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Julia Noteworthy differences from MATLAB
Although MATLAB users may find Julia's syntax familiar, Julia is not a MATLAB clone. There are major syntactic and functional differences. The following are some noteworthy differences that may trip up Julia users accustomed to MATLAB:
will modify `A` as well. To avoid this, use `A = copy(B)`.
corresponding Julia statement `a[5] = 7` throws an error if the length of `a` is less than 5 or if this statement is the first use of the identifier `a`. Julia has [`push!`](@ref) and [`append!`](@ref), which grow `Vector`s much more efficiently than MATLAB's `a(end+1) = val`.
point numbers. As a result, some operations can throw a domain error if they expect a float; for example, `julia> a = -1; 2^a` throws a domain error, as the result is not an integer (see [the FAQ entry on domain errors](@ref faq-domain-errors) for details).
MATLAB's `nargout`, which is often used in MATLAB to do optional work based on the number of returned values, does not exist in Julia. Instead, users can use optional and keyword arguments to achieve similar capabilities.
[`rand(N)`](@ref) makes a 1-dimensional array.
with semicolons (`[x; y; z]`).
with spaces (`[x y z]`).
or combine spaces and semicolons (`[a b; c d]`).
use [`collect(a:b)`](@ref). Generally, there is no need to call `collect` though. An `AbstractRange` object will act like a normal array in most cases but is more efficient because it lazily computes its values. This pattern of creating specialized objects instead of full arrays is used frequently, and is also seen in functions such as [`range`](@ref), or with iterators such as `enumerate`, and `zip`. The special objects can mostly be used as if they were normal arrays.
listing the names of variables to return in the function definition (see [The return Keyword](@ref) for details).
when the file is loaded. Function definitions can be loaded from files outside the current working directory.
over every element of an array when called with a single argument, as in `sum(A)`, even if `A` has more than one dimension.
automatically printed (except at the interactive prompt), and lines of code do not need to end with semicolons. [`println`](@ref) or [`@printf`](@ref) can be used to print specific output.
an array of booleans. Instead, use `A .== B`, and similarly for the other boolean operators like [`<`](@ref), [`>`](@ref).
R where the vectors only need to share a common index range. For example, `c(1, 2, 3, 4) + c(1, 2)` is valid R but the equivalent `[1, 2, 3, 4] + [1, 2]` will throw an error in Julia.
This can cause confusion among R users when indexing into arrays. For example, `x[1,]` in R would return the first row of a matrix; in Julia, however, the comma is ignored, so `x[1,] == x[1]`, and will return the first element. To extract a row, be sure to use `:`, as in `x[1,:]`.
in R. Similarly Julia's equivalent of `apply(X, MARGIN, FUN, ...)` in R is [`mapslices`](@ref) where the function is the first argument.
in Julia. Equivalently Julia offers a shorter dot syntax for vectorizing functions `binomial.(11:13, 1:3)`.
`for`, and functions. In lieu of the one-line `if ( cond ) statement`, Julia allows statements of the form `if cond; statement; end`, `cond && statement` and `!cond ]] | [[]] | [[ statement`. Assignment statements in the latter two syntaxes must be explicitly wrapped in parentheses, e.g. `cond && (x = value)`.
matrices, then `A * B` denotes a matrix multiplication in Julia, equivalent to R's `A %*% B`. In R, this same notation would perform an element-wise (Hadamard) product. To get the element-wise multiplication operation, you need to write `A .* B` in Julia.
the `'` operator or the `adjoint` function. Julia's `transpose(A)` is therefore equivalent to R's `t(A)`. Additionally a non-recursive transpose in Julia is provided by the `permutedims` function.
instead of `for (i in c(1, 2, 3))` and `if i == 1` instead of `if (i == 1)`.
because `if` statements accept only booleans. Instead, you can write `if true`, `if Bool(1)`, or `if 1==1`.
for `ncol(M)`.
In Julia, they cannot be used interchangeably.
you cannot write `diag(M) = fill(1, n)`.
for Julia is found in [packages](https://pkg.julialang.org/) under the [JuliaStats organization](https://github.com/JuliaStats). For example:
you should typically use a tuple or a named tuple: instead of `list(a = 1, b = 2)`, use `(1, 2)` or `(a=1, b=2)`.
in R. Julia's multiple dispatch system means that `table(x::TypeA)` and `table(x::TypeB)` act like R's `table.TypeA(x)` and `table.TypeB(x)`.
will be visible in the caller. This is very different from R and allows new functions to operate on large data structures much more efficiently.
[`hvcat`](@ref), not `c`, `rbind` and `cbind` like in R.
object that is used for iteration. To convert a range into a vector, use [`collect(a:b)`](@ref).
have higher precedence than the `:` operator, whereas the reverse is true in R. For example, `1:n-1` in Julia is equivalent to `1:(n-1)` in R.
in R, but both arguments need to have the same dimensions. While [`maximum`](@ref) and [`minimum`](@ref) replace `max` and `min` in R, there are important differences.
from their counterparts in R. They all accept an optional keyword argument `dims`, which indicates the dimensions, over which the operation is carried out. For instance, let `A = [1 2; 3 4]` in Julia and `B <- rbind(c(1,2),c(3,4))` be the same matrix in R. Then `sum(A)` gives the same result as `sum(B)`, but `sum(A, dims=1)` is a row vector containing the sum over each column and `sum(A, dims=2)` is a column vector containing the sum over each row. This contrasts to the behavior of R, where separate `colSums(B)` and `rowSums(B)` functions provide these functionalities. If the `dims` keyword argument is a vector, then it specifies all the dimensions over which the sum is performed, while retaining the dimensions of the summed array, e.g. `sum(A, dims=(1,2)) == hcat(10)`. It should be noted that there is no error checking regarding the second argument.
and [`sort!`](@ref).
code is often achieved by using devectorized loops.
means that there are very few unquoted expressions or column names.
behaves like a scalar value rather than like a list. Use `x === nothing` instead of `is.null(x)`.
Use [`ismissing(x)`](@ref) (or `ismissing.(x)` for element-wise operation on vectors) instead of `is.na(x)`. The [`skipmissing`](@ref) function is generally used instead of `na.rm=TRUE` (though in some particular cases functions take a `skipmissing` argument).
`x[x>3]` or in the statement `x = x[x>3]` to modify `x` in-place. In contrast, Julia provides the higher order functions [`filter`](@ref) and [`filter!`](@ref), allowing users to write `filter(z->z>3, x)` and `filter!(z->z>3, x)` as alternatives to the corresponding transliterations `x[x.>3]` and `x = x[x.>3]`. Using [`filter!`](@ref) reduces the use of temporary arrays.
Julia Noteworthy differences from Python
is not significant as it is in Python. Unlike Python, Julia has no `pass` keyword.
in Python.
Python's special interpretation of negative indexing, `a[-1]` and `a[-2]`, should be written `a[end]` and `a[end-1]` in Julia.
in array-indexing. The `range` function is also supported.
expression, it is considered done; otherwise the input continues. One way to force an expression to continue is to wrap it in parentheses.
by default. To get optimal performance when looping over arrays, the order of the loops should be reversed in Julia relative to NumPy (see [relevant section of Performance Tips](@ref man-performance-column-major)).
means `A = [1, 1]; B = A; B += [3, 3]` doesn't change values in `A`, it rather rebinds the name `B` to the result of the right-hand side `B = B + 3`, which is a new array. For in-place operation, use `B .+= 3` (see also [dot operators](@ref man-dot-operators)), explicit loops, or `InplaceOps.jl`.
in Python where the default values are evaluated only once when the function is defined. For example, the function `f(x=rand()) = x` returns a new random number every time it is invoked without argument. On the other hand, the function `g(x=[1,2]) = push!(x,3)` returns `[1,2,3]` every time it is called as `g()`.
to pass them positionally. Attempting to pass a keyword argument positionally alters the method signature leading to a `MethodError` or calling of the wrong method.
This means in Julia the `Int` type will overflow, such that `2^64 == 0`. If you need larger values use another appropriate type, such as `Int128`, [`BigInt`](@ref) or a floating point type like `Float64`.
This syntax is not just syntactic sugar for a reference to a pointer or address as in C/C++. See [the manual entry about array construction](@ref man-multi-dim-arrays).
as well. Updating operators like `+=` do not operate in-place, they are equivalent to `A = A + B` which rebinds the left-hand side to the result of the right-hand side expression.
default. To get optimal performance when looping over arrays, the order of the loops should be reversed in Julia relative to C/C++ (see [relevant section of Performance Tips](@ref man-performance-column-major)).
will be visible in the caller.
whitespace from a Julia program.
`Int`, but literals too large to fit in the machine word size will automatically be promoted to
a larger size type, such as `Int64` (if `Int` is `Int32`), `Int128`, or the arbitrarily large
`BigInt` type. There are no numeric literal suffixes, such as `L`, `LL`, `U`, `UL`, `ULL` to indicate
unsigned and/or signed vs. unsigned. Decimal literals are always signed, and hexadecimal literals
(which start with `0x` like C/C++), are unsigned, unless when they encode more than 128 bits,
in which case they are of type `BigInt`. Hexadecimal literals also, unlike C/C++/Java
and unlike decimal literals in Julia, have a type based on the *length* of the literal, including
leading 0s. For example, `0x0` and `0x00` have type [`UInt8`](@ref), `0x000` and `0x0000` have type
[`UInt16`](@ref), then literals with 5 to 8 hex digits have type `UInt32`, 9 to 16 hex digits type
`UInt64`, 17 to 32 hex digits type `UInt128`, and more that 32 hex digits type `BigInt`.
This needs to be taken into account when defining
hexadecimal masks, for example `~0xf == 0xf0` is very different from `~0x000f == 0xfff0`. 64 bit `Float64`
and 32 bit [`Float32`](@ref) bit literals are expressed as `1.0` and `1.0f0` respectively. Floating point
literals are rounded (and not promoted to the `BigFloat` type) if they can not be exactly represented.
Floating point literals are closer in behavior to C/C++. Octal (prefixed with `0o`) and binary
(prefixed with `0b`) literals are also treated as unsigned (or `BigInt` for more than 128 bits).are of integer type. To perform integer division, use [`div`](@ref) or [`÷`](@ref div).
equivalent of the C expression `a[i / 2]` is `a[i ÷ 2 + 1]`, where `i` is of integer type.
`"` characters without quoting it like `"\""`. String literals can have values of other variables or expressions interpolated into them, indicated by `$variablename` or `$(expression)`, which evaluates the variable name or the expression in the context of the function.
values can be returned from functions and assigned as tuples, e.g. `(a, b) = myfunction()` or `a, b = myfunction()`, instead of having to pass pointers to values as one would have to do in C/C++ (i.e. `a = myfunction(&b)`.
not automatically printed (except at the interactive prompt, i.e. the REPL), and lines of code do not need to end with semicolons. [`println`](@ref) or [`@printf`](@ref) can be used to print specific output. In the REPL, `;` can be used to suppress output. `;` also has a different meaning within `[ ]`, something to watch out for. `;` can be used to separate expressions on a single line, but are not strictly necessary in many cases, and are more an aid to readability.
[`^`](@ref) in C/C++. Also, the bitwise operators do not have the same precedence as C/C++, so parenthesis may be required.
[`xor`](@ref), in Julia)
always performs a logical shift, unlike C/C++, where the meaning of `>>` depends on the type of the value being shifted.
instead of `for (int i=1; i <= 3; i++)` and `if i == 1` instead of `if (i == 1)`.
because `if` statements accept only booleans. Instead, you can write `if true`, `if Bool(1)`, or `if 1==1`.
`for`, and functions. In lieu of the one-line `if ( cond ) statement`, Julia allows statements of the form `if cond; statement; end`, `cond && statement` and `!cond ]] | [[]] | [[ statement`. Assignment statements in the latter two syntaxes must be explicitly wrapped in parentheses, e.g. `cond && (x = value)`, because of the operator precedence.
expression, it is considered done; otherwise the input continues. One way to force an expression to continue is to wrap it in parentheses.
them to perform sophisticated transformations of Julia code. Macro names start with the `@` character, and have both a function-like syntax, `@mymacro(arg1, arg2, arg3)`, and a statement-like syntax, `@mymacro arg1 arg2 arg3`. The forms are interchangeable; the function-like form is particularly useful if the macro appears within another expression, and is often clearest. The statement-like form is often used to annotate blocks, as in the distributed `for` construct: `@distributed for i in 1:n; #= body =#; end`. Where the end of the macro construct may be unclear, use the function-like form.
For example: `@enum(Fruit, banana=1, apple, pear)`
`push!`.
in order to have dynamic dispatch. On the other hand, in Julia every method is "virtual" (although it's more general than that since methods are dispatched on every argument type, not only `this`, using the most-specific-declaration rule).
* Integer division using `/` always returns a floating-point result, even if the computation is exact.
* Bignums are supported, but conversion is not automatic; ordinary integers [overflow](@ref faq-integer-arithmetic). * Complex numbers are supported, but to get complex results, [you need complex inputs](@ref faq-domain-errors). * There are multiple Complex and Rational types, with different component types.
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