11–20 of 64 posts
Either way, looking forward to reading about where the optimizations themselves came from.
WRT julia's type system: I still don't get it. Is Julia dynamically or statically typed? Are type errors detected at compile time (or since it's jitted when loading a file) or at runtime (i.e. when the code is executed)? I personally don't see much use in just another dynamically typed language that gives me similar errors as R does.
Julia uses its type system for:
- self-documentation
- reduction of boilerplate manual type checking that litters libraries in dynamic languages
- all those times you need to express the type of something, which happens especially frequently in numerical code
- performance
In the future, some "type linting" could be built into the standard library since we can infer types for so much code, but it isn't the top priority.
5 years ago I thought for sure I would be using Julia today. (I mostly use R) I have found that news tools have come into play that I haven't really felt the need for a faster language then R for my work. I'm interested in people's everyday use of Julia and how it has impacted your workflow. I don't work with "Big Data" most of my data sets are bellow 100k in size. Anyone using Julia for medium and small data sets?
5 years ago I thought for sure I would be using Julia today. (I mostly use R) I have found that news tools have come into play that I haven't really felt the need for a faster language then R for my work. I'm interested in people's everyday use of Julia and how it has impacted your workflow. I don't work with "Big Data" most of my data sets are bellow 100k in size. Anyone using Julia for medium and small data sets?
Fortunately the DataFrames ecosystem and related packages (like Query.jl and StructuredQueries.jl) seem to be headed in the right direction.
5 years ago I thought for sure I would be using Julia today. (I mostly use R) I have found that news tools have come into play that I haven't really felt the need for a faster language then R for my work. I'm interested in people's everyday use of Julia and how it has impacted your workflow. I don't work with "Big Data" most of my data sets are bellow 100k in size. Anyone using Julia for medium and small data sets?
Could you elaborate? Which new tools?
I'm curious to hear from people who are using Julia on a regular basis. What's the UX like? I only looked at it briefly and was turned off by some things (1-based indexing comes to mind). But I really liked other things (multiple dispatch).
1-based for statistics is a STRONG reason to use it. 0 based is ridiculous for a statistics program and the reason why I left Python. I understand for looping and the arras to be internally 0 but when doing statistics I should have to use two different index.
Could you elaborate on that? What about statistics do you feel makes it necessary to have 1-based indexing?
5 years ago I thought for sure I would be using Julia today. (I mostly use R) I have found that news tools have come into play that I haven't really felt the need for a faster language then R for my work. I'm interested in people's everyday use of Julia and how it has impacted your workflow. I don't work with "Big Data" most of my data sets are bellow 100k in size. Anyone using Julia for medium and small data sets?
If you're comfortable using R and the tooling and performance it offers, you should probably keep using it. Julia isn't just about big data, but it does tend to appeal to people who are struggling with problems that are sufficiently hard that their old tools left them in a world of pain. That pain may comes from size, complexity, CPU-intensiveness, or need for more language expressiveness (a hard thing to define). In particular, Julia offers a unique combination of productivity and speed for numerical work that can't be found anywhere else.
Exciting to hear -- I have used Julia for prototyping, and have found it to be excellent for that: In my experience there are still some rough edges as compared to the Python ecosystem (of course!), which together with the 0.x status make it impractical for many production situations. However it is fantastic for prototyping numerical code, the type system is a pleasure, and the JuMP mathematical optimization library…
Really interested in why you would use a "faster" language in julia then move to a "slower" language like Python or R?