Julia is able to run very well on you Ipython notebook Environment. After all, All you have to do is Data-Science and Machine-Learning. :) 2. DataFrames: Whenever you have to read lot of files in… (more…)

Visualization of Julia profiling data. Contribute to timholy/ProfileView.jl development by creating an account on GitHub. (more…)

Julia has been around since 2012 and after more than six years of development, its 1.0 version has been finally released. This is a major milestone and one that has inspired me to write a new blogpost... (more…)

In Julia, Can I do DF1[B] = DF2[B] where B is a boolean array and DF1 and DF2 are DataFrames? Well, yes! You can by using Julia's multiple-dispatch system by... (more…)

One of the most impressive features of Julia is that it lets you write generic code. Julia’s powerful LLVM-based compiler can automatically generate highly efficient machine code for base functions and user-written functions alike, on any architecture sup... (more…)