# Description
Closes#7110. ~~Note that unlike "real" `mut` vars, $env can be deeply
mutated via stuff like `$env.PYTHON_IO_ENCODING = utf8` or
`$env.config.history.max_size = 2000`. So, it's a slightly awkward
special case, arguably justifiable because of what $env represents (the
environment variables of your system, which is essentially "outside"
normal Nushell regulations).~~
EDIT: Now allows all `mut` vars to be deeply mutated using `=`, on
request.
# User-Facing Changes
See above.
# Tests + Formatting
Don't forget to add tests that cover your changes.
Make sure you've run and fixed any issues with these commands:
- `cargo fmt --all -- --check` to check standard code formatting (`cargo
fmt --all` applies these changes)
- `cargo clippy --workspace -- -D warnings -D clippy::unwrap_used -A
clippy::needless_collect` to check that you're using the standard code
style
- `cargo test --workspace` to check that all tests pass
# After Submitting
If your PR had any user-facing changes, update [the
documentation](https://github.com/nushell/nushell.github.io) after the
PR is merged, if necessary. This will help us keep the docs up to date.
This adds support for (limited) mutable variables. Mutable variables are created with mut much the same way immutable variables are made with let.
Mutable variables allow mutation via the assignment operator (=).
❯ mut x = 100
❯ $x = 200
❯ print $x
200
Mutable variables are limited in that they're only tended to be used in the local code block. Trying to capture a local variable will result in an error:
❯ mut x = 123; {|| $x }
Error: nu::parser::expected_keyword (link)
× Capture of mutable variable.
The intent of this limitation is to reduce some of the issues with mutable variables in general: namely they make code that's harder to reason about. By reducing the scope that a mutable variable can be used it, we can help create local reasoning about them.
Mutation can occur with fields as well, as in this case:
❯ mut y = {abc: 123}
❯ $y.abc = 456
❯ $y
On a historical note: mutable variables are something that we resisted for quite a long time, leaning as much as we could on the functional style of pipelines and dataflow. That said, we've watched folks struggle to work with reduce as an approximation for patterns that would be trivial to express with local mutation. With that in mind, we're leaning towards the happy path.
