown cells.
- twitter cards and YouTube videos
- tags support
- Support for pure markdown posts
- and even MS Word docs for posts. (but really, don’t).
- Documentation and introduction written in fastpages itself, https://fastpages.fast.ai/
Michael #2: BeeKeeper Studio Open Source SQL Editor and Database Manager
- Use Beekeeper Studio to query and manage your relational databases, like MySQL, Postgres, SQLite, and SQL Server.
- Runs on all the things (Windows, Linux, macOS)
- Features
- Autocomplete SQL query editor with syntax highlighting
- Tabbed interface, so you can multitask
- Sort and filter table data to find just what you need
- Sensible keyboard-shortcuts
- Save queries for later
- Query run-history, so you can find that one query you got working 3 days ago
- Default dark theme
- Connect: Alongside normal connections you can encrypt your connection with SSL, or tunnel through SSH. Save a connection password and Beekeeper Studio will make sure to encrypt it to keep it safe.
- SQL Auto Completion: Built-in editor provides syntax highlighting and auto-complete suggestions for your tables so you can work quickly and easily.
- Open Lots of Tabs: Open dozens of tabs so you can write multiple queries and tables in tandem without having to switch windows.
- Save queries
- View Table Data: Tables get their own tabs too! Use our table view to sort and filter results by column.
Calvin #3: 2nd Annual Python Web Conference
- The most in-depth Python conference for web developers
- Targeted at production users of Python
- Talks on Django, Flask, Twisted, Testing, SQLAlchemy, Containers, Deployment and more
- June 17th-19th — One day of tutorials and two days of talks in 3 tracks
- Keynote talks by
- Lorena Mesa
- Hynek Schlawack
- Russell Keith-Magee
- Steve Flanders
- Fireside Chat with Carl Meyer about Instragram’s infrastructure, best practices
- Participate in 40+ presentations and 6 tutorials
- Fun will be had and connections made
- Virtual cocktails
- Online gaming
- Board game night
- Tickets are $199 and $99 for Students
- As a bonus, for every Professional ticket purchased, we'll donate a ticket to an attendee in a developing country.
- As a Python Bytes listener you can get a 20% discount with the code PB20
Brian #4: Mimesis - Fake Data Generator
- “…helps generate big volumes of fake data for a variety of purposes in a variety of languages.”
- Custom and generic data providers
- >33 locales
- Lots of locale dependent providers, like address, Food, Person, …
- Locale independent providers.
- Super fast. Benchmarking with 10k full names was like 60x faster than Faker.
- Data generation by schema. Very cool
>>> from mimesis.schema import Field, Schema
>>> _ = Field('en')
>>> description = (
... lambda: {
... 'id': _('uuid'),
... 'name': _('text.word'),
... 'version': _('version', pre_release=True),
... 'timestamp': _('timestamp', posix=False),
... 'owner': {
... 'email': _('person.email', domains=['test.com'], key=str.lower),
... 'token': _('token_hex'),
... 'creator': _('full_name'),
... },
... }
... )
>>> schema = Schema(schema=description)
>>> schema.create(iterations=1)
- Output:
[
{
"owner": {
"email": "[email protected]",
"token": "cc8450298958f8b95891d90200f189ef591cf2c27e66e5c8f362f839fcc01370",
"creator": "Veronika Dyer"
},
"name": "widget",
"version": "4.3.1-rc.5",
"id": "33abf08a-77fd-1d78-86ae-04d88443d0e0",
"timestamp": "2018-07-29T15:25:02Z"
}
]
Michael #5: Schemathesis
- A tool for testing your web applications built with Open API / Swagger specifications.
- Supported specification versions:
- Swagger 2.0
- Open API 3.0.x
- Built with:
- It reads the application schema and generates test cases which will ensure that your application is compliant with its schema.
- Use: There are two basic ways to use Schemathesis:
- CLI supports passing options to
hypothesis.settings
. - To speed up the testing process Schemathesis provides
-w/--workers
option for concurrent test execution - If you'd like to test your web app (Flask or AioHTTP for example) then there is
--app
option for you - Schemathesis CLI also available as a docker image
- Code example:
import requests
import schemathesis
schema = schemathesis.from_uri("http://0.0.0.0:8080/swagger.json")
@schema.parametrize()
def test_no_server_errors(case):
# `requests` will make an appropriate call under the hood
response = case.call() # use `call_wsgi` if you used `schemathesis.from_wsgi`
# You could use built-in checks
case.validate_response(response)
# Or assert the response manually
assert response.status_code < 500
Calvin #6: Finding secrets by decompiling Python bytecode in public repositories
- Jesse’s initial research revealed that thousands of GitHub repositories contain secrets hidden inside their bytecode.
- It has been common practice to store secrets in Python files that are typically ignored such as
settings.py
,config.py
orsecrets.py
, but this is potentially insecure - Includes a nice crash course on Python byte code and cached source
- This post comes with a small capture-the-flag style lab for you to try out this style of attack yourself.
- You can find it at https://github.com/veggiedefender/pyc-secret-lab/
- Look through your repositories for loose
.pyc
files, and delete them - If you have
.pyc
files and they contain secrets, then revoke and rotate your secrets - Use a standard gitignore to prevent checking in
.pyc
files - Use JSON files or environment variables for configuration
Extras:
Michael:
- Python 3.9.0b1 Is Now Available for Testing
- Python 3.8.3 Is Now Available
- Ventilators and Python: Some particle physicists put some of their free time to design and build a low-cost ventilator for covid-19 patients for use in hospitals. https://arxiv.org/pdf/2003.10405.pdf Search of the PDF for Python:
- "Target computing platform: Raspberry Pi 4 (any memory size), chosen as a trade-off between its computing power over power consumption ratio and its wide availability on the market; • Target operating: Raspbian version 2020-02-13; • Target programming language: Python 3.5; • Target PyQt5: version 5.11.3."
- "The MVM GUI is a Python3 software, written using the PyQt5 toolkit, that allows steering and monitoring the MVM equipment."
Brian:
- Call for Volunteers! Python GitHub Migration Work Group
- migration from bugs.python.org to GitHub
Calvin:
- Learn Python Humble Bundle
- Pay $15+ and get an amazing set of Python books to start learning at all levels
- Book Industry Charitable Foundation
- The No Starch Press Foundation
Joke:
More O’Really book covers