宏通过trait `std::fmt::Display`确定输出的具体格式。自定义的struct因为没有实现Display,输出格式不确定,所以会报错。 方法1:添加attribute#[derive(Debug)],并用{:?}约束输出格式 #[derive(Debug)] struct Coordinate { x: u32, y: u32, } fn main() { let coor = Coordinate { x: 1, y: 5, ...
I would like to be able to define aimplforfmt::Displaywithout having to enumerate the fields again so there is no chance for missing one if a new one is added. For the struct: leteg= Example{ a:Some("test".to_string), b:Some(123), c:None, } I would like the output format: ...
usestd::fmt;//#[derive(Debug)]structStudent{name:String,year:u8,}implStudent{fnnew(a:&str,b:u8)->Self{Self{name:a.to_string(),year:b,}}}implfmt::DebugforStudent{fnfmt(&self,f:&mutfmt::Formatter)->fmt::Result{write!(f,"Student {{ x: {}, y: {} }}",self.name,self.year...
在Rust中，结构体（Struct）是一种自定义数据类型，它允许我们将多个相关的值组合在一起，形成一个更...
because you want each element to be displayed using its Display trait, not its Debug trait; however, as noted, you can't implement Display on Vec because of Rust's coherence rules. Instead of implementing a wrapper struct with the Display trait, you can implement a more general solution wit...
repeat(len + 4)); } } struct Point { x: i32, y: i32, } impl OutlinePrint for Point {} impl fmt::Display for Point { fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result { write!(f, "({}, {})", self.x, self.y) } } fn main() { let p = Point{x:1, y:3}...
Polars 是一个用于操作结构化数据的高性能 DataFrame 库,可以说是平替 pandas 最有潜质的包。Polars 其核心部分是用 Rust 编写的,但该库也提供了 Python 接口。它的主要特点包括: 快速: Polars 是从零开始编写的,紧密与机器结合,没有外部依赖。 I/O: 对所有常见数据存储层提供一流支持:本地、云存储和数据库...
use tabled::{Tabled, Table}; #[derive(Tabled)] struct Language { name: &'static str, designed_by: &'static str, invented_year: usize, } let languages = vec![ Language{ name: "C", designed_by: "Dennis Ritchie", invented_year: 1972 }, Language{ name: "Go", designed_by: "Rob...
pub struct PrintImpl<'a, ExtField = Fe1024> { name: &'a str, generator: &'a [Fe32], target: &'a [Fe32], bit_len: usize, hex_width: usize, midstate_repr: &'static str, phantom: PhantomData<ExtField>, } #[cfg(feature = "alloc")] impl<'a, ExtField> PrintImpl<'a, Ext...
Polars是一个用于操作结构化数据的高性能DataFrame库,可以说是平替pandas最有潜质的包。Polars其核心部分是用Rust编写的,但该库也提供了Python接口。它的主要特点包括: 快速: Polars是从零开始编写的,紧密与机器结合,没有外部依赖。 I/O: 对所有常见数据存储层提供一流支持:本地、云存储和数据库。