It is possible to get an interface that is similar to the IPython REPL and the Mathematica notebook with numbered input prompts and output prefixes.
Check this out (Julia v1.9+ required): https://docs.julialang.org/en/v1/stdlib/REPL/#Numbered-prompt

#Tips__JG
❤️ JuliaGeeks ❤️

‍ One of the most important new methods that have been added into Julia through the latest update is in! which pushes a new element to an existing Set, if it isn't already in the Set:

julia> S = Set([1,2,3,4]);

julia> S
Set{Int64} with 4 elements:
  4
  2
  3
  1

julia> in!(5, S)
false

julia> S
Set{Int64} with 5 elements:
  5
  4
  2
  3
  1

⚠️ Note that it returned false since the 5 didn't exist in the Set.
1️⃣ Don't know about the exclamation mark? Check this.
2️⃣ Don't know about Sets in Julia? Check this.

#Tips__JG |#Function__JG
❤️ JuliaGeeks

Wow, new functions such as allunique, and allequal that came with Julia 1.11, are super handy:

allunique(itr) [doc]
Return true if all values from itr are distinct compared with isequal. Or if all of [f(x) for x itr] are distinct, for the second method.

  julia> allunique([1, 2, 3])
  true

  julia> allunique([1, 2, 1, 2])
  false

allequal(itr) [doc]
Return true if all values from itr are equal when compared with isequal. Or if all of [f(x) for in itr] are equal, for the second method.

  julia> allequal([1, 1])
  true

  julia> allequal([1, 2])

#Tips__JG |#Function__JG
❤️ JuliaGeeks

Julia 1.11.1 documentation is available now.

#Documentation__JG
❤️ JuliaGeeks

😁 Introduction of Memory type

StaticArrays play an essential role in mathematical operations and the optimization of such operations. However, Julia developers have decided to use a built-in type in spite of using a third-party package in this regard.

So far, they have written dozens of lines of code to provide the optimizations as a built-in type of Memory. It is such a relief to announce that this type can be vastly replaced by Vector{T}. Also, many inner specifications of arrays have been implemented based on Memory type. Hence, performing Array operations is now more efficient and blazingly faster in Julia +1.11. ⏩

Later on, I will write more details about this.

#Update__JG
❤️ JuliaGeeks

Suppose you want to test a package just once without getting involved with the installation and uninstallation process. What would you do?
Well, Julia devs have provided a handy flag, --temp, to activate a temporary environment for such cases. The temporarily-created env would be deleted automatically right after deactivating it. 🤟

To activate such an env, one should enter the following command after entering into the pkg section by hitting the ] key ⌨️:

pkg> activate --temp

🔗 More information in this regard is available on the official documentation.
❓ Do you know about shared environments in Julia?

#Tips__JG |#Environment__JG
❤️ JuliaGeeks

Sometimes, you must see all elements within a Vector or something else. The Julia REPL limits the number of elements that can be shown and truncates the output to better fit the screen. One way to do this is to use the following code to avoid the truncation:

show(stdout, "text/plain", rand(30))

The output of this code is exactly the same as when you call the rand(30) code, vertically adjusted but without truncation. However, if you would like to see the elements next to each other separated using a comma (like what we have in Python 📱), you should use the following code:

show(IOContext(stdout, :limit=>false), rand(30))

The output would be something like [0.8223948799776574, 0.01535264435200867, 0.7524808776993049, 0.8567173366672614, 0.19442069230608283, 0.5655759545291479, 0.11702055746225415] (here I truncated the output to avoid a lengthy message)

#Tips__JG
❤️ JuliaGeeks

HELL YEAAAA 🎉
Julia 1.10.8 currently is deployed internally by Google fucking Colab 😎
Let's go baby 🥳

#News__JG
❤️ JuliaGeeks

Julia 1.12 allows redefining structs 💃

#News@JuliaGeeks
❤️ JuliaGeeks