如何在 Rust 中读取和处理管道分隔文件？

Question

我想读取竖线分隔文件、处理数据并生成 CSV 格式的结果。

输入文件数据

A|1|Pass
B|2|Fail
A|3|Fail
C|6|Pass
A|8|Pass
B|10|Fail
C|25|Pass
A|12|Fail
C|26|Pass
C|26|Fail

我想在第 1 列和第 3 列上按函数应用分组，并根据特定分组生成第 2 列的总和。

我卡在了如何维护记录以按功能应用组：

use std::fs::File;
use std::io::{BufReader};
use std::io::{BufRead};
use std::collections::HashMap;

fn say_hello(id: &str, value: i32, no_change : i32) {

    if no_change == 101 {
        let mut data = HashMap::new();
    }
    if value == 0 {
        if data.contains_key(id) {
            for (key, value) in &data {
                if value.is_empty() {

                }
            }
        } else {
            data.insert(id,"");
        }
    } else if value == 2 {
        if data.contains_key(id) {
            for (key, value) in &data {
                if value.is_empty() {

                } else {

                }
            }
        } else {
            data.insert(id,"");
        }
    }
}

fn main() {

    let f = File::open("sample2.txt").expect("Unable to open file");
    let br = BufReader::new(f);
    let mut no_change = 101;
    for line in br.lines() {
        let mut index = 0;
        for value in line.unwrap().split('|') {
            say_hello(&value,index,no_change);
            index = index + 1;
        }
    }
}

我期待这样的结果：

name,result,num
A,Fail,15
A,Pass,9
B,Fail,12
C,Fail,26
C,Pass,57

是否有任何特定的技术来读取管道分隔的文件并像上面那样处理数据？ Python 的 pandas 完成了这个要求，但我想在 Rust 中完成。

Answer 1

我建议这样：

use std::str;
use std::collections::HashMap;
use std::io::{BufReader, BufRead, Cursor};

fn main() {
    let data = "
A|1|Pass
B|2|Fail
A|3|Fail
C|6|Pass
A|8|Pass
B|10|Fail
C|25|Pass
A|12|Fail
C|26|Pass
C|26|Fail";
    let lines = BufReader::new(Cursor::new(data))
        .lines()
        .flat_map(Result::ok)
        .flat_map(parse_line);
    for ((fa, fb), s) in group(lines) {
        println!("{}|{}|{}", fa, fb, s);
    }
}

type ParsedLine = ((String, String), usize);

fn parse_line(line: String) -> Option<ParsedLine> {
    let mut fields = line
        .split('|')
        .map(str::trim);
    if let (Some(fa), Some(fb), Some(fc)) = (fields.next(), fields.next(), fields.next()) {
        fb.parse()
            .ok()
            .map(|v| ((fa.to_string(), fc.to_string()), v))
    } else {
        None
    }
}

fn group<I>(input: I) -> Vec<ParsedLine> where I: Iterator<Item = ParsedLine> {
    let mut table = HashMap::new();
    for (k, v) in input {
        let mut sum = table.entry(k).or_insert(0);
        *sum += v;
    }
    let mut output: Vec<_> = table
        .into_iter()
        .collect();
    output.sort_by(|a, b| a.0.cmp(&b.0));
    output
}

playground link

此处HashMap用于对条目进行分组，然后将结果移至Vec进行排序。

Answer 2

如前所述，使用 csv crate to do the heavy lifting of parsing the file. Then it's just a matter of grouping each row by using a BTreeMap which also helpfully performs sorting. The 有助于高效地插入 BTreeMap。

extern crate csv;
extern crate rustc_serialize;

use std::fs::File;
use std::collections::BTreeMap;

#[derive(Debug, RustcDecodable)]
struct Record {
    name: String,
    value: i32,
    passed: String,
}

fn main() {
    let file = File::open("input").expect("Couldn't open input");
    let mut csv_file = csv::Reader::from_reader(file).delimiter(b'|').has_headers(false);

    let mut sums = BTreeMap::new();
    for record in csv_file.decode() {
        let record: Record = record.expect("Could not parse input file");
        let key = (record.name, record.passed);
        *sums.entry(key).or_insert(0) += record.value;
    }

    println!("name,result,num");
    for ((name, passed), sum) in sums {
        println!("{},{},{}", name, passed, sum);
    }
}

您会注意到输出是正确的：

name,result,num
A,Fail,15
A,Pass,9
B,Fail,12
C,Fail,26
C,Pass,57

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