By: Blog by Bogumił Kamiński Re-posted from: https://bkamins.github.io/julialang/2024/04/12/sorting.html Introduction Sorting is one of the most common operations one wants to do with collections. In this post I discuss how one can sort data that contain missing values. The post was written under Julia 1.10.1 and Missings.jl 1.2.0. General rules of comparison with missing values By default missing is considered as greater than any other different value it is compared with: julia> isless(Inf, missing) true julia> isless("abc", missing) true julia> isless(r"abc", missing) true Note ..read more

By: Blog by Bogumił Kamiński Re-posted from: https://bkamins.github.io/julialang/2024/04/05/duplicates.html Introduction Deduplication of rows in a table is one of the basic functionalities that is often needed when working with data frames. Today I discuss the allunique, nonunique, unique, and unique! functions that are provided by DataFrames.jl and can help you with this task. The post was written under Julia 1.10.1 and DataFrames.jl 1.6.1. Checking if a data frame has duplicate rows Let us start with discussing how one can check if a data frame has duplicate rows as this is the simplest che ..read more

By: Julia Frank Re-posted from: https://juliaifrank.com/ml-project-environment-setup-in-julia/ If you opt for running your ML project code locally on your machine, one of the very first things to do is to take care of the ML environment setup. But why and how ..read more

By: Blog by Bogumił Kamiński Re-posted from: https://bkamins.github.io/julialang/2024/03/29/ffd.html Introduction Often when working with data we need to get all possible combinations of some input factors in a data frame. In the field of design of experiments this is called full factorial design. In this post I will discuss two functions that DataFrames.jl provides that can help you to generate such designs if you needed them. The post was written under Julia 1.10.1 and DataFrames.jl 1.6.1. What is a full factorial design and how to create it? Assume we are a cardboard box producer have three ..read more

By: Blog by Bogumił Kamiński Re-posted from: https://bkamins.github.io/julialang/2024/03/22/minicontainers.html Introduction The beauty of DataFrames.jl design is that you can store any data as columns of a data frame. However, this leads to one tricky issue – what if we want to store a vector as a single cell of a data frame? Today I will explain you what is exactly the problem and how to solve it. The post was written under Julia 1.10.1 and DataFrames.jl 1.6.1. Basic transformations of columns in DataFrames.jl Let us start with a simple example: julia> using DataFrames julia> df = D ..read more

By: Steven Whitaker Re-posted from: https://blog.glcs.io/staticarrays The Julia programming languageis known for being a high-level languagethat can still compete with Cin terms of performance.As such,Julia already has performant data structures built-in,such as arrays.But what if arrays could be even faster?That’s where the StaticArrays.jl package comes in. StaticArrays.jl provides drop-in replacements for Array,the standard Julia array type.These StaticArrays work just like Arrays,but they provide one additional piece of informationin the type:the size of the array.Consequently,you can’t ins ..read more

By: Blog by Bogumił Kamiński Re-posted from: https://bkamins.github.io/julialang/2024/03/15/transforms.html Introduction Today I want to comment on a recurring topic that DataFrames.jl users raise. The question is how one should transform multiple columns of a data frame using operation specification syntax. The post was written under Julia 1.10.1 and DataFrames.jl 1.6.1. What is operation specification syntax? In DataFrames.jl the combine, select, and transform functions allow users for passing the requests for data transformation using operation specification syntax. This syntax is feature-r ..read more

By: Dean Markwick's Blog -- Julia Re-posted from: https://dm13450.github.io/2024/03/09/Calibrating-an-Ornstein-Uhlenbeck-Process.html Read enough quant finance papers or books and you’ll come across the Ornstein–Uhlenbeck (OU) process. This is a post that explores the OU process, the equations, how we can simulate such a process and then estimate the parameters. Enjoy these types of posts? Then you should sign up for my newsletter. I’ve briefly touched on mean reversion and OU processes before in my Stat Arb – An Easy Walkthrough blog post where we modelled the spread between an asset and its ..read more

By: Blog by Bogumił Kamiński Re-posted from: https://bkamins.github.io/julialang/2024/03/01/gdf.html Introduction One of the features of DataFrames.jl that I often find useful is that when you group a data frame by some of its columns the resulting GroupedDataFrame is an object that gains new and useful functionalities. Some time ago I have discussed how GroupedDataFrame can be filtered. You can find this post here. In this post and the following one that I plan to write next week I thought that it would be useful to review other key functionalities of a GroupedDataFrame. The post was written ..read more

By: Miguel Raz Guzmán Macedo Re-posted from: https://miguelraz.github.io/blog/gsoc2024/index.html I'm trying to get a GSoC 2024 in LLVM and I will be documenting my work with this ongoing blogpost in reverse chronological order. If you want to see more posts like this, consider chucking a buck or two on my GitHub sponsors, or, you know, hire me as a grad student. 29/02/2024 "hazlo cobarde" Add the 3 way comparison instruction <=> to LLVM. I like this GSoC in particular because I will learn a wide swath of LLVM I'll be working with a lot of optimization passes I'll get to bring cool ..read more