Tag: python

Code Density

As many of the regular readers of this blog know, I largely use R for most of my data work. There have been a few occasions when I’ve tried to use Python, but have found that I’m far less efficient in that than I am with R, and so abandoned it, despite the relative ease of putting things into production.

Now in my company, like in most companies, people use both Python and R (the team that reports to me largely uses R, everyone else largely uses Python). And while till recently I used to claim that I’m multilingual in the sense that I can read Python code fairly competently, of late I’m not sure I am. I find it increasingly difficult to parse and read production grade python code.

And now, after some experiments with ChatGPT, and exploring other people’s codes, I have an idea on why I’m finding it hard to read production-grade Python code. It has to do with “code density”.

Of late I’ve been experimenting with Spark (finally, in this job I do a lot of “big data” work – something I never had to in my consulting career prior to this). Related to this, I was reading someone’s PySpark code.

And then it hit me – the problem (rather, my problem) with Python is that it is far more verbose than R. The number of characters or lines of code required to do something in Python is far more than what you need in R (especially if you are using the tidyverse family of packages, which I do, including sparklyr for spark).

Why does the density of code matter? It is to do with aesthetics and modularity and ease of understanding.

Yesterday I was writing some code that I plan to put into production. After a few hours of coding, I looked at the code and felt disgusted with myself – it was a way too long monolithic block of code. It might have been good when I was writing it, but I knew that if I were to revisit it in a week or two, I wouldn’t be able to understand what the hell was happening there.

I’ve never worked as a professional software engineer, but with the amount of coding I’ve done, I’ve worked out what is a “reasonable length for a code block”. It’s like that apocryphal story of Indian public examiners for high school exams who evaluate history answers based on how long they are – “if they were to place an ordinary Reynolds 045 pen vertically on the sheet, the answer should be longer than that for the student to get five marks”.

An answer in a high school history exam needs to be longer than this. A code block or function should be shorter than this

It’s the reverse here. Approximately speaking, if you were to place a Reynolds pen vertically on screen (at your normal font size), no function (or code block) can be longer than the pen.

This easily approximates how much the eye can see on one normal Macbook screen (I use a massive external monitor at work, and a less massive, but equally wide, one at home). If you have to keep scrolling up and down to understand the full logic, there is a higher chance you might make mistakes, and higher difficulty for someone to understand the code.

Till recently (as in earlier this week) I would crib like crazy that people coding in Python would make their code “too modular”. That I would have to keep switching back and forth between different functions (and classes!!) to make sense of some logic, and about how that would make codes hard to debug (I still think there is a limit to how modular you can make your ETL code).

Now, however (I’m writing this on a Saturday – I’m not working today), from the code density perspective, it all makes sense to me.

The advantage of R is that because the code is far denser, you can pack in a far greater amount of logic in a Reynolds pen length of code. So over the years I’ve gotten used to having this much logic being presented to me in one chunk (without having to scroll or switch functions).

The relatively lower density of Python, however, means that the same amount of logic that would be one function in R is now split across several different functions. It is not that the writer of the code is “making this too modular” or “writing functions just for the heck of it”. It is just that their “mental Reynolds pens” doesn’t allow them to pack in more lines in a chunk or function, and Python’s density means there is only so much logic that can go in there.

As part of my undergrad, I did a course on Software Engineering (and the one thing the professor told us then was that we should NOT take up software engineering as a career – “it’s a boring job”, he had said). In that, one of the things we learnt was that in conventional software services contexts, billing would happen as a (nonlinear) function of “kilo lines of code” (this was in early 2003).

Now, looking back, one thing I can say is that the rate per kilo line of R code ought to be much higher than the rate per kilo line of Python code.

Cross posted on my now-largely-dormant Art of Data Science newsletter

The Second Great Wall (of programming)

Back in 2000, I entered the Computer Science undergrad program at IIT Madras thinking I was a fairly competent coder. In my high school, I had a pretty good reputation in terms of my programming skills and had built a whole bunch of games.

By the time half the course was done I had completely fallen out of love with programming, deciding a career in Computer Science was not for me. I even ignored Kama (current diro)’s advice and went on to do an MBA.

What had happened? Basically it was a sudden increase in the steepness of the learning curve. Or that I’m a massive sucker for user experience, which the Computer Science program didn’t care for.

Back in school, my IDE of choice (rather the only one available) was TurboC, a DOS-based program. You would write your code, and then hit Ctrl+F9 to run the program. And it would just run. I didn’t have to deal with any technical issues. Looking back, we had built some fairly complex programs just using TurboC.

And then I went to IIT and found that there was no TurboC, no DOS. Most computers there had an ancient version of Unix (or worse, Solaris). These didn’t come with nice IDEs such as TurboC. Instead, you had to use vi (some of the computers were so old they didn’t even have vim) to write the code, and then compile it from outside.

Difficulties in coming to terms with vi meant that my speed of typing dropped. I couldn’t “code at the speed of thought” any more. This was the first give up moment.

Then, I discovered that C++ had now got this new set of “standard template libraries” (STL) with vectors and stuff. This was very alien to the way I had learnt C++ in school. Also I found that some of my classmates were very proficient with this, and I just couldn’t keep up with this. The effort seemed too much (and the general workload of the program was so high that I couldn’t get much time for “learning by myself”), so I gave up once  again.

Next, I figured that a lot of my professors were suckers for graphic UIs (though they denied us good UX by denying us good computers). This, circa 2001-2, meant programming in Java and writing applets. It was a massive degree of complexity (and “boringness”) compared to the crisp C/C++ code I was used to writing. I gave up yet again.

I wasn’t done with giving up yet. Beyond all of this, there was “systems programming”. You had to write some network layers and stuff. You had to go deep into the workings of the computer system to get your code to run. This came rather intuitively to most of my engineering-minded classmates. It didn’t to me (programming in C was the “deepest” I could grok). And I gave up even more.

A decade after I graduated from IIT Madras, I visited IIM Calcutta to deliver a lecture. And got educated.

I did my B.Tech. project in “theoretical computer science”, managed to graduate and went on to do an MBA. Just before my MBA, I was helping my father with some work, and he figured I sucked at Excel. “What is the use of completing a B.Tech. in computer science if you can’t even do simple Excel stuff?”, he thundered.

In IIMB, all of us bought computers with pirated Windows and Office. I started using Excel. It was an absolute joy. It was a decade before I started using Apple products, but the UX of Windows was such a massive upgrade compared to what I’d seen in my more technical life.

In my first job (where I didn’t last long) I learnt the absolute joy of Visual Basic macros for Excel. This was another level of unlock. I did some insane gymnastics in that. I pissed off a lot of people in my second job by replicating what they thought was a complex model on an Excel sheet. In my third job, I replaced a guy on my team with an Excel macro. My programming mojo was back.

Goldman Sachs’s SLANG was even better. By the time I left from there, I had learnt R as well. And then I became a “data scientist”. People asked me to use Python. I struggled with it. After the user experience of R, this was too complex. This brought back bad memories of all the systems programming and dealing with bad UX I had encountered in my undergrad. This time I was in control (I was a freelancer) so I didn’t need to give up – I was able to get all my work done in R.

The second giving up

I’ve happily used R for most of my data work in the last decade. Recently at work I started using Databricks (still write my code in R there, using sparklyr), and I’m quite liking that as well. However, in the last 3-4 months there has been a lot of developments in “AI”, which I’ve wanted to explore.

The unfortunate thing is that most of this is available only in Python. And the bad UX problem is back again.

Initially I got excited, and managed to install Stable Diffusion on my personal Mac. I started writing some OpenAI code as well (largely using R). I started tracking developments in artificial intelligence, and trying some of them out.

And now, in the last 2-3 weeks, I’ve been struggling with “virtual environments”. Each newfangled open-source AI that is released comes with its own codebase and package requirements. They are all mutually incompatible. You install one package, and you break another package.

The “solution” to this, from what I could gather, is to use virtual environments – basically a sandbox for each of these things that I’ve downloaded. That, I find, is grossly inadequate. One of the points of using open source software is to experiment with connecting up two or more of them. And if each needs to be in its own sandbox, how is one supposed to do this? And how are all other data scientists and software engineers okay with this?

This whole virtual environment mess means that I’m giving up on programming once again. I won’t fully give up – I’ll continue to use R for most of my data work (including my job), but I’m on the verge of giving up in terms of these “complex AI”.

It’s the UX thing all over again. I simply can’t handle bad UX. Maybe it’s my ADHD. But when something is hard to use, I simply don’t want to use it.

And so I’m giving up once again. Tagore was right.

How Python swallowed R

A week ago, I put a post on LinkedIn saying if someone else working in analytics / data science primarily uses R for their work, I would like to chat.

I got two responses, one of which was from a guy who strictly isn’t in analytics / data science, but needs to analyse large amounts of data for his work. I had a long chat with the other guy today.

Yesterday I put the same post on Twitter, and have got a few more responses from there. However, it is staggering. An overwhelming majority of data people who I know work in Python. One of the reasons I put these posts was to assure myself that I’m not alone in using R, though the response so far hasn’t given me too much of an assurance.

So why do most companies end up using Python for analytics, even when R is clearly better for things like data wrangling, reporting, visualisation, dashboarding, etc.? I have a few theories on this, and I think all of them come together to result in python having its “overwhelming marketshare” (at least among people I know).

Tech people clearly prefer python since it’s easier to integrate. So the tech leaders request the data science leaders to use Python, since it is much easier for the tech people. In a lot of organisations, data science reports into tech, so this request is honoured.

Even if it isn’t, if you recall, “data scientists” are generally tech facing rather than business facing. This means that the models they build need to be codified, and added to the company’s code base. This means necessarily working together with tech, and this means using a programming language that tech is comfortable with.

Then, this spills over. Usually, someone has the bright idea that the firm shouldn’t use two languages for what is essentially the same thing. And so the analytics people are also forced to use python for their analytics, even if it isn’t built for the purpose. And then it spreads.

Next is the “cool factor”. There is this impression that the more technical a solution is, the more superior it is, even if it has no direct business impact (an employer had once  told me, “I have raised money saying we are using machine learning. If our investors see the algorithms you’re proposing, they’ll want their money back”).

So a youngster getting into data wants to do “all the latest stuff”. This means machine learning. Deep learning. Reinforcement learning. And all that. There is an impression that this kind of work is “better work” compared to let’s say generating business insights using data. And in general, the packages for machine learning have traditionally been easier in Python than they are in R (though R is fast catching up, and in general python is far behind R when it comes to user friendliness).

Then, the growth in data and jobs associated with it such as machine learning or data engineering have meant that a lot of formerly tech people have got into data work. Python is fundamentally a programming language, with a package (pandas) added on to do data work. Techies find it far more intuitive than R, which is fundamentally a statistical software. On the other hand, people who are coming from a business / Excel background find it far more comfortable to use R. Python can be intimidating (I fall in this bucket).

So yeah – the tech integration, the number of tech people who are coming into data and the “cool factor” associated with the more techie stuff means that Python is gaining, at R’s expense (in my circle at least).

In any case I’m going to continue to use R. I’m at least 10X faster in R than I am in Python, and having used R for 12 years now, I’m too used to that way of working to change things up.

Programming Languages

I take this opportunity to apologise for my prior belief that all that matters is thinking algorithmically, and language in which the ideas are expressed doesn’t matter.

About a decade ago, I used to make fun of information technology company that hired developers based on the language they coded in. My contention was that writing code is a skill that you either have or you don’t, and what a potential employer needs to look for is the ability to think algorithmically, and then render ideas in code. 

While I’ve never worked as a software engineer I find myself writing more and more code over the years as a part of doing data analysis. The primary tool I use is R, where coding doesn’t really feel like coding, since it is a rather high level language. However, I’m occasionally asked to show code in Python, since some clients are more proficient in that, and the one thing that has done is to teach me the value of domain knowledge of a programming language. 

I take this opportunity to apologise for my prior belief that all that matters is thinking algorithmically, and language in which the ideas are expressed doesn’t matter. 

This is because the language you usually program in subtly nudges you towards thinking in a particular way. Having mostly used R over the last decade, I think in terms of tables and data frames, and after having learnt tidyverse earlier this year, my way of thinking algorithmically has become in a weird way “object oriented” (no, this has nothing to do with classes). I take an “object” (a data frame) and then manipulate it in various ways, changing it, summarising stuff, calculating things on the fly and aggregating, until the point where the result comes out in an elegant manner. 

And while Pandas allows chaining (in fact, it is from Pandas that I suspect the tidyverse guys got the idea for the “%>%” chaining operator), it is by no means as complete in its treatment of chaining as R, and that that makes things tricky. 

Moreover, being proficient in R makes you think in terms of vectorised operations, and when you see that python doesn’t necessarily offer that, and and operations that were once simple in R are now rather complicated in Python, using list comprehension and what not. 

Putting it another way, thinking algorithmically in the framework offered by one programming language makes it rather stressful to express these thoughts in another language where the way of algorithmic thinking is rather different. 

For example, I’ve never got the point of the index in pandas dataframes, and I only find myself “resetting” it constantly so that my way of addressing isn’t mangled. Compared to the intuitive syntax in R, which is first and foremost a data analysis tool, and where the data frame is “native”, the programming language approach of python with its locs and ilocs is again irritating. 

I can go on… 

And I’m guessing this feeling is mutual – someone used to doing things the python way would find R’s syntax and way of doing things rather irritating. R’s machine learning toolkit for example is nowhere as easy as scikit learn is in python (this doesn’t affect me since I seldom need to use machine learning. For example, I use regression less than 5% of the time in my work). 

The next time I see a job opening for a “java developer” I will not laugh like I used to ten years ago. I know that this posting is looking for a developer who can not only think algorithmically, but also algorithmically in the way that is most convenient to express in Java. And unlearning one way of algorithmic thinking and learning another isn’t particularly easy. 

Stirring the pile efficiently

Warning: This is a technical post, and involves some code, etc. 

As I’ve ranted a fair bit on this blog over the last year, a lot of “machine learning” in the industry can be described as “stirring the pile”. Regular readers of this blog will be familiar with this image from XKCD by now:

Source: https://xkcd.com/1838/

Basically people simply take datasets and apply all the machine learning techniques they have heard of (implementation is damn easy – scikit learn allows you to implement just about any model in three similar looking lines of code; See my code here to see how similar the implementation is).

So I thought I’ll help these pile-stirrers by giving some hints of what method to use for different kinds of data. I’ve over-simplified stuff, and so assume that:

  1. There are two predictor variables X and Y. The predicted variable “Z” is binary.
  2. X and Y are each drawn from a standard normal distribution.
  3. The predicted variable Z is “clean” – there is a region in the X-Y plane where Z is always “true” and another region where Z is always “false”
  4. So the idea is to see which machine learning techniques are good at identifying which kind of geometrical figures.
  5. Everything is done “in-sample”. Given the nature of the data, it doesn’t matter if we do it in-sample or out-of-sample.

For those that understand Python (and every pile-stirrer worth his salt is excellent at Python), I’ve put my code in a nice Jupyter Notebook, which can be found here.

So this is what the output looks like. The top row shows the “true values” of Z. Then we have a row for each of the techniques we’ve used, which shows how well these techniques can identify the pattern given in the top row (click on the image for full size).

As you can see, I’ve chosen some common geometrical shapes and seen which methods are good at identifying those. A few pertinent observations:

  1. Logistic regression and linear SVM are broadly similar, and both are shit for this kind of dataset. Being linear models, they fail to deal with non-linear patterns
  2. SVM with RBF kernel is better, but it fails when there are multiple “true regions” in the dataset. At least it’s good at figuring out some non-linear patterns. However, it can’t figure out the triangle or square – it draws curves around them, instead.
  3. Naive Bayesian (I’ve never understood this even though I’m pretty good at Bayesian statistics, but I understand this is a commonly used technique; and I’ve used default parameters so not sure how it is “Bayesian” even) can identify some stuff but does badly when there are disjoint regions where Z is true.
  4. Ensemble methods such as Random Forests and Gradient Boosting do rather well on all the given inputs. They do well for both polygons and curves. Elsewhere, Ada Boost mostly does well but trips up on the hyperbola.
  5. For some reason, Lasso fails to give an output (in the true spirit of pile-stirring, I didn’t explore why). Ridge is again a regression method and so does badly on this non-linear dataset
  6. Neural Networks (Multi Layer Perceptron to be precise) does reasonably well, but can’t figure out the sharp edges of the polygons.
  7. Decision trees again do rather well. I’m pleasantly surprised that they pick up and classify the disjoint sets (multi-circle and hyperbola) correctly. Maybe it’s the way scikit learn implements them?

Of course, the datasets that one comes across in real life are never such simple geometrical figures, but I hope that this set can give you some idea on what techniques to use where.

At least I hope that this makes you think about the suitability of different techniques for the data rather than simply applying all the techniques you know and then picking the one that performs best on your given training and test data.

That would count as nothing different from p-hacking, and there’s an XKCD for that as well!

Source: https://xkcd.com/882/

Python and Hindi

So I’ve recently discovered that using Python to analyse data is, to me, like talking in Hindi. Let me explain.

Back in 2008-9 I lived in Delhi, where the only language spoken was Hindi. Now, while I’ve learnt Hindi formally in school (I got 90 out of 100 in my 10th boards!), and watched plenty of Hindi movies, I’ve never been particularly fluent in the language.

The basic problem is that I don’t know the language well enough to think in it. So when I’m talking Hindi, I usually think in Kannada and then translate my thoughts. This means my speech is slow – even Atal Behari Vajpayee can speak Hindi faster than me.

More importantly, thinking in Kannada and translating means that I can get several idioms wrong (can’t think of particular examples now). And I end up using the language in ways that native speakers don’t (again can’t think of examples here).

I recently realised it’s the same with programming languages. For some 7 years now I’ve mostly used R for data analysis, and have grown super comfortable with it. However, at work nowadays I’m required to use Python for my analysis, to ensure consistency with the rest of the firm.

While I’ve grown reasonably comfortable with using Python over the last few months, I realise that I have the same Hindi problem. I simply can’t think in Python. Any analysis I need to do, I think about it in R terms, and then mentally translate the code before performing it in Python.

This results in several inefficiencies. Firstly, the two languages are constructed differently and optimised for different things. When I think in one language and mentally translate the code to the other, I’m exploiting the efficiencies of the thinking language rather than the efficiencies of the coding language.

Then, the translation process itself can be ugly. What might be one line of code in R can sometimes take 15 lines in Python (and vice versa). So I end up writing insanely verbose code that is hard to read.

Such code also looks ugly – a “native user” of the language finds it rather funnily written, and will find it hard to read.

A decade ago, after a year of struggling in Delhi, I packed my bags and moved back to Bangalore, where I could both think and speak in Kannada. Wonder what this implies in a programming context!