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Talk Python To Me

Updated 2 months ago

Rank #49 in Technology category

Technology
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Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive deep into the popular packages and software developers, data scientists, and incredible hobbyists doing amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite packages and the hot new ones coming out of open source.

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Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive deep into the popular packages and software developers, data scientists, and incredible hobbyists doing amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite packages and the hot new ones coming out of open source.

iTunes Ratings

412 Ratings
Average Ratings
377
23
9
1
2

Indispensable

By Rintel - Jan 03 2020
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Any must-listen podcast for any aspiring Python professional.

Excellent

By dldnh - Dec 18 2019
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This is an excellent podcast. The topics, the guests, the host, the interviews - really well done!

iTunes Ratings

412 Ratings
Average Ratings
377
23
9
1
2

Indispensable

By Rintel - Jan 03 2020
Read more
Any must-listen podcast for any aspiring Python professional.

Excellent

By dldnh - Dec 18 2019
Read more
This is an excellent podcast. The topics, the guests, the host, the interviews - really well done!
Cover image of Talk Python To Me

Talk Python To Me

Latest release on Aug 10, 2020

Read more

Talk Python to Me is a weekly podcast hosted by developer and entrepreneur Michael Kennedy. We dive deep into the popular packages and software developers, data scientists, and incredible hobbyists doing amazing things with Python. If you're new to Python, you'll quickly learn the ins and outs of the community by hearing from the leaders. And if you've been Pythoning for years, you'll learn about your favorite packages and the hot new ones coming out of open source.

Rank #1: #261 Monitoring and auditing machine learning

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Traditionally, when we have depended upon software to make a decision with real-world implications, that software was deterministic. It had some inputs, a few if statements, and we could point to the exact line of code where the decision was made. And the same inputs lead to the same decisions.

Nowadays, with the rise of machine learning and neural networks, this is much more blurry. How did the model decide? Has the model and inputs drifted apart, so the decisions are outside what it was designed for?

These are just some of the questions discussed with our guest, Andrew Clark, on this episode of Talk Python To Me.

Links from the show

Andrew on Twitter: @aclarkdata1
Andrew on LinkedIn: linkedin.com
Monitaur: monitaur.ai

scikit-learn: scikit-learn.org
networkx: networkx.github.io
Missing Number Package: github.com
alibi package: github.com
shap package: github.com
aequitas package: github.com
audit-ai package: github.com
great_expectations package: github.com

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Talk Python Training

Apr 25 2020

1hr

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Rank #2: #258 Thriving in a remote developer environment

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If you are listening to this episode when it came out, April 4th, 2020, there's a good chance you are listening at home, or on a walk. But it's probably not while commuting to an office as much of the world is practicing social distancing and working from home. Maybe this is a new experience, brought upon quickly by the global lockdowns, or maybe it's something you've been doing for awhile.

Either way, being effective while working remotely, away from the office, is an increasingly valuable skill that most of us in the tech industry have to quickly embrace.

On this episode, I'll exchange stories about working from home with Jayson Phillips. He's been writing code and managing a team from his home office for years and has brought a ton of great tips to share with us all.

Links from the show

Jayson on Twitter: @_jjphillips
Jayson's twitter thread on remote work: twitter.com
Clockwise: getclockwise.com
Calendly: calendly.com
Ideas on Making Remote Work... Work For You: jaysonjphillips.com
[Book] Remote - Office Not Required: amazon.com

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Linode
Talk Python Training

Apr 04 2020

1hr 7mins

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Rank #3: #254 A Python mentorship story

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How do you go from poking around at Python code to actually solving real problems, the right way?

There are many paths. The longest one probably is to get a 4-year CS degree. Maybe faster, but pricy as well, is a solid in-person developer bootcamp.

Have you considered reaching out to the community to find a mentor? Many Python meetups have project nights where folks who could help will be attending. If you're up for giving back, maybe you could become a mentor too.

That's what this episode is about. We'll hear from two former guests of Talk Python, Rusti Gregory and Doug Farrell. They teamed up and are back to share their mentorship story!

Links from the show

Guests

Rusti Gregory: talkpython.fm
Doug Farrell: @writeson

Doug's Real Python articles: realpython.com
Code Mentor Program: codementor.io
D-Tale Project: github.com
Let Me Google That For You Example: lmgtfy.com
JustPy Web Project: justpy.io
Doug's Well-Grounded Python Dev Book: manning.com

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Linode
Talk Python Training

Mar 06 2020

1hr 7mins

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Rank #4: #255 Talking to cars with Python

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Modern cars have become mobile computer systems with many small computers running millions of lines of code. On this episode, we plug a little Python into those data streams.

You'll meet Shea Newton, who is a Python developer who has worked on autonomous cars and is currently at ActiveState.

Links from the show

Shea on Twitter: shnewto

Video presentation of PDX Talk: youtube.com
Shea's source for PDX Python talk: github.com

DonkeyCar: donkeycar.com
Roomba Programming: github.com

Sponsors

Datadog
Clubhouse
Talk Python Training

Mar 14 2020

51mins

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#277 10 tips every Django developer should know

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We recently covered 10 tips that every Flask developer should know. But we left out a pretty big group in the Python web space: Django developers! And this one is for you. I invited Bob Belderbos, who's been running his SaaS business on Python and Django for several years now, to share his tips and tricks.

The 10 tips

  1. Django Admin
  2. ORM magic
  3. Models
  4. Debugging/Performance Toolbar
  5. Extending the User model
  6. Class based views (CBVs)
  7. manage.py
  8. Write your own middleware
  9. Config variable management with python-decouple and dj-database-url
  10. Built-in template tags and filters

Links from the show

Bob on Twitter: @bbelderbos
Code Challenges Platform: codechalleng.es
PyBites: pybit.es

Django admin: docs.djangoproject.com
Django admin cookbook: books.agiliq.com
Use some Django ORM magic to get the most common first names: twitter.com/pybites
Django custom manager: riptutorial.com
Debug toolbar: django-debug-toolbar.readthedocs.io
select_related: docs.djangoproject.com
Extending the user model / working with signals / @receiver: simpleisbetterthancomplex.com
Class-based views: docs.djangoproject.com
Comparing class and function-based views: github.com/talkpython/100daysofweb
Example of class-based views: github.com/talkpython/100daysofweb
Django command template: gist.github.com
Django middleware example: gist.github.com

Config settings management:
python-decouple: pypi.org
dj-database-url: pypi.org

Useful template tags and filters: docs.djangoproject.com

for-empty: gist.github.com
is_new filter example: gist.github.com
Asynchronous Tasks with Django and Celery: testdriven.io
Celery debugging - CELERY_ALWAYS_EAGER: twitter.com/pybites
secure.py: github.com/TypeError/secure.py
django-tinymce: github.com/aljosa

Extra tools Michael mentioned
BeeKeeper Studio: beekeeperstudio.io
SimpleMDE: simplemde.com
Human time to Python parse string site (the one I forgot): pystrftime.com

Sponsors

Linode
Talk Python Training

Aug 10 2020

1hr 11mins

Play

#276 Geekout: Life in the solar system and beyond

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We're back with another GeekOut episode. Richard Campbell, a developer and podcaster who also dives deep into science and tech topics, is back for our second GeekOut episode. Last time we geeked out about the real science and progress around a moon base. This time it's why is there life on Earth, where could it be or have been in the solar system, and beyond.

In case you didn't catch the first GeekOut, episode 253, this one is more of a general science and tech episode. I love digging into the deep internals of all the tools of the Python space, but given all that is going on in the world, I thought it'd be fun to take a step back and just enjoy some fun geekery and give you all something to just sit back and let your mind dream.

Links from the show

Richard on Twitter: @richcampbell
All Richard's GeekOut Episodes: geekout.show
Moonbase Geekout Episode: talkpython.fm/253
High Altitude Venus Operational Concept (HAVOC): sacd.larc.nasa.gov
New Horizons: solarsystem.nasa.gov
The Planets: Saturn - NOVA documentary (Cassini): pbs.org
Mission to Jupiter - Galileo: jpl.nasa.gov

Sponsors

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Talk Python Training

Aug 06 2020

1hr 14mins

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#275 Beautiful Pythonic Refactorings

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Do you obsess about writing your code just the right way before you get started? Maybe you have some ugly code on your hands and you need to make it better. Either way, refactoring could be your ticket to happier days! On this episode, we'll talk through a powerful example of iteratively refactoring some code until we eventually turn our ugly duckling into a Pythonic beauty.

Conor Hoekstra is our guest on this episode to talk us through refactoring some web scraping code.

Links from the show

The PyCon talk: youtube.com
Presentation source code: github.com/codereport
Conor on Twitter: @code_report
Youtube channel: youtube.com/codereport
Perf example exceptions vs. test: gist.github.com/mikeckennedy
PyCon Online: us.pycon.org/2020/online
RAPIDS AI project: rapids.ai
Slides from presentation (with 9 refactoring steps): github.com/codereport
Talk Python episode on Sourcery: talkpython.fm/266

pip for venv only environment variable
PIP_REQUIRE_VIRTUALENV: docs.python-guide.org

Sponsors

Linode
Talk Python Training

Aug 01 2020

55mins

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#274 Profiling data science code with FIL

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Do you write data science code? Do you struggle loading large amounts of data or wonder what parts of your code use the maximum amount of memory? Maybe you just want to require smaller compute resources (servers, RAM, and so on).

If so, this episode is for you. We have Itamar Turner-Trauring, creator of the Python data science memory profiler FIL here to talk memory usage and data science.

Links from the show

Itamar on twitter: @itamarst
FIL: pythonspeed.com
Python Bytes coverage of FIL: pythonbytes.fm
Video: Small Big Data: using NumPy and Pandas when your data doesn't fit in memory: youtube.com
Software Engineering for Data Scientists Article: pythonspeed.com

Python Tutor: pythontutor.com
Weak references: docs.python.org

memory_profiler package: github.com
Austin profiler: github.com
WSL2 on Windows: pbpython.com/wsl-python.html

Sponsors

Linode
Talk Python Training

Jul 24 2020

58mins

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#273 CoCalc: A fully colloborative notebook development environment

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Everyone in the Python space is familiar with Notebooks these days. One of the original notebook environments was SageMath. Created by William Stein, and collaborators, it began as an open-source, Python-based, computational environment focused on mathematicians.

It has since grown into a full-blown company and has become a proper collaborative environment for things like Jupyter notebooks, Linux-backed Bash shells, and much more. Think Google Docs but across all these facets of development in your browser.

We welcome back William Stein to give us an update on his journey from professor to entrepreneur building CoCalc along the way.

Links from the show

William on Twitter: @wstein389
CoCalc: cocalc.com
Episode 59 about SageMath: talkpython.fm/59
Comparing CoCalc to other products: cocalc.com
Examples/Gallery: share.cocalc.com
SageMath: sagemath.org
X11 server: xpra.org

Sponsors

Linode
Talk Python Training

Jul 18 2020

55mins

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#272 No IoT things in hand? Simulate them with Device Simulator Express

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Python is one of the primary languages for IoT devices. With runtimes such as CircuitPython and MicroPython, they are ideal for the really small IoT chips.

Maybe you've heard of the Circuit Playground Express, BBC micro:bit, or the fancy Adafruit CLUE. They aren't too expensive (ranging from $25 to $50 each). But for large groups such as classrooms, this can be a lot of money. Moreover, getting your hands on these devices can sometimes be tricky as well.

With an extension for VS Code called Device simulator express, you can have instant access to all three (virtually of course). This cool extension adds a visual emulator as well as the native interactions such as buttons and temperature sensors.

Get your IoT on without a real device using VS code today. Let's meet the most recent team behind this project:

Guests

* Andrea Mah
* Sayyeda Mussa
* Vandy Liu
* Xuan-Nam Kevin Nguyen

Links from the show

Device simulator express: microsoft.com
Source code: github.com
Makecode: microsoft.com
Meet the team (short video intro): youtu.be
Overview video: youtu.be

Devices
Circuit Playground Express: adafruit.com
CLUE: adafruit.com
BBC micro:bit: microbit.org

Sponsors

Linode
Talk Python Training

Jul 12 2020

51mins

Play

#271 Unlock the mysteries of time, Python's datetime that is!

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Time is a simple thing, right? And working with it in Python is great. You just import datetime and then (somewhat oddly) use the datetime class from that module.

Oh except, there are times with timezones, and times without. And why is there a total_seconds() but not total_minutes(), hours() or days() on timedelta? How about computing the number of weeks?

What if you wanted to iterate over the next 22 workdays, skipping weekends?

Ok, we'd better talk about time in Python! Good thing Paul Ganssle is here. He's a core developer who controls time in CPython.

Links from the show

Talk Python Training Humble Bundle: humblebundle.com

Paul on Twitter: @pganssle
Paul's Blog: blog.ganssle.io
Paul's Website: ganssle.io

Datetime blog posts
pytz: The fastest footgun in the West: blog.ganssle.io
Stop using utcnow and utcfromtimestamp: blog.ganssle.io
A curious case of non-transitive datetime comparison: blog.ganssle.io
Semantics of timezone-aware datetime arithmetic: blog.ganssle.io

PEPs

PEP 495: Local time disambiguation: python.org
PEP 615: Support for the IANA Time Zone Database in the Standard Library: python.org

zoneinfo documentation in Python 3.9: docs.python.org
backports.zoneinfo: pypi.org
pytz_deprecation_shim: readthedocs.io

Extra libraries
dateutil: readthedocs.io
break-my-python: pypi.org
arrow: readthedocs.io
pendulum: pendulum.eustace.io

Indiana Time Zones: google.com

Sponsors

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Talk Python Training

Jul 04 2020

1hr 4mins

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#270 Python in supply chains: oil rigs, rockets, and lettuce

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On this episode, we are going to weave a thread through three different areas of Python programming that at first seem unlikely to have much in common. Yet, the core will be the same throughout. I think this is a cool lesson to learn as you get deeper into programming and a great story to highlight it.

We are going to meet Ravin Kumar who wrote Python code and data science tooling for oil rig tool manufacturer, a rocket company, and a hip multilocation restaurant chain.

Links from the show

Ravin on Twitter: @canyon289
PyMC3: pymc.io
Arviz project: arviz-devs.github.io/arviz
pystan project: pystan.readthedocs.io
NumFocus: numfocus.org
Bayesian Decision Making: canyon289.github.io
open-aerospace project: open-aerospace.github.io
SweetGreen: sweetgreen.com
Get notified when Bayesian Computation In Python is out: docs.google.com/forms
Bayesian Analysis with Python Book: packtpub.com

Sponsors

Sentry Error Monitoring, Code TALKPYTHON
Linode
Talk Python Training

Jun 25 2020

59mins

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#269 HoloViz - a suite of tools for Python visualization

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The toolchain for modern data science can be intimidating. How do you choose between all the data visualization libraries out there? How about creating interactive web apps from those analyses? On this episode, we dive into a project that attempts to bring the whole story together: HoloViz.

HoloViz is a coordinated effort to make browser-based data visualization in Python easier to use, easier to learn, and more powerful. And we have Philipp Rudiger from HoloViz here to guide us through it.

Links from the show

Philipp on Twitter: @PhilippJFR
HoloViews on Twitter: @HoloViews
Panel on Twitter: @Panel_org
Datashader on Twitter: @datashader
Examples: examples.pyviz.org
HoloViz tutorial: holoviz.org
Panel website: panel.holoviz.org
HoloViews website: holoviews.org
GeoViews website: geoviews.org
Project Discourse: discourse.holoviz.org
PyData Berlin talk: youtube.com
Census example: examples.pyviz.org/...

Sponsors

Brilliant
Datadog
Talk Python Training

Jun 19 2020

55mins

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#268 Analyzing dozens of notebook environments

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Are you using interactive notebooks for your data exploration or day-to-day programming? What environment do you use? Was it Jupyter and now you've made the move to JupyterLab? That's a great choice. But did you know there are more environments out there to choose from and compare? Have you heard of Callisto or Iodide? How about CoCalc or PolyNote? That's just the tip of the iceberg!

That's why I'm happy to have Sam Lau and Philip Guo here to share their research comparing and categorizing over 60 notebook environments.

Links from the show

Sam on Twitter: @samlau95
Philip's site: pgbovine.net
The paper: pgbovine.net/publications.htm
PDF download: computational-notebooks-design-space_VLHCC-2020.pdf
NBInteract: nbinteract.com
NBStripout: pypi.org/project/nbstripout
Audio live coding: foxdot.org
NBDev: github.com/fastai/nbdev
PyIodide episode: talkpython.fm
Carnets: holzschu.github.io/Carnets_Jupyter

Sponsors

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Sentry Error Monitoring, Code TALKPYTHON
Talk Python Training

Jun 13 2020

54mins

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#267 15 amazing pytest plugins

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Do you write tests for your code? You probably should. And most of the time, pytest is the industry standard these days. But pytest can be much more than what you get from just installing it as a tool.

There are many amazing plugins that improve pytest in many aspects. That's why I invited Brian Okken to the show to tell us about his favorites. Listen in and your Python testing will be faster, stronger, and more beautiful!

Links from the show

Brian Okken: @brianokken
Brian's pytest book: amazon.com
Test & Code podcast: testandcode.com
Test & Code 104: Top 28 pytest plugins: testandcode.com/104

The list of plugins

pytest-sugar: github.com/Teemu/pytest-sugar
pytest-cov: pypi.org/project/pytest-cov
pytest-stress: github.com/pytest-dev/pytest-stress
pytest-repeat: github.com/pytest-dev/pytest-repeat
pytest-instafail: pypi.org/project/pytest-instafail
pytest-metadata: github.com/pytest-dev/pytest-metadata
pytest-randomly: github.com/pytest-dev/pytest-randomly
pytest-xdist: pypi.org/project/pytest-xdist
pytest-flake8: github.com/tholo/pytest-flake8
pytest-timeout: pypi.org/project/pytest-timeout
pytest-spec: pypi.org/project/pytest-spec
pytest-picked: github.com/anapaulagomes/pytest-picked
pytest-freezegun: github.com/ktosiek/pytest-freezegun
pytest-check: github.com/okken/pytest-check
fluentcheck: github.com/csparpa/fluentcheck

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Sentry Error Monitoring, Code TALKPYTHON
Talk Python Training

Jun 06 2020

53mins

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#266 Refactoring your code, like magic with Sourcery

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Refactoring your code is a fundamental step on the path to professional and maintainable software. We rarely have the perfect picture of what we need to build when we start writing code and attempts to over plan and overdesign software often lead to analysis paralysis rather than ideal outcomes.

Join me as I discuss refactoring with Brendan Maginnis and Nick Thapen as well as their tool, Sourcery, to automate refactoring in the popular Python editors.

Links from the show

Guests

Brendan Maginnis: @brendan_m6s
Nick Thapen: @nthapen

Sourcery
Sourcery: sourcery.ai
Sourcery on Twitter: @sourceryai
VS Code and PyCharm Plugins: sourcery.ai/editor
GitHub Bot: sourcery.ai/github
For an instant demo ⭐ this repo, and Sourcery will refactor your most popular Python repo: github.com/sourcery-ai/sourcery

Python Refactorings article: sourcery.ai/blog

Nuitka
Talk Python episode: talkpython.fm
Nuitka site: github.com

Gilded Rose Kata: github.com

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Datadog
Linode
Talk Python Training

May 29 2020

57mins

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#265 Why is Python slow?

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The debate about whether Python is fast or slow is never-ending. It depends on what you're optimizing for: Server CPU consumption? Developer time? Maintainability? There are many factors. But if we keep our eye on pure computational speed in the Python layer, then yes, Python is slow.

In this episode, we invite Anthony Shaw back on the show. He's here to dig into the reasons Python is computationally slower than many of its peer languages and technologies such as C++ and JavaScript.

Links from the show

Anthony's CPython Source Book: realpython.com/products
Anthony's PyCon Talk: youtube.com
N-body problem example: github.com
HPy project: github.com
Austin profiler: github.com

Prior episodes:
#240: A guided tour of the CPython source: talkpython.fm
#214: Dive into CPython 3.8: talkpython.fm
#168: 10 Python security holes: talkpython.fm

Sponsors

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Sentry Error Monitoring, Code TALKPYTHON
Talk Python Training

May 19 2020

1hr 3mins

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#264 10 tips every Flask developer should know

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Are you a web developer who uses Flask? It has become the most popular Python web framework. Even if you have used it for years, I bet we cover at least one thing that will surprise you and make your Flask code better.

Join me as I speak with Miguel Grinberg about his top 10 list for tips and tricks in the Flask world. They're great!

The 10 tips

  1. No need to use jsonify anymore
  2. Do not store sensitive information in the user session
  3. Using a .env file for secrets - python-dotenv
  4. Windows laptops and Chromebooks are both great Python/Flask development machines with their Linux emulation
  5. Differences between App context and Request context
  6. Flask outside of a web server (Celery workers, cron jobs, etc.)
  7. Use https://github.com/TypeError/secure.py
  8. Use httpie instead of curl to send requests to your app
  9. Flask for asyncio: Quart https://pgjones.gitlab.io/quart/
  10. Greenlet frameworks (gevent, eventlet) and Flask
  11. Blueprints

Links from the show

Miguel on Twitter: @miguelgrinberg
Miguel's blog: blog.miguelgrinberg.com

python-dotenv package: pypi.org
httpie package: httpie.org
Quart: pgjones.gitlab.io
Talk Python episode on Quart: talkpython.fm
secure.py package: github.com

Sponsors

Sentry Error Monitoring, Code TALKPYTHON
Linode
Talk Python Training

May 12 2020

1hr 8mins

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#263 SEO for developers

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As developers and technologists, it's easy to think that powerful and unique ideas will percolate to the top. If we build something amazing, enthusiastic users will find and share our creations.

Sometimes this happens. But more often, success is an iceberg, on so many levels. We are going to look at one of those icebergs on this episode. Join me and Cristian Medina as we discuss SEO, search engine optimization, for developers. Some of your search ranking is out of your control, but as you will see, there are many tools in the developer's toolbox that will directly affect your search rank. Let's dive in!

Links from the show

Cris on Twitter: @tryexceptpass
tryexceptpass: tryexceptpass.org

The Beginner's Guide to SEO: moz.com

webassets Python bundler: pypi.org
PageSpeed Insights: developers.google.com
JSON-LD decriptors and schema.org: moz.com
Imageoptimz: imageoptim.com
Google Search Console: search.google.com
Twitter Card Validator: cards-dev.twitter.com

Sponsors

Kite AI Autocomplete
Linode
Talk Python Training

May 06 2020

1hr 2mins

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#262 Build a career in data science

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Has anyone told you that you should get into data science? Have you heard it's a great career? In fact, data scientist is the best job in America according to Glassdoor's 2018 rankings.

That's great. But how do you get a career in data science? Once you land that first job, how do you find the right fit? How do you find the right company? And how do you get more deeply involved in the community?

I have brought two great guests, both highly successful data scientists, on the show today who have been thinking deeply about this. Jacqueline Nolis and Emily Robinson are here to give you real-world, actionable advice on getting into this rewarding career.

Guests


Jacqueline Nolis (left) and Emily Robinson (right)

Links from the show

Emily on Twitter: @robinson_es
Jacqueline on Twitter: @skyetetra

Data Science Careers book (choose your version!)
Professional: datascicareer.com
Cool: bestbook.cool

Book discount code at Manning: podtalkpython19

Jacqueline’s offensive license plate project: github.com
Emily’s Pokémon project: hookedondata.org
PyJanitor package: pyjanitor.readthedocs.io
MissingNo package: github.com

Sponsors

Kite AI Autocomplete
Linode
Talk Python Training

May 01 2020

1hr 11mins

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#261 Monitoring and auditing machine learning

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Traditionally, when we have depended upon software to make a decision with real-world implications, that software was deterministic. It had some inputs, a few if statements, and we could point to the exact line of code where the decision was made. And the same inputs lead to the same decisions.

Nowadays, with the rise of machine learning and neural networks, this is much more blurry. How did the model decide? Has the model and inputs drifted apart, so the decisions are outside what it was designed for?

These are just some of the questions discussed with our guest, Andrew Clark, on this episode of Talk Python To Me.

Links from the show

Andrew on Twitter: @aclarkdata1
Andrew on LinkedIn: linkedin.com
Monitaur: monitaur.ai

scikit-learn: scikit-learn.org
networkx: networkx.github.io
Missing Number Package: github.com
alibi package: github.com
shap package: github.com
aequitas package: github.com
audit-ai package: github.com
great_expectations package: github.com

Sponsors

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Reuven's Weekly Python Exercises
Talk Python Training

Apr 25 2020

1hr

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#260 From basic script to interactive data sci app with Streamlit

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If you work on the data science or data visualization side of Python, you may have come to it from a scripting side of things. Writing just a little Python, using its magical libraries, with little structure or formalism to build a powerful analysis tool that runs in the terminal or maybe a jupyter notebook.

What if you could take that same code, sprinkle in just a bit of a simple API, and turn it into a fast and dynamic single page application allowing your users to dive into the visualizations on the web?

Well, that's basically what the folks over at Streamlit created! We'll dive into it with one of the creators, Adrien Treuille.

Links from the show

Adrien on Twitter: @myelbows
Streamlit on Twitter: @Streamlit

Self-driving car demo: github.com
Roadmap: streamlit.io

Gallery of apps
Gallery: streamlit.io/gallery
Face-GAN explorer: streamlit.io
Geographic data browser: streamlit.io
Real-time object detection: streamlit.io
Deep Dream network debugger: streamlit.io

Altair visualization package: altair-viz.github.io

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Reuven's Ace Interviews Course
Talk Python Training

Apr 18 2020

59mins

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#259 From Academia to Tech Industry and Python

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Did you come to Python from the academic side of the world? Maybe got into working with code for research or lab work and found you liked coding more than your first field of study. Whatever the reason, many people make the transition from the academic world over to tech and industry.

On this episode, you'll meet three women who have made this transition, and you'll hear their stories. I'm excited to speak with Jennifer Stark, Kaylea Haynes, and Eslene Bikoumou about their journey to the tech field.

Links from the show

Guests

Kaylea on Twitter: @kayleahaynes
Jennifer on Twitter: @_JAStark
Eslene on Twitter: @eslene_girl7

VADER Sentiment Analysis: github.com
LAD Bible: ladbible.com
Peak AI: peak.ai
PyData Manchester: meetup.com
Her plus Data Manchester Meetup: meetup.com
Cookiecutter for data-driven journalism: github.com

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Linode
Talk Python Training

Apr 09 2020

1hr

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#258 Thriving in a remote developer environment

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If you are listening to this episode when it came out, April 4th, 2020, there's a good chance you are listening at home, or on a walk. But it's probably not while commuting to an office as much of the world is practicing social distancing and working from home. Maybe this is a new experience, brought upon quickly by the global lockdowns, or maybe it's something you've been doing for awhile.

Either way, being effective while working remotely, away from the office, is an increasingly valuable skill that most of us in the tech industry have to quickly embrace.

On this episode, I'll exchange stories about working from home with Jayson Phillips. He's been writing code and managing a team from his home office for years and has brought a ton of great tips to share with us all.

Links from the show

Jayson on Twitter: @_jjphillips
Jayson's twitter thread on remote work: twitter.com
Clockwise: getclockwise.com
Calendly: calendly.com
Ideas on Making Remote Work... Work For You: jaysonjphillips.com
[Book] Remote - Office Not Required: amazon.com

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Brilliant
Linode
Talk Python Training

Apr 04 2020

1hr 7mins

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iTunes Ratings

412 Ratings
Average Ratings
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Indispensable

By Rintel - Jan 03 2020
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Any must-listen podcast for any aspiring Python professional.

Excellent

By dldnh - Dec 18 2019
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This is an excellent podcast. The topics, the guests, the host, the interviews - really well done!