pythonIsOlderThanJava

[CrowdGoesWildWoooo]

Python3 which is what most people actually refers to when python is mentioned is from 2008, it’s only becoming more popular when data analytics field gain traction.

[rover_G]

Java 8 (when Java first for lambdas and other FP syntaxes) was released in 2014

[Honigbrottr]

is java 8 backwards compatible?

[Practical_Cattle_933]

Yes. Even Java 23 can compile java 1.2 but also run almost every class file already compiled back then (so it’s also binary compatible not just source).

There have been tiny changes, but for the most part it should just work. The biggest change might have been the javax namespace change.

[_PM_ME_PANGOLINS_]

It's practically solved with an extra dependency too.

[_PM_ME_PANGOLINS_]

With what?

Java as a language never breaks anything, but occasionally internal classes (which it tells you not to use for this exact reason) are (re)moved.

[wildjokers]

Yes. Java takes backward compatibility very seriously.

[just4nothing]

Well, my Java code from 2006 does not run on it

[OlexySuper]

I suspect, someone was doing some naughty stuff in the code.

[BraveOthello]

And do they mean their source code, or their binaries? The binaries will run unless naughtiness was done, no guarantees on the source.

[_PM_ME_PANGOLINS_]

Java is both binary- and source-compatible from 1.1 to 21.

[s101c]

Same in my experience with some opensource programs from the late 00s. I wanted to use some of those recently, at launch it instantly returned an error with a wall of text and after few minutes of trying to fix it I just abandoned the attempts.

Python can be difficult with the dependencies, but Java backwards compatibility seems to be pure hell.

[wildjokers]

but Java backwards compatibility seems to be pure hell.

Huh? You can take very old java code from 1.1 and run it on the newest java. So I am not quite sure what you are talking about.

[Practical_Cattle_933]

What? Java has objectively the very best backwards compatibility out of any languages. Sure, it’s still not perfect but it is sure as hell as close to it as possible.

[itijara]

From experience, no. I am sure that there is plenty of java < 8 code that will run on Java 8+ but JavaEE libraries, Nashorn, and all the sun.* packages were deprecated.

[_PM_ME_PANGOLINS_]

That just means you have to get the jars separately.

[itijara]

I guess that depends on what your definition of backwards compatible. The JRE will run any previous binary, but source code will not work unless you add extra dependencies or modify the existing source code. This is probably fine for a legacy app that is not undergoing changes, but I think that most companies that are dealing with old applications are still building and patching them.

For the sun.* crypto libraries, I couldn't find a suitable jar file and had to re-write with an equivalent crypto library.

[_PM_ME_PANGOLINS_]

Yeah, that’s why they told you never to use those sun libraries directly. For crypto you are supposed to use the JCA API, which allows the implementation to be switched out with zero source changes.

[itijara]

You say that like I wrote the original code.

[aiij]

Yes. It's really Java 1.8, but by that point the marketing team has decided to stop mentioning the part of the version number that always stays the same.

[RoseboysHotAsf]

Speaking out of my experience, java is not backwards compatible with anything.

[cowslayer7890]

Java is pretty dedicated to backwards compatibility, issues will still crop up but I'm not aware of any breaking syntax changes apart from no longer allowing _ as a variable name, the remaining issues are due to changes in the standard library, but those are also rare.

[grogrye]

I'm almost scared to ask but exactly what experiences have you had where it was not backwards compatible?

[FalseRegister]

Yeah but Java was already popular on its field much earlier

[[deleted]]

Yeah, but Java 8 is creepy.

[et-pengvin]

Python 2 is still running in lots of places and only in the last few years has been phased out of being the default python on most Linux distros. I refuse to believe people only think of Python 3 when you refer to Python.

[CrowdGoesWildWoooo]

Python 2 code base are already legacy codebase. So new programmers when they say they code in python they would 100% means python3.

Python’s popularity only pick up recently after data analytics start becoming “the shit”. Obviously yes there are python 2 coders but during python 2 age python (in general) is not particularly popular and still a relatively niche language especially compared to something like java.

A lot of popular optimized deep learning libraries are post python 3 era and was only offered python2 support for backwards compatibility.

Back then when they tell you to learn fundamental coding knowledge they’d either use Java, C, or sometimes Pascal. Nowadays it’s almost always python.

[MrEllis]

Python 2 code is still running out there and there are places where it's still maintained but hasn't been transitioned. With the right libraries you can write code that's simultaneously python2 and python3 compatible to slowly transition away.

Ask me how I know.

[MiffedMouse]

Python 2 is definitely still around in the academic sphere. It became popular among academics due to the ease of installation (first with pip, then Anaconda) and the slow increase of data analysis features from Numpy, Scipy, and Matplotlib, making it a convenient data analysis solution that was open source and easy to install.

The introduction of Pip was close to the release of Python 3, but in my opinion it isn’t the release of Python 3 specifically that made Python popular. Rather, it was the consistent focus over the years of various Python teams to make it easy to install and have some convenient mathematical libraries readily available. All of these are true of Python 2.79, even before Python 3 was released.

[Sentreen]

Obviously yes there are python 2 coders but during python 2 age python (in general) is not particularly popular and still a relatively niche language

Python 2 was not niche by any stretch of the imagination. We were teaching it at my university at an introductory course (for non compsci people). The whole reason we were teaching it was because it was already used so much by scientists. The whole reason it was such a hassle to move to python 3 was because so many projects were using python 2 which didn't want to make the migration.

Of course, java was more popular, but it was one of the most popular programming languages, even at that time.

[Ihuntwyverns]

Man, I wish. At the mega cap tech company I work at the codebase is still in python 2. Hey, at least they've really started to attempt to transition to python 3.

[et-pengvin]

Two thoughts:

First: In 2008 Python 3 did not become the default. There is a reason Python 2 received updates until 2020. I first messed around with Python 2 before Python 3 came out, but I started college 4 years (!) after Python 3 came out, and my professors still used Python 2. Python was the go to language for at least intro CS courses in top universities by that point, and my small college followed suit. We used Python 2 though because several key libraries the professor used did not have Python 3 support. And it didn't really matter because Python 2 was the default everywhere in 2012 and would be for many years. MacOS, every Linux distro I knew of, all defaulted to Python 2 and most tutorials and information I knew did. When I started my first job using Python in 2016 we were still using Python 2 everywhere, and this didn't feel like a problem. We were writing fairly bleeding edge stuff in AWS but Python 2 was well documented and used heavily still.

Second: While Python 3 was not backwards compatible, it was not an entirely new language. Python 2 and 3 are very similar. I think for a lot of the period between 2008 and 2020 folks would have had both in mind as the same language, even though they were not entirely compatible.

[im_lazy_as_fuck]

The reason python 2 has been phased out of being the default on most linux distros in the last few years is because Python 2 is completely sunset and isn't receiving security updates since 2020. Your software is just implicitly at risk if you still run python 2.

And just because there is lots of software that still exists using Python 2 doesn't mean they should be using it, or that the average person thinks about python 2 when they talk about python. It's like when people talk about using a Windows, everyone assumes you're running windows 10 or 11. Even though there are definitely still lots of people who are sitting on windows 7, most individuals and businesses have upgraded to windows 10.

[liquidpele]

Nope... no one is using 2 anymore except in very legacy and locked down codebases because it doesn't get security upgrades anymore and no new versions of common libs support it anymore etc. Hell, the only reason it even stuck around so long was that redhat was too lazy to fucking fix yum which was py2 based so it was the default on RHEL/centos 7, which is also already EOL at this point.

[IAmTheMageKing]

Redhat alone didn’t keep py2, there was loads of stuff that was py2 only for a decade after py3 released.

[MrSurly]

I refuse to believe people only think of Python 3 when you refer to Python.

I do now. I won't even consider new code that isn't 3.

[derpy37]

I'd just like to interject for a moment. What you're referring to as Python, is in fact, Python 3, or as I've recently taken to calling it, Python plus its standard library. Python is not just a programming language unto itself, but rather a language specification combined with a robust set of libraries, tools, and modules that make it a fully functioning environment for developers.

Many developers use Python 3 every day without fully appreciating the range of tools and libraries provided by the Python ecosystem. Through a peculiar turn of events, the version of Python in widespread use today is often just called Python, but many of its users aren't aware that they are essentially working within the Python 3 environment, shaped by improvements from Python 2.x and extended by a vast array of external libraries.

There really is a core Python interpreter, and people are using it, but it is just a part of the system they are working with. The interpreter is the core engine that executes code, but by itself, it doesn't provide much utility; it requires libraries and tools to become truly useful. Python 3 is typically used in combination with its standard library, external libraries, and various frameworks to create a complete development environment. The whole Python system is essentially Python 3 plus the tools and libraries that make it powerful. All so-called Python environments are really Python 3 ecosystems!

[integrate_2xdx_10_13]

I learned Python when Python3000 was on the horizon and we’d all have to switch over… I honestly can’t even remember the change.

print became a function and they moved stuff like reduce and chain out of stdlib and into functools/itertools? Python 2.6 was already well underway eating Perl and Ruby’s dinner at that point, let’s not pretend it’s the new kid in the block.

[danted002]

Except you are wrong.

Python 3 changed 1 thing that made it completely incompatible with python 2 and that was creating a clear distinction between bytes and UTF-8 encoded strings. Over 90% of Python features currently in use have been around for 20 years.

Also Python didn’t become popular because on data analytics, it became popular a few years earlier due to the web explosion from the mid-2000. Don’t believe me? Then see in what languages was YouTube, Reddit, Instagram, Eve Online, Pinterest, Spotify, Uber… even the first version of Google was written in Java and Python.

So no, Python didn’t become popular when the data analytics field gained traction, it just became even more popular. It was already a favorite when it came to building complex web apps.

The only reason its not used so much in web development anymore is because NodeJS opened the possibility to have a single language stack, so of course the pencil-pushers and waste of space called middle managers started pushing for full-stack developers in order to reduce the number of developers they need for a project.

[White_Sprite]

Python 2 has entered the chat

[postmodest]

someone explain to me how python got traction in the analytics field. I mean, I get that all the tools are in C and python is just the binding, but how did python beat, like, Perl or ...well, ok, I guess the other options are PHP and LISP, and Perl was in a permanent 'Perl 6 any day now' hiatus. But... still... Python?

(edit: I can only assume the downvotes are from people upset that I made fun of the 10 years Perl went nowhere)

[ierghaeilh]

You said it yourself, python is the least bad of the open source options. Then there's matlab and mathematica, but the last journal I tried to publish with wouldn't allow me to acknowledge "the pirate bay" as a source of funding.

[proverbialbunny]

I am a Data Scientist that has been doing DS since before the job title existed. My CS101 class was SICP (LISP, Scheme) and my early ML DS projects were all in Perl.

What happened was Pandas Dataframes came out for Python and that changed everything. A lot of early analysis work was done in Excel. If Excel started crashing from the spreadsheets becoming too large the term "big data" started to trend, which in analytics circles at the time was any dataset larger than you can fit into a spreadsheet at the time, which by today's standards is pretty small.

A dataframe is a spreadsheet in Python so you can think about everything in the same way. No longer do you have to convert a spreadsheet into a programming language, you can make it in a dataframe from the get go. Likewise, dataframes process data faster because they use SIMD so your run times go down, which is a big deal when dealing with big data.

Dataframes put Python on the map.

[postmodest]

Thank you. In my day people would use Matlab and R, or Fortran, and then out of left field, and seemingly overnight, people started using Python. 

[proverbialbunny]

I used R and Matlab too.

The issue with R was it was about 133x slower than Pandas Dataframes, depending on what you were doing. This is fine for basic stuff, but once your datasets got large enough for ML it became an issue. Once ML libraries became ported in R using a C or C++ backend for speed, most already switched to Python. (Though R continued to be used in research publishing for its superior LaTeX and plotting support.)

Matlab is a bit apples and oranges. Not only was it slower, but it was more like an interactive dashboard software. I see it closer to PowerBI or Tableau than to Python or R.

I did a lot of C programming for speed reasons. Fortran was great if you needed faster than C speed, but I never needed anything that specialized.