JT 9068093081
Improve type hovers (#9515)
# Description

This PR does a few things to help improve type hovers and, in the
process, fixes a few outstanding issues in the type system. Here's a
list of the changes:

* `for` now will try to infer the type of the iteration variable based
on the expression it's given. This fixes things like `for x in [1, 2, 3]
{ }` where `x` now properly gets the int type.
* Removed old input/output type fields from the signature, focuses on
the vec of signatures. Updated a bunch of dataframe commands that hadn't
moved over. This helps tie things together a bit better
* Fixed inference of types from subexpressions to use the last
expression in the block
* Fixed handling of explicit types in `let` and `mut` calls, so we now
respect that as the authoritative type

I also tried to add `def` input/output type inference, but unfortunately
we only know the predecl types universally, which means we won't have
enough information to properly know what the types of the custom
commands are.

# User-Facing Changes

Script typechecking will get tighter in some cases
Hovers should be more accurate in some cases that previously resorted to
any.

# Tests + Formatting
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---------

Co-authored-by: Darren Schroeder <343840+fdncred@users.noreply.github.com>
2023-06-29 05:19:48 +12:00

130 lines
4.5 KiB
Rust

use super::super::values::{Column, NuDataFrame};
use nu_protocol::{
ast::Call,
engine::{Command, EngineState, Stack},
Category, Example, PipelineData, ShellError, Signature, Span, Type, Value,
};
#[derive(Clone)]
pub struct ToDataFrame;
impl Command for ToDataFrame {
fn name(&self) -> &str {
"dfr into-df"
}
fn usage(&self) -> &str {
"Converts a list, table or record into a dataframe."
}
fn signature(&self) -> Signature {
Signature::build(self.name())
.input_output_type(Type::Any, Type::Custom("dataframe".into()))
.category(Category::Custom("dataframe".into()))
}
fn examples(&self) -> Vec<Example> {
vec![
Example {
description: "Takes a dictionary and creates a dataframe",
example: "[[a b];[1 2] [3 4]] | dfr into-df",
result: Some(
NuDataFrame::try_from_columns(vec![
Column::new(
"a".to_string(),
vec![Value::test_int(1), Value::test_int(3)],
),
Column::new(
"b".to_string(),
vec![Value::test_int(2), Value::test_int(4)],
),
])
.expect("simple df for test should not fail")
.into_value(Span::test_data()),
),
},
Example {
description: "Takes a list of tables and creates a dataframe",
example: "[[1 2 a] [3 4 b] [5 6 c]] | dfr into-df",
result: Some(
NuDataFrame::try_from_columns(vec![
Column::new(
"0".to_string(),
vec![Value::test_int(1), Value::test_int(3), Value::test_int(5)],
),
Column::new(
"1".to_string(),
vec![Value::test_int(2), Value::test_int(4), Value::test_int(6)],
),
Column::new(
"2".to_string(),
vec![
Value::test_string("a"),
Value::test_string("b"),
Value::test_string("c"),
],
),
])
.expect("simple df for test should not fail")
.into_value(Span::test_data()),
),
},
Example {
description: "Takes a list and creates a dataframe",
example: "[a b c] | dfr into-df",
result: Some(
NuDataFrame::try_from_columns(vec![Column::new(
"0".to_string(),
vec![
Value::test_string("a"),
Value::test_string("b"),
Value::test_string("c"),
],
)])
.expect("simple df for test should not fail")
.into_value(Span::test_data()),
),
},
Example {
description: "Takes a list of booleans and creates a dataframe",
example: "[true true false] | dfr into-df",
result: Some(
NuDataFrame::try_from_columns(vec![Column::new(
"0".to_string(),
vec![
Value::test_bool(true),
Value::test_bool(true),
Value::test_bool(false),
],
)])
.expect("simple df for test should not fail")
.into_value(Span::test_data()),
),
},
]
}
fn run(
&self,
_engine_state: &EngineState,
_stack: &mut Stack,
call: &Call,
input: PipelineData,
) -> Result<PipelineData, ShellError> {
NuDataFrame::try_from_iter(input.into_iter())
.map(|df| PipelineData::Value(NuDataFrame::into_value(df, call.head), None))
}
}
#[cfg(test)]
mod test {
use super::super::super::test_dataframe::test_dataframe;
use super::*;
#[test]
fn test_examples() {
test_dataframe(vec![Box::new(ToDataFrame {})])
}
}