polars: expand polars col to handle multiple columns and by types (#15570)

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# Description
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This PR seeks to expand `polars col` functionality to allow selecting
multiple columns and columns by type, which is particularly useful when
piping to subsequent expressions that should be applied to each column
selected (e.g., `polars col int --type | polars sum` as a shorthand for
`[(polars col a | polars sum), (polars col b | polars sum)]`). See
examples below.

```nushell
#  Select multiple columns (cannot be used with asterisk wildcard)
  > [[a b c]; [x 1 1.1] [y 2 2.2] [z 3 3.3]] | polars into-df 
          | polars select (polars col b c | polars sum) | polars collect
  ╭───┬───┬──────╮
  │ # │ b │  c   │
  ├───┼───┼──────┤
  │ 0 │ 6 │ 6.60 │
  ╰───┴───┴──────╯

#  Select multiple columns by types (cannot be used with asterisk wildcard)
  > [[a b c]; [x o 1.1] [y p 2.2] [z q 3.3]] | polars into-df 
           | polars select (polars col str f64 --type | polars max) | polars collect
  ╭───┬───┬───┬──────╮
  │ # │ a │ b │  c   │
  ├───┼───┼───┼──────┤
  │ 0 │ z │ q │ 3.30 │
  ╰───┴───┴───┴──────╯
```

# User-Facing Changes
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helps us keep track of breaking changes. -->
No breaking changes. Users have the additional capability to select
multiple columns in `polars col`.

# Tests + Formatting
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Examples have been added to `polars col`.

# After Submitting
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This commit is contained in:
pyz4 2025-04-16 17:30:49 -04:00 committed by GitHub
parent d273ce89df
commit 0e9927ea4d
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@ -1,14 +1,14 @@
use crate::{
dataframe::values::NuExpression,
values::{Column, CustomValueSupport, NuDataFrame},
values::{str_to_dtype, Column, CustomValueSupport, NuDataFrame},
PolarsPlugin,
};
use nu_plugin::{EngineInterface, EvaluatedCall, PluginCommand};
use nu_protocol::{
record, Category, Example, LabeledError, PipelineData, Signature, Span, SyntaxShape, Type,
Value,
record, Category, Example, LabeledError, PipelineData, ShellError, Signature, Span,
SyntaxShape, Type, Value,
};
use polars::prelude::col;
use polars::prelude::DataType;
#[derive(Clone)]
pub struct ExprCol;
@ -31,6 +31,12 @@ impl PluginCommand for ExprCol {
SyntaxShape::String,
"Name of column to be used. '*' can be used for all columns.",
)
.rest(
"more columns",
SyntaxShape::String,
"Additional columns to be used. Cannot be '*'",
)
.switch("type", "Treat column names as type names", Some('t'))
.input_output_type(Type::Any, Type::Custom("expression".into()))
.category(Category::Custom("expression".into()))
}
@ -57,6 +63,31 @@ impl PluginCommand for ExprCol {
.into_value(Span::test_data()),
),
},
Example {
description: "Select multiple columns (cannot be used with asterisk wildcard)",
example: "[[a b c]; [x 1 1.1] [y 2 2.2] [z 3 3.3]] | polars into-df | polars select (polars col b c | polars sum) | polars collect",
result: Some(
NuDataFrame::try_from_columns(vec![
Column::new("b".to_string(), vec![Value::test_int(6)]),
Column::new("c".to_string(), vec![Value::test_float(6.6)]),
],None)
.expect("should not fail")
.into_value(Span::test_data()),
),
},
Example {
description: "Select multiple columns by types (cannot be used with asterisk wildcard)",
example: "[[a b c]; [x o 1.1] [y p 2.2] [z q 3.3]] | polars into-df | polars select (polars col str f64 --type | polars max) | polars collect",
result: Some(
NuDataFrame::try_from_columns(vec![
Column::new("a".to_string(), vec![Value::test_string("z")]),
Column::new("b".to_string(), vec![Value::test_string("q")]),
Column::new("c".to_string(), vec![Value::test_float(3.3)]),
],None)
.expect("should not fail")
.into_value(Span::test_data()),
),
},
]
}
@ -71,8 +102,27 @@ impl PluginCommand for ExprCol {
call: &EvaluatedCall,
_input: PipelineData,
) -> Result<PipelineData, LabeledError> {
let name: String = call.req(0)?;
let expr: NuExpression = col(name.as_str()).into();
let mut names: Vec<String> = vec![call.req(0)?];
names.extend(call.rest(1)?);
let as_type = call.has_flag("type")?;
let expr: NuExpression = match as_type {
false => match names.as_slice() {
[single] => polars::prelude::col(single).into(),
_ => polars::prelude::cols(&names).into(),
},
true => {
let dtypes = names
.iter()
.map(|n| str_to_dtype(n, call.head))
.collect::<Result<Vec<DataType>, ShellError>>()
.map_err(LabeledError::from)?;
polars::prelude::dtype_cols(dtypes).into()
}
};
expr.to_pipeline_data(plugin, engine, call.head)
.map_err(LabeledError::from)
}