Release of IPython 5.0
blog.jupyter.org
blog.jupyter.org
The most interesting part of this for me is that IPython 6 will not support Python 2.
>Projects such as Matplotlib and SymPy plan to drop support in the next few years, while a few projects like Scikit-Bio are already ahead of us, and should be Python 3 only soon.
This was also very surprising for the standard reasons, especially for a library like matplotlib. Glad to find Python moving forward. But what will companies stuck on Python2 do? Will libraries like numpy, matplotlib, and scipy all maintain a Python2 LTS?
That's already false in many cases for at least a year. Arch and Gentoo come to mind.
Several libs today are Python 3 only. Many, many more are not going to pay the cost of supporting Python2 and drop it in next few years.
Unless you are retiring next couple years or live with head in sand, any new code you write should either by python2-3 compatible (it's not hard at all, do it all day every day) or Python 3 only.
Joking aside, Python 3 is the only Python. The only people who should use Python 2 are those supporting legacy codebases, and even they should start migrating.
"Hey folks. I've got to do lots of work that will result in no new visible functionality and I'd like to be paid for it"
I did consider slipping a Python 3 upgrade in there at the same time but there are limits to my powers of persuasion.
So - Python 3 will have to wait.
Something like 2 days maintenance every 9 months to stay current, or 4 days every 2-3 years to go from LTS to LTS. If a customer doesn't understand why they need to be paying maintenance every x months or years, isn't that your fault for not making that clear in the beginning? Sorry if it seems like I'm picking on you, but I've seen this kind of comment quite a lot, and I've never asked the question before.
In other words, they understand continuous maintenance, but not continual maintenance. Anything else is either too awkward to write on a budget. If you suggest that it needs periodic review, they'll think you just need to "do it right the first time, so this isn't an issue going forward", whether or not that makes any sense.
However for many reasons it's often tricky:
1. This is something you have to address in the initial stages of a client relationship and with price-sensitive clients unused to software development if you're not careful it can sound like you're just rent-seeking.
2. 'Big but infrequent' changes are difficult to cost. Django LTS upgrades are every 5 years. Breaking changes like Python 2 to 3 are even rarer. What figure would I have proposed during the sales process that would have correctly covered me for these? I'm almost certain I would have significantly underestimated.
3. It's not always clear if you're entering into a long-term relationship with the client. They might expect the website to be completely replaced in a year or so, they might be intending to bring development in-house or various other things that make 'saving for a rainy day' a tricky proposition.
I've also found that clients will often be acquired, go through management changes, etc that make having these things built in become unstable. One of my clients was acquired by Merck, then Merck sent a letter saying they were terminating all contracts. 18 months later, I get a frantic e-mail from some low-level people asking why I was no longer updating things for them.
The median life of many companies makes building in release->release upgrade support difficult in practice.
1. Yes. I always put the on-going fee in the initial project estimate, so there's no surprises.
2. Yes, it requires some guess-work. I've also found that it saves time to take one feature migration (like removing django's url patterns() function) and making that change to all projects at the same time (rather than making all the upgrades to one project at once). I also have found it helpful to reduce the number of 3rd party dependencies. (Fewer things to upgrade, and fewer things that may potentially stop getting supported.)
(Also "Django LTS upgrades are every 5 years" - I was going to correct that to 2 years, but I suppose you can completely skip an LTS line if you time it just right (like, jump from 1.8 directly to 2.0). But you still need to upgrade every _4_ years. Right?)
3. Yes, it's something to figure out initially.
For new, smaller projects, that don't have too many external dependencies - Python 3 is fine. Again, even in these cases, the benefits are largely superficial.
Out of interest whats the biggest time sink while doing this? I've had a lot of luck using 2to3 and with decent test coverage and a UAT environment that people can access, its not exactly months and months of work changing "xrange" to "range", and "print" to "print()"
You were indeed very lucky if all projects you've worked on had decent test coverage.
In order to ease porting, I've created a modified Python 3.6 interpreter that generates warnings in a lot of the cases that real Python 3 would generate an exception. See https://github.com/nascheme/ppython .
The goal is to have a 2to3 script that generates something that will run, with warnings, under me modified Python. Once you fix all the warnings, your code should run correctly in Python 3.
Surely that's a bug in the system that you've just uncovered and fixed? Py2 used to use the memory address of the object when comparing by default, which is just... crazy, especially for a language with so few WTFs:
>>> object() > object()
True
>>> object() > object()
False
>>> object() > object()
True
>>> object() > object()
False
That looks amazing! We're going to end up porting a fairly large + critical Django app to py3 and I definitely think this could ease some of the pain. I'm going to give it a go when I get the chance.
Its true that that's crazy, but, OTOH, everything-can-be-sorted is a useful feature (which Erlang has, for instance). Py3 could conceptually have retained it with a different implementation (this would probably still have been a breaking change from Py2, but not a feature loss.)
The cost is justified if you want to use a supported language.
Obviously when I say "I will support it", I don't mean myself, single-handedly. I mean my company. Or someone else's company. The point is, commercially speaking, nobody cares about official PSF support. The world will go on unaffected, with or without it.
Saying new versions will not be Python 2 compatible is not a request for Companies to upgrades. It's just a statement that they will be stuck on old version of libraries, unless they buy* support for a vendor.
We (the IPython team) have no objection if Continuum or Enthought provide a Paying version of IPython 6.x that is Python 2 compatible, but that's their problem. It does not prevent either the core developer of the Python-3 only project to consult on potential Python 2 internal fork.
I honestly think that this would be a much sane model that would make both sides of the Py2vsPy3 battle happy.
The end of life of python 2, and the end of python 2 support in popular libraries isn't "a damn good reason"?
Note - I'm the co-chair for PyDataLondon (and the local conference series). I regularly poll my monthly meetup audience to see how many folk upgrade each month. Normally 1-10 per month stick their hand up, nobody sticks their hand up if I ask "how many downgraded from Python 3 this month?".
All my python 2 only code explicitly invokes python 2 on the shebang line.
This can be read two very different ways. ;)
1) Get caught by surprise because they aren't planning. 2) Plan and pay the price of upgrading their legacy code. 3) Join an active developed (currently hypothetical) fork of python 2
I think in 2020 we'll see all 3 routes done by several companies. Better get the popcorn ready!
RedHat is probably going to keep Python2 on some degree of life-support at least until 2030 or so, just based on a guess that RHEL8 most likely will still have python2, and its support will continue for 10+ years after its release.
4) migrate to other languages, such as Go
... which is not uncommon, and in many cases a pretty good idea.
This depends heavily on the type of work someone is doing. People doing numeric/scientific computing work with Python can't really do this, for example, because there's a distinct lack of other languages with equivalents to the libraries and tooling Python gives them for their work. And that's a chicken/egg feedback loop problem: Go needs the libraries and tools, but in order get them needs people to move, but in order to get people to move needs the libraries/tools, and so on.
All in all, this is a bad recommendation. Julia is version 0.4. Its syntax changes with every version. It is not going to be stable soon. It is so far from stable that it doesn't have a plan for what stability will look like.
It's fine if you want to get in on the ground floor of a programming language, or to learn a new language for the fun of it, but it's completely unreasonable to suggest that you could replace a programming language that people use for their jobs that way.
The way I understand this is that you will need python 3 to open ipython 6, but once running, you can interact with python2 and run and inspect python 2 code. I think that is fine. I can't imagine a modern scientific setup that can't readily create python 2 and 3 virtual environments.
So it will only be possible to run Python 3 code in IPython 6, but it will still be possible to use IPython 5 as a kernel for future versions of Jupyter.
On the other hand, I'm really happy to say farewell to readline. I've been stuck with readline v6.2.4.1 for ages just so I can have proper linewrap [0]. Of course this breaks virtualenv creation so you end up having to override the system readline [1]. Needless to say, this is well overdue.
[0]: https://github.com/ipython/ipython/issues/3329/
[1]: https://github.com/pypa/virtualenv/issues/4#issuecomment-966...
The best feature I've discovered so far, is that when I want to change a function, I can simply 'up arrow', and I get the whole thing! Not a single line of the function, as in the normal python terminal. And if I write a syntax error while typing the function, it tells me immediately!
Does anybody have other examples of great features in ipython over the standard python terminal?
python3 -ic "import readline; import rlcompleter; readline.parse_and_bind('tab: complete')"
Another feature I end up using constantly is the documentation support. Say you want to read the docstring for an object, just prefix or append a '?' to the object, e.g. 'subprocess?'.
If you want to examine the source code for the object, just prefix or append '??', e.g. 'subprocess.call??'.
Usually I can paste code just fine, I think? As long as the indentation is correct and I have double blank lines between functions, etc.
> Say you want to read the docstring for an object, just prefix or append a '?' to the object, e.g. 'subprocess?'
Isn't that the same as typing `help(subprocess)` in the standard terminal?
> If you want to examine the source code for the object, just prefix or append '??', e.g. 'subprocess.call??'.
That's pretty cool :-)
class Sample:
def __init__(self):
print('hehe')
s = Sample()
https://gist.github.com/gknoy/97de793db43ff7ea4d4bfd1bab96b4...
It seemed to work fine when I didn't use `%cpaste`, though, so that might be a vestigial thing? (I remember having to do it on my linux workstation, so maybe it's just my mac terminal behaving differently.)
The cool thing about directly pasting is, if you fumble the indentation like I did when I initially pasted, it auto-indents for you. ;)
We got this feature with no effort on our part - it's built into prompt_toolkit.
So much nicer than messing around with subprocess and pipes.
See tutos like https://www.youtube.com/watch?v=xe_ATRmw0KM , most of what works in the notebook work in IPython.
But yes you can do things like:
files = ! ls *.txt
for name in files:
with open(name) as f:
if 'Content-Type: text/x-rst' in f.read():
!git mv {name} {name[:-3]}rstI started out adding to the launch script so that I would have data and helper functions ever-present in my terminal when I was working to rapidly answer Stack Overflow questions, especially for often repeated tropes like group-by in pandas or some broadcasting issues in numpy.
But then I started accumulating other things, like the boilerplate for reflecting some local toy databases using SQLAlchemy, and some toy stuff with metaclasses and decorators, and kept going from there.
You should read the docs on IPython configuration -- you can even create different profiles and then launch IPython with a specific profile if you want to get different defaults in your launch environment for different use cases [1].
[0] < https://github.com/spearsem/configs/blob/master/ipython_conf... >
[1] < http://ipython.readthedocs.io/en/stable/config/intro.html >
Extremely handy.
I want to execute through lines with cmd-return, or by highlighting and pressing cmd-return, and then see the change in the variables in a separate pane, like RStudio's environment pane. Bonus points if I can click on table variables in the environment pane and examine them in a separate tab with sorting and searching. Spyder comes closest but the execution part doesn't work as fluidly.
this might be an upcoming feature for beaker notebook (it definitely has the tables with sorting and searching).
It is extremely handy to have a spreadsheet like "variable explorer" handy to get an overview of what you have in memory and what your numbers look like and a feature that I miss from rStudio.
Syntax highlighting as you type
Real multi-line editing (up and down arrow keys move between lines)
Multi-line paste without breaking indentation or immediately executing code
Better code completion interface (we plan to improve that more)
Optional mouse supportI was waiting for something like this for years!
tldr; add `c.TerminalInteractiveShell.editing_mode = 'vi'` to ipython_config.py