There were tons of great talks at PyCon 2018 – more than I could attend in person – that are now available on the PyCon 2018 YouTube channel. This post has links to my favorites. Enjoy!
By the Numbers: Python Community Trends in 2017/2018 (Dmitry Filippov, Ewa Jodlowska) – At the end of 2017, the Python Software Foundation teamed up with JetBrains to conduct an official Python Developers Survey. Data science is taking Python by storm, and Python 3 now has majority adoption. There are tons of other cool statistics, too!
How Netflix does failovers in 7 minutes flat (Amjith Ramanujam) – That speed at that scale is mind-blowing. This is a fascinating talk, even for non-engineers!
Solve Your Problem With Sloppy Python (Larry Hastings) – “If you ever start writing a shell script, delete it and write a Python script instead.” This talk is a jovial reminder that Python is a powerful tool, even for hack-n-slash jobs.
The AST and Me (Emily Morehouse-Valcarcel) – Emily gives a great overview of the inner workings of the Python language. This talk is a must-see for anyone into compiler theory.
Dataclasses: The code generator to end all code generators (Raymond Hettinger) – Dataclasses are new data structures to Python to generate classes based on specs.
Pipenv: the Future of Python Dependency Management (Kenneth Reitz) – Pipenv is a new tool that makes pip, Pipfile, and virtualenv easier to use together. Kenneth gives a good overview of Python packaging and why pipenv is awesome.
Type-checked Python in the real world (Carl Meyer) – Sometimes, I wish Python had static typing. Now, it can! Facebook has done some innovative things to make it possible.
Beyond Unit Tests: Taking Your Testing to the Next Level (Hillel Wayne) – Property tests + contracts = integration tests. Hillel gives a fantastic strategy for making tests smarter.
“WHAT IS THIS MESS?” – Writing tests for pre-existing code bases (Justin Crown) – This is a pragmatic guide to adding new tests to old code. Now, you’ll never procrastinate on your tests again!
Demystifying the Patch Function (Lisa Roach) – Mocking is a great practice for limiting scope in unit tests. Whether using unittest.mock or other patching/mocking packages, this is a great talk for learning why and how to do mocking in unit tests!
Automating Code Quality (Kyle Knapp) – One of Python’s most beloved traits is its elegance. Maintaining high standards of code quality can be challenging for large projects, though. Kyle shows how to use existing tools to drive higher quality.
Keynote (Ying Li) – [35:07] – Ying is a software security engineer at Docker. In her keynote, she urged all people involved in technology to learn the basics of security. Definitely watch the video recording – she used a fun story to illustrate her points.
Keynote: The People and Python (Qumisha Goss) – [1:07:35] – “Q” is a librarian at the Detroit Public Library who taught herself Python so she could teach coding classes to kids. She shared the highs and lows of her experiences, especially in light of many disadvantages her students had. My favorite takeaway was to “cultivate greatness in others.”
The main reason I went to PyCon 2018 was to deliver a talk entitled “Behavior-Driven Python” about behave, one of Python’s most popular BDD test automation frameworks. One of my major professional goals for 2018 was to speak at a conference – and any conference would do. Fortunately for me, PyCon accepted my proposal, and Piper Companies in Raleigh graciously footed the bill! The video recording for my talk is linked below. It also has a GitHub example project and a companion article. (I’ll write a separate article with links to other talks I enjoyed.)
The Destination
PyCon 2018 was held in Cleveland, Ohio. I had never been to Cleveland before, and I found the downtown area to be charming. Everything I needed was within walking distance: the Huntington Convention Center where the conference was held, the DoubleTree by Hilton on Lakeside Ave where I stayed, the skyline, the city hall, the Rock and Roll Hall of Fame, the Great Lakes Science Center, and Lake Erie itself.
Cleveland has a nice skyline.
The Huntington Convention Center, where PyCon 2018 took place.
Interesting shot on a bridge near the Rock and Roll Hall of Fame.
“FREE” stamp sculpture next to city hall.
I flew into Cleveland on the evening of Thursday, May 10. Unfortunately, I missed the opening reception because my Frontier Airlines flight was delayed. (I guess I got what I paid for.) When I arrived, I had only one destination in mind: Great Lakes Brewing Company. Great Lakes has been one of my favorite breweries since I first started drinking craft beer in college. I boarded the Red Line train at the airport and rode it directly to the Ohio City station, where their pub is located. The food and beer did not disappoint!
Great Lakes Brewing Company
GLBC Brats and Pierogies
Delayed flights are never fun.
Great Lakes Brewing Company
Ohio City
GLBC Flight Set
The Red Line
The First Morning
PyCon 2018 had three major phases: tutorials from May 9-10, talks and events from May 11-13, and sprints from May 14-17. I attended only from May 11-13 for the “main” part of the conference. I really didn’t know what to expect, but I was blown away by what I found.
The first thing I did on the first day was registration. I showed up at about 8am to get my badge and my conference t-shirt. The volunteers also handed me a “swag bag,” pre-populated with a map and some random goodies. Since I had my backpack, I didn’t think I would need an extra bag – boy, was I wrong!
The main conference area was an expo hall full of companies and organizations giving away endless freebies, much to my naive surprise. (This was nothing like the last conference I attended, PyData Carolinas 2016.) The major stalls were Microsoft, Amazon (AWS), Google, Facebook/Instagram, LinkedIn, Anaconda, O’Reilly, and Heroku. Others included the Python Software Foundation, Django Girls, JetBrains, Elasticsearch, ChowNow, Yelp, Patreon, Squarespace, Linode, platform.sh, PostgreSQL, Nylas, DigitalOcean, DataDog, Cactus, Six Feet Up, OfferUp, Twilio, Nexmo, Okta, Pluralsight, Zapier, Bloomberg, Shopify, PyBee, EdgeDB, Anvil, and others I can’t remember. Over the three days of main events, I talked with people at nearly every stall to learn about what they do and to score that dank swag. I walked away with twelve t-shirts, five pairs of socks, laminated Python guides, a JetBrains yo-yo, a Google puzzle, Yelp gloves, a Facebook earbuds case, an OfferUp water bottle, a couple koozies, and a countless number of stickers.
The talks in Grand Ballroom A. Mine was Friday at 12:10pm!
The Django Pony at the Django Girls table!
Setting up the expo hall
The welcome banner at my hotel
The expo hall entrance
All the swag!
While waiting for the first keynote address, I ran into Kenneth Reitz at the Python Software Foundation table. Kenneth is the original author of requests and pipenv. It was a pleasure to meet him in person. He also interviewed me for his PyCon 2018 podcast! My segment is at 19:11.
I was about to go to the keynote address when I walked by the O’Reilly stall and discovered a book signing: Harry J.W. Percival was scheduled to give away free signed copies of the second edition of his book, Test-Driven Development with Python. I got my “golden ticket,” waited in line, skipped the keynote, and scored that dankest swag of the conference. O’Reilly was giving out other books throughout the conference, but as a Software Engineer in Test, this one was the big kahuna for me. #worthit
The O’Reilly book signing
Kenneth Reitz (left) and the Automation Panda (right)
My talk, “Behavior-Driven Python,” was scheduled at 12:10pm in Grand Ballroom A. I wasn’t terribly nervous because I had given this sort of talk many times, but I was worried that I would run out of time. Before speakers give their talks, they go to the “green room” where they test projector cables and meet the “runner” who will take them to the auditorium. I got to meet other speakers, which made me feel more comfortable. My talk got off to a delayed start due to some technical difficulties with the projector, but I think it went really well. The ballrooms could each seat several hundred people, and it looked like my talk was fairly well attended. A number of people asked me questions afterwards. Then, I ended up having lunch with a new friend I met named Gabriel, too!
The Rest of Day 1
I spent the rest of the afternoon attending talks, which can be mentally exhausting after too much. However, at the end of the day, I got to spend some sweet time with my dear friend Kennedy from college. Kennedy reached out to me before the conference to let me know that he would be there. I ran into him each day, but we got to spend the most time together sitting outside Ballroom A just catching up on life. We hadn’t seen each other since 2010 at RIT. Kennedy is really getting into software infrastructure and DevOps-like work. It was such a blessing to see him there.
My bro Kennedy sports that Linode shirt like a boss!
Dinner was another fortuitous blessing. ChowNow invited me to dinner and drinks at TownHall. I met their chief product officer, their engineers, and their recruiters. We talked a lot about test automation. They’re in a very similar situation as my current company, PrecisionLender: a hundred people and growing, realizing their need for automated feature tests, and discovering how hard it is to build an automation solution themselves or find someone who can. They were really great people doing awesome things, and I can’t wait to see them grow.
ChowNow also had a fun giveaway challenge. To enter, one needed to hit a REST API endpoint, which then returned further instructions. It was a bit of a puzzle – I got confused for the first few minutes, but after a hearty facepalm I figured out the challenge and successfully submitted my entry. (Python REPL and requests FTW!) The grand prize was an iPad Pro, but I won the consolation prize of $20 in ChowNow bucks. Not bad.
The Second Day
For me, Day 2 at PyCon was almost entirely about talks. The morning keynotes were really inspiring: Ying Li told a great story modeled after the Wizard of Oz about how everyone plays a part in security, and Qumisha Goss shared how she inspires kids at the Detroit public library to get into coding with Python. There were more talks I wanted to attend than I could. I learned about sloppy Python, developing arcade-style games, statistics on Python users, Appium, and compiler tools.
In the expo hall, my most memorable conversation was at the Python Bytes / Talk Python To Me table. These are major Python podcasts. Julian Sequeira of PyBites told me all about the #100DaysOfCode in Python, which I really want to try so I can learn about things beyond my domain. He then introduced me to Brian Okken, who wrote Python Testing with pytest and runs the Test and Code podcast. We talked quite a bit about testing practices and frameworks. I almost convinced him of BDD’s benefits, and he tried to convince me that unit testing is waste. It was a great conversation, and I really want to learn more about Brian’s pragmatic testing perspective.
Julian championing the Sceptre of Python!
That evening, Instagram invited me to dinner. I thought it would be drinks and appetizers at a bar, like with ChowNow. Oh, no, it was … Let me tell the story. Before PyCon started, Instagram invited me to join them for a special dinner. I think they invited me because I was a speaker. Instagram provided promo codes for a free Lyft to Crop Bistro, one of the best restaurants in the city. It resides in an old bank: marble columns and fresco paintings on the walls. When I arrived, the hostess walked me to the back, down the stairs, through the kitchen, and into the bank’s old vault, which had been converted into a private party room. They served a full three-course meal with a full bar, and it was damn good. That ribeye steak… I met a lot of great people, too. I talked with Instagram’s release manager at length about struggles with test automation. I also got to chat with a number of their engineers (many of whom were from China), as well as other Pythonistas who were invited. It was a wonderful event, and I truly thank Instagram for the invitation.
Crop Bistro
A 3-Course Meal!
The Vault at Crop Bistro
Instagram Cookie
Also, I want to say that the weather in Cleveland was pretty darn cold! Daily temperatures were in the 50s, while at the same time in Raleigh, they were in the 90s. I froze my ears off walking from the hotel to the convention center!
The Third Day
At the Instagram dinner, I met a few guys who help organize Python conferences. They encouraged me to submit proposals to PyGotham and PyOhio. So, when I woke up on Day 3, I did! Hopefully, my proposals will be accepted.
The Job Fair
Representing PrecisionLender! Someone asked me about the four values on the back of the shirt.
The main event in the morning was the Poster expo / Job Fair. However, since I had already met most of the companies, I spent most of my time at the Rock and Roll Hall of Fame instead. I’m a huge fan of rock music. The museum had awesome exhibits with really cool memorabilia. I even got to vote for new inductees – I voted for Iron Maiden! I headed back to PyCon for the afternoon talks, but then I skipped out of the finale to finish seeing all of the Rock and Roll Hall of Fame exhibits before the museum closed.
The Beatles entry in the Hall of Fame
Outside the Rock and Roll Hall of Fame
Chinese Lunch
Angus Young’s Stage Outfit
Cliff Burton’s Bass Guitar (Metallica)
Aerosmith’s Set
My original dinner plan was to visit another local brewery. PyCon was hosting an event dinner at the Great Lakes Science Center, but I didn’t register in time to get a ticket. Nevertheless, my friend Kennedy offered me his meal ticket since he was returning home that afternoon. Hashtag-BLESSED. The museum was so cool. My favorite part was the NASA Glenn Visitor Center – they had one of the Apollo Skylab capsules! Many of us Pythonistas built a really tall tower out of wood blocks that we had to knock down once dinner was ready. The food was excellent, too: steak, salmon, mac ‘n cheese, green beans, and cheesecake for dessert.
Inside the Mercury Capsule
Block Tower!
Apollo Capsule from Skylab
Apollo Capsule from Skylab
Delicious Dinner
Mercury Capsule
My (Delta) direct flight home left on time Monday morning. I could barely fit all the swag into my suitcase! I caught a cold while attending the conference, but thankfully it didn’t take effect until I was back home.
Major Takeaways
So much happened at PyCon 2018. Even though I was doing stuff nonstop for three days, there was still so much more there to do. It will take me a few weeks to fully process everything. Here were my major takeaways:
I accomplished my goals. Before going to PyCon, I set three major goals for myself: (1) get a pulse on the state and direction of Python, (2) establish rapport in the community, and (3) become inspired to pursue greater work. CHECK! I met a lot of great people who all love using Python. A number of people really enjoyed my talk. I learned how so many groups, from top-tier Silicon Valley companies to local user orgs, are using Python to do cool stuff. There are also now just as many data scientists as web developers using Python. Seeing Python used in so many different domains really inspired me to learn more about those domains in which my knowledge is limited. I feel like I have more work to do after the conference than I did to prepare for it!
The Python community is so welcoming and friendly. When PyCon’s banner says, “For the community, by the community,” it’s true. This conference was about people much more than it was about programming. I was initially afraid that I would be lonely because I didn’t know anyone, but once I got there, everyone was outgoing; even me! Python is a language for everyone from beginners to experts, and there was no sense of elitism whatsoever at the conference. The atmosphere reminded me very much of freshman orientation week at RIT, when total strangers would strike up conversations and bestow well-wishes at every turn.
All companies have major test automation struggles. There is a universal awareness of the need for good testing, but there is also universal struggle to develop reliable feature tests at scale. Knowing that companies like Facebook/Instagram and ChowNow have problems similar to companies where I have worked gives me boosted motivation as a Software Engineer in Test to keep going!
Never write shell scripts. Just write Python scripts. Yes! This came from the “sloppy Python” talk. It’s so true. Shell scripting is so low-level and often unreadable. Plus, Python is cross-platform!
Flask is a big deal. Flask is a minimalist Pythonic web framework. It is a lightweight alternative to Django and Pyramid. Everyone was talking about it. O’Reilly was giving away books about it.
Prep for PyCon 2019! I want to return to PyCon very much. Next year, I have a better understanding of the talks, so I can make better proposals. I’ll also check out the open spaces and lightning talks. PyCon 2019 will be back in Cleveland, too!
What’s Next?
I feel like I have so much more to learn! Here’s what’s next for me:
Watch videos for all the talks I missed.
Come up with a personal professional development plan.
Django is a fantastic Python Web framework, and one of its great out-of-the-box features is internationalization (or “i18n” for short). It’s pretty easy to add translations to nearly any string in a Django app, but what about translating admin site pages? Titles, names, and actions all need translations. Those admin pages are automatically generated, so how can their words be translated? This guide shows you how to do it easily.
Want an internationalized admin site like this? Follow this guide to find out how!
i18n Review
If you are new to translations in Django, definitely read the official Translation page first. In a nutshell, all strings that need translation should be passed into a translation function for Python code or a translation block for Django template code. Django management commands then generate language-specific message files, in which translators provide translations for the marked strings, and finally compile them for app use. Note that translations require the gettext tools to be installed on your machine. Django also provides some advanced logic for handling special cases like date formats and pluralization, too. It’s really that simple!
Initial Setup
A Django project needs some basic config before doing translations, which is needed for both the main site and the admin.
Enabling Internationalization
Make sure the following settings are given in settings.py:
# settings.py
LANGUAGE_CODE = 'en-us' # or other appropriate code
USE_I18N = True
USE_L10N = True
They were probably added by default. The Booleans could be set to False to give apps with no internationalization a small performance boost, but we need them to be True so that translations happen.
Changing Locale Paths
By default, message files will be generated into locale directories for each app with strings marked for translation. You may optionally want to set LOCALE_PATHS to change the paths. For example, it may be easiest to put all message files into one directory like this, rather than splitting them out by app:
This will avoid translation duplication between apps. It’s a good strategy for small projects, but be warned that it won’t scale well for larger projects.
Middleware for Automatic Translation
Django provides LocaleMiddleware to automatically translate pages using “context clues” like URL language prefixes, session values, and cookies. (The full pecking order is documented under How Django discovers language preference on the official doc page.) So, if a user accesses the site from China, then they should automatically receive Chinese translations! To use the middleware, add django.middleware.locale.LocaleMiddleware to the MIDDLEWARE setting in settings.py. Make sure it comes after SessionMiddleware and CacheMiddleware and before CommonMiddleware, if those other middlewares are used.
Getting automatic translations from context clues is great, but it’s nevertheless useful to have direct URLs to different page translations. The i18n_patterns function can easily add the language code as a prefix to URL patterns. It can be applied to all URLs for the site or only a subset of URLs (such as the admin site). Optionally, patterns can be set so that URLs without a language prefix will use the default language. The main caveat for using i18n_patterns is that it must be used from the root URLconf and not from included ones. The project’s root urls.py file should look like this:
# urls.py
from django.conf.urls.i18n import i18n_patterns
from django.contrib import admin
from django.urls import path
urlpatterns = i18n_patterns(
# ...
path('admin/', admin.site.urls),
# ...
# If no prefix is given, use the default language
prefix_default_language=False
)
Limiting Language Choices
When adding language prefixes to URLs, I strongly recommend limiting the available languages. Django includes ready-made message files for several languages. A site would look bad if, for example, the “/fr/” prefix were available without any French translations. Set the available languages using LANGUAGES in settings.py:
# settings.py
from django.utils.translation import gettext_lazy as _
LANGUAGES = [
('en', _('English')),
('zh-hans', _('Simplified Chinese')),
]
Note that language codes follow the ISO 639-1 standard.
Doing the Translations
With the configurations above, translations can now be added for the main site! The steps below show how to add translations specifically for the admin. Unless there is a specific need, use lazy translation for all cases.
Out-of-the-Box Phrases
Admin site pages are automatically generated using out-of-the-box templates with lots of canned phrases for things like “login,” “save,” and “delete.” How do those get translated? Thankfully, Django already has translations for many major languages. Check out the list under django/contrib/admin/locale for available languages. Django will automatically use translations for these languages in the admin site – there’s nothing else you need to do! If you need a language that’s not available, I strongly encourage you to contribute new translations to the Django project so that everyone can share them. (I suspect that you could also try to manually create messages files in your locale directory, but I have not tested that myself.)
Custom Admin Titles
There are a few ways to set custom admin site titles. My preferred method is to set them in the root urls.py file. Wherever they are set, mark them for lazy translation. It’s easy to overlook them!
from django.contrib import admin
from django.utils.translation import gettext_lazy as _
admin.site.index_title = _('My Index Title')
admin.site.site_header = _('My Site Administration')
admin.site.site_title = _('My Site Management')
App Names
App names are another set of phrases that can be easily missed. Add a verbose_name field with a translatable string to every AppConfig class in the project. Do not simply try to translate the string given for the name field: Django will yield a runtime exception!
from django.apps import AppConfig
from django.utils.translation import gettext_lazy as _
class CustomersConfig(AppConfig):
name = 'customers'
verbose_name = _('Customers')
Model Names
Models are full of strings that need translations. Here are the things to look for:
Give each field a verbose_name value, since the identifiers cannot be translated.
Mark help texts, choice descriptions, and validator messages as translatable.
Add a Meta class with verbose_name and verbose_name_plural values.
Look out for any other strings that might need translations.
Here is an example model:
from django.db import models
from django.core.validators import RegexValidator
from django.utils.translation import gettext_lazy as _
class Customer(models.Model):
name = models.CharField(
max_length=100,
help_text=_('First and last name.'),
verbose_name=_('name'))
address = models.CharField(
max_length=100,
verbose_name=_('address'))
phone = models.CharField(
max_length=10,
validators=[RegexValidator(
'^\d{10}$',
_('Phone must be exactly 10 digits.'))],
verbose_name=_('phone number'))
class Meta:
verbose_name = _('customer')
verbose_name_plural = _('customers')
Run the Commands
Once all strings are marked for translation, generate the message files:
# Generate message files for a desired language
python manage.py makemessages -l zh_Hans
# After adding translations to the .po files, compile the messages
python manage.py compilemessages
Warning: The language code and the locale name may be different! For example, take Simplified Chinese: the language code is “zh-hans”, but the locale name is “zh_Hans”. Notice the underscore and the caps. Locale names often include a country code to differentiate language nuances, like American English vs. British English. Refer to django/contrib/admin/local for a list of examples.
Bonus: Admin Language Buttons
With LocaleMiddleware and i18n_patterns, pages should be automatically translated based on context or URL prefix. However, it would still be great to let the user manually switch the language from the admin interface. Clicking a button is more intuitive than fumbling with URL prefixes.
There are many ways to add language switchers to the admin site. To me, the most sensible way is to add flag icons to the title bar. Behind the scenes, each flag icon would be linked to a language-prefixed URL for the page. That way, whenever a user clicks the flag, then the same page is loaded in the desired language.
It’s pretty easy to make something like this, but it needs a few steps.
Language Code Prefix Switcher
Since URL paths use i18n_patterns, their language codes can be trusted to be uniform. A utility function can easily add or substitute the desired language code as a URL path prefix. For example, it would convert “/admin/” and “/en/admin/” into “/zh-hans/admin/” for Simplified Chinese. This function should also validate that the path and language are correct. It can be put anywhere in the project. Below is the code:
from django.conf import settings
def switch_lang_code(path, language):
# Get the supported language codes
lang_codes = [c for (c, name) in settings.LANGUAGES]
# Validate the inputs
if path == '':
raise Exception('URL path for language switch is empty')
elif path[0] != '/':
raise Exception('URL path for language switch does not start with "/"')
elif language not in lang_codes:
raise Exception('%s is not a supported language code' % language)
# Split the parts of the path
parts = path.split('/')
# Add or substitute the new language prefix
if parts[1] in lang_codes:
parts[1] = language
else:
parts[0] = "/" + language
# Return the full new path
return '/'.join(parts)
Prefix Switch Template Filter
Ultimately, this function must be called by Django templates in order to provide links to language-specific pages. Thus, we need a custom template filter. The filter implementation module can be put into any app, but it must be in a sub-package named templatetags – that’s how Django knows to look for custom template tags and filters. The new filters will be easy to write because we already have the switch_lang_code function. (Separating the logic to handle the prefix from the filter itself makes both more testable and reusable.) The code is below:
# [app]/templatetags/i18n_switcher.py
from django import template
from django.template.defaultfilters import stringfilter
register = template.Library()
@register.filter
@stringfilter
def switch_i18n_prefix(path, language):
"""takes in a string path"""
return switch_lang_code(path, language)
@register.filter
def switch_i18n(request, language):
"""takes in a request object and gets the path from it"""
return switch_lang_code(request.get_full_path(), language)
Admin Template Override
Finally, admin templates must be overridden so that we can add new elements to the admin pages. Any admin template can be overridden by creating new templates of the same name under [project-root]/templates/admin. Parent content will be used unless explicitly overridden within the child template file. Since we want to change the title bar, create a new template file for base_site.html with the following contents:
The static CSS file named css/custom_admin.css should have the following contents:
Notice that the whole userlinks block had to be rewritten to fit the flag into place. The static image files for the flags are simply free flag emojis. They are hyperlinked to the appropriate language URL for the page: the switch_i18n filter is applied to the active request object to get the desired language-prefixed path. (Note: In my example code, I removed the “View Site” link because my site didn’t need it.)
Completed View
The admin site should now look like this:
This slideshow requires JavaScript.
The files in my project needed for the admin language buttons are organized like this (without showing other files in the project):
As mentioned before, flag icons in the title bar are simply one way to provide easy links to translated pages. It works well when there are only a few language choices available. A different view would be better for more languages, like a dropdown, a second line in the title bar, or even a page footer.
With a bit more polishing, this would also make a nifty little Django app package that others could use for their projects. Maybe I’ll get to that someday.
This week, I was featured as the PyDev of the Week on DZone and The Mouse Vs. The Python. Many thanks to Mike Driscoll, who interviewed me about my development experience for the article.
While recently deploying a new Python Django app to Heroku, I noticed the documentation mentioned a tool I hadn’t known before: pipenv. I thought to myself, “Great, now I need to learn a new tool. What was so bad about pip and virtualenv?” So, I did my research, and BOOM! Yes. Mind blown. Life changed. This.
What It Is
Pipenv is the Python packaging and environments tool for champions.
It unites pip, Pipfile, and virtualenv into a sophisticated workflow with simple commands.
It automatically creates virtual environments for projects.
It automatically updates package dependencies (and their dependencies).
Packages and environments (“packagement”) are essential to Python development. Typically, Pythoneers create a virtual environment for each project and install dependent packages into it locally using pip. They then “freeze” the dependencies into a requirements.txt file so that others can easily recreate the environment. Virtual environments thus enable different projects to use different package versions without global conflict.
Unfortunately, this traditional workflow has some problems:
It uses multiple tools instead of one and requires many commands.
Different projects can do the workflow differently, which can be confusing.
The requirements.txt file must be manually generated and can easily fall out of date.
Dev-only dependencies are a hassle to separate.
Uninstalling packages will not remove sub-packages.
Dependencies with version ranges instead of fixed versions cause nondeterministic builds.
Pipenv solves these problems by combining pip, Pipfile, and virtualenv into a standard workflow that automatically handles and locks package updates.
How to Use It
See how simple it is to use pipenv with a Python project:
# Install pipenv
pip install pipenv
# Create a new project directory
mkdir panda_project
cd panda_project
echo "print('hello')" > main.py
# Init pipenv:
# Creates a virtual environment
# Then creates Pipfile and Pipfile.lock files
pipenv install
# Install a package:
# Updates the Pipfiles
pipenv install requests
# Install a dev-only package:
# Updates the Pipfiles
pipenv install --dev pytest
# Run commands in the environment
pipenv run python --version
pipenv run python main.py
More Info
There’s no need for me to repeat what other people have already said:
Product quality metrics measure the excellence of a product and its features. They measure the “goodness” inherent in the product, apart from how the product was developed. High-quality processes and tests contribute to, but do not alone guarantee, high-quality products. That’s why quality must be built into the product from the start and checked throughout all phases of development. Below are metrics for assuring quality in the delivered products.
Functionality
Quality Aspect
Does the product work correctly?
Desired State
True – Features either work, or they don’t.
Metrics
Test Failure Rate – The whole purpose of functional testing is to determine which features work and which don’t. Assuming test quality is high, the test failure rate is the single best indicator of product functionality. Higher failure rates mean more broken features. Teams should target low-to-zero test failures. It may be useful to keep a failure history for each test. For large products, it may also be useful to break down failure rates by feature area.
It is imperative to recognize, however, that the test failure rate is meaningful only if test quality is high – meaning that tests have good coverage and reliability. Poor-quality tests will give untrustworthy results. For example, weak coverage could mean that failure rate is low because functionality is not truly exercised, and poor reliability could mean that failure rate is high because tests always crash. Be sure to back up any reporting on test failure rate with assurance that test quality is high (using test quality metrics).
Stability
Quality Aspect
Does the product work reliably?
Desired State
High – Product functionality should be consistently good and available.
Metrics
Build Failure Rate – The build failure rate is the proportion of builds that have failed for whatever reason over a given period of time. While process metrics focus on response times to fix broken builds, the build failure rate itself indicates the health of the product while it is being developed. It does not track how badly a build failed like test failure rate does, but instead it impartially tracks ultimate success or failure. Make sure to limit the history of builds included in the calculation to keep it relevant (such as the last 30 days or so). Occasional build failures are acceptable as long as they are fixed quickly. High build failure rates indicate product instability, which could be due to design flaws, weak pre-check-in testing, tricky bugs, or even pipeline faults.
Uptime – Uptime refers to the total time a system is usable. For example, consider a website that must go down for a one-hour service window every week – its uptime would be 167/168 = 99.4%. Not all downtime is planned, however. A bad deployment during maintenance could knock that website offline for an additional 3 hours – dragging uptime down to 97.6% for the week. This may not seem bad at first, but it’s quite terrible when considering that (a) lost time is lost money and (b) the goal of Six Sigma is 99.99966%. A product should have near-perfect availability. System monitoring tools can easily measure uptime. Low uptime indicates either poor design or lack of failover redundancy.
Performance
Quality Aspect
Does the product work optimally?
Desired State
Optimal – Performance should be at its best in all areas.
Metrics
There are four classic software performance metrics. They may be applied in various ways to aspects of product behavior. Ultimately, software products should have a minimal impact on the system while providing a maximal capacity for work.
Processor Usage – Processor cycles should not be needlessly wasted. Make sure algorithms are efficient in terms of computational complexity (big O) and implementation details.
Memory Usage – Watch out for both memory bloat (when features take up a lot of memory unnecessarily) and memory leaks (when memory is not freed up after it is no longer needed.)
Response Time – Response time, or latency, measures the turnaround time from when an action is taken to when the actor receives feedback that the action is completed. Common examples of response time are web page loading, REST API call responses, and database queries. Response time should be as short as possible.
Throughput – Throughput measures how much load a system can handle. It could refer to data I/O bandwidth, transactions per time unit, number of concurrent users, etc. Typically, higher stress on a system will cause other performance metrics to degrade. The “sweet spot” to find is the maximum throughput value that does not unacceptably impact other performance aspects.
Complexity
Quality Aspect
Is the software code unnecessarily complicated?
Desired State
Minimal – Simple is better than complex. Complex is better than complicated. (See The Zen of Python.)
Metrics
There are a number of code metrics that indicate complexity in various ways.
Lines of Code – One of the most rudimentary metrics is to count the lines of code. All things equal, line count indicates the magnitude of the software product, with the assumption that fewer lines will be easier to maintain. Any modern IDE (or, worst case, shell scripting) can yield line counts. However, all things are not equal, and line count alone does not indicate quality or efficiency.
Cyclomatic Complexity – Cyclomatic complexity measures the number of different execution paths the code can take. It is more meaningful than counting sheer lines of code because it indicates the magnitude of testing needed for full coverage. Lower values are better. Cyclomatic complexity is a popular code metric, and many modern analysis tools can measure it.
Depth of Inheritance – For object-oriented languages, the depth of inheritance measures the maximum length of a class inheritance tree from child class to its ultimate root. For example, in the class inheritance tree of Tiger > Cat > Animal > Object, Tiger would have an inheritance depth of 3. Lower values are desirable because they make classes easier to understand.
There are countless other code metrics available. For example, Microsoft Visual Studio calculates the metrics above plus a maintainability index and class coupling. Halstead metrics are another way to measure complexity.
Satisfaction
Quality Aspect
Does the product satisfy the end user?
Desired State
High – The product should meet the end user’s needs, and the end user should like using it.
Metrics
Customer satisfaction is inherently subjective, so trying to measure it is difficult. Ultimately, the end users must find compelling value in the product over other alternatives, or else they won’t use it or buy it. There are many ways to attempt to gauge customer satisfaction: surveys, interviews, A/B testing, etc. Statistics and psychology also play a part. Check out articles here, here, and here to get some ideas.
Process quality metrics make sure that software development practices build good, high-quality features. Healthy software processes identify and resolve issues as early as possible because later bug discovery means higher cost to fix. Quality starts at inception, when features are first brainstormed, and it carries through design, implementation, and testing. Every step in the development process should have quality checkpoints: acceptance criteria for planning, reviews for design and implementation, and reports for testing. Process quality metrics primarily focus on delivery speed or the effectiveness of feedback loops to make sure a team is responding appropriately to change.
Note: Standard software development methodologies often come with canned metrics. For example, Agile Scrum focuses heavily on velocity for determining a team’s capacity for work, while Agile Kanban focuses heavily on lead time and cycle time for measuring how fast work gets done. This article will not cover methodology-specific metrics – please refer to external resources to learn more about them. Instead, this article will cover generic aspects of process quality.
Delivery Speed
Quality Aspect
How fast are new features with high quality delivered to the end user?
Desired State
ASAP – Deliver them fast without compromising quality.
Metrics
People are impatient – they always want things as soon as possible. Fast delivery speed is thus crucial for businesses to meet client expectations and respond quickly to change. However, delivery speed is not the sole metric for success: it must be counterbalanced with safety measures. Delivery speed could be absolutely minimized by committing changes directly to production, but that’s a terrible practice because the damage risk is too high. The best strategy is to pursue the fastest speed without sacrificing too much coverage.
Time to Production – Time to production focuses on the time it takes for a developer’s checked-in code to become useful to end users. It’s a decent way to judge from a business perspective how quickly new stuff gets out the door. Measure the total time for each code check-in from when it is first committed to when it is deployed to production. Source control logs and deployment histories can be pieced together to measure the total time. It may be beneficial to split check-ins by feature area and to review distributions rather than averages. Short, consistent times are desirable. Long times reveal delays in testing, fixing, and deploying changes.
Pipeline Speed – Pipeline speed is a DevOps-y metric. Measure the total start-to-end time from triggering the build pipeline to the final deployment, and measure the time taken by each stage. This will give insights into bottlenecks, such as: system resource exhaustion, network delays, being stuck in job queues, tests that are too long, etc. Knowing each stage will indicate where the greatest optimizations can occur. For example, parallel test execution can significantly reduce total pipeline time. Use pipeline speed metrics to find efficiencies, not to justify cutting vital stages. Most modern continuous integration systems should provide time metrics.
Test Coverage per Time Period – There is always a tradeoff between test coverage and delivery speed. Assuming tests have optimally efficient execution times, higher coverage means slower delivery. Whenever time periods are fixed (such as CI pipeline limits or release deadlines), the best strategy is to maximize test coverage during the available time. For this purpose, coverage should be heuristically scored in terms of feature coverage priority (or the importance of the behaviors under test), not so much in terms of numerical code coverage. Then, for each test, divide the coverage score by the execution time. Sort tests by this ratio, and select the tests with the greatest scores until the total test execution time reaches the time limit. This approach guarantees that maximal test coverage will be achieved in the given period. It may also be advantageous to determine a threshold score for minimal coverage – if the maximum score for a given time period is below the minimal coverage threshold, then the time period should be increased. This metric is compelling if, for example, a CI pipeline needs more time for tests but managers are hesitant to slow down delivery.
Note: The metrics here cover speed after code is checked in, focusing on operational excellence. Metrics covering speed before code is checked in are important but are typically already covered by standard processes (like Scrum’s velocity). There are several ways to measure speed before code check-in: development time, backlog age, story completion rate, etc. Slow times before check-in indicate that a team is overloaded with work, lacks focus on priorities, or is being disrupted too frequently. However, one major caution for these metrics is that they are difficult to accurately measure, and they presume artifacts are logged precisely at event times. For example, if a story ticket is not created until a week after a new feature was first inspired, then the actual times measured will be inaccurate.
Feedback Notification
Quality Aspect
How quickly does a team identify problems?
Desired State
Fast – Fast feedback helps teams resolve issues quickly before they become more costly.
Metrics
Software development is the poster child for Murphy’s Law: anything that can go wrong will. Problems will happen. Metrics targeting perfection (such as 100% pass rates or 0-bug counts) are foolishly impossible and hopelessly destructive. Instead, metrics should gauge feedback loops – how well a team handles problems as they arise. Feedback has two parts: (1) notification time to discover and report problems, and (2) response time to fix problems. Ultimately, the sum should be minimal, but separating the parts identifies bottlenecks. This section covers notification.
Code Review Effectiveness – Code reviews are often the second line of defense against bugs (the first line being the author themselves). They grant an opportunity for other experts to inspect code for problems before fully committing changes. However, measuring the effectiveness of code reviews can be tricky. A few metrics to consider are:
Percentage of code check-ins that undergo review, if the team notoriously skips reviews
Average review turnaround time, if reviews are ignored
Code change size in terms of line number or another similar unit, if reviews are too large for teams to handle effectively
Issues caught, whenever a review successfully identifies and resolves an issue
Issue Discovery Time – The sooner issues are discovered, the less costly they are to resolve. “Issues” typically mean defects in the product (e.g., “bugs”), but they could include problems with the environment, deployment, or tests. The simplest form of issue discovery time is the measurement from when a pipeline starts to the time the issue is discovered. More advanced measurements can track time back to the root cause, such as when code containing a bug was committed, but these may be difficult to gather or may be less accurate. Issue types should be analyzed as separate distributions. Look specifically for blocking issues that appear late in the pipeline, such as critical services being down, and add checks early in the pipeline to discover them ASAP.
Bugs per Phase – Raw bug counts, like test counts, are not helpful beyond soundbites, but the proportions of bug counts per phase are useful for determining test effectiveness. A well-engineered pipeline should have meaningful phases (or “stages” or “steps”) with feedback after each one. A typical pipeline could have phases for build, unit tests, integration tests, end-to-end tests, and production deployment. Ideally, bugs should be caught in the shortest time, at the lowest level, and in the earliest phase. For example, if the majority of bugs are caught by end-to-end tests or (gasp!) in production, then the lower-level tests might need stronger coverage.
Feedback Response
Quality Aspect
How quickly does a team resolve problems once they are found?
Desired State
Fast – Again, resolve issues quickly before they become more costly.
Metrics
Time to Fix a Broken Build – Build health is vital for successful software development, especially in continuous integration. After a build is broken, it must be fixed ASAP so that it does not block progress. “Fixing” a build means that the pipeline can run to completion with an acceptable test passing rate. Fixing a build may mean:
Fixing a bug in the product
Fixing a problem in the environment, deployment, or tests
Reverting a code check-in that caused a bug
Updating tests to somehow flag the failure
Subverting safety checks (like removing tests or skipping phases) is not acceptable because it doesn’t truly fix the build’s underlying problems.
Measure the time it takes from when a pipeline reports a broken build to when the pipeline produces the first subsequent working build. The distribution of these times will reveal the team’s dedication to build stability. Clearly, shorter times are better. When broken builds are caused by code changes, the author should favor reverting check-ins over attempting fixes for faster recovery speed.
Time to Resolve Bugs – While the time to fix a broken build focuses on immediate product stability, the time to resolve bugs focuses instead on ultimate correctness. Just because a build is fixed does not mean a bug is necessarily fixed – tests may mark it as an acceptable failure, or the code containing the bug may simply be reverted. The time to resolve a bug is the total time from when the bug was first discovered to when it is fixed or otherwise closed (such as being marked as invalid or won’t fix). Bug tracker tools should easily provide this data. Bugs should be separated by severity when analyzing resolution times. Bugs should be resolved quickly, with priority given to higher-severity bugs. Resolution time metrics indicate if bugs are addressed adequately and in the proper order. Long resolution times may indicate overloaded teams, tolerance of low quality, or the need for redesign/refactoring.
