Question

我需要一些帮助，我必须按区域和年份的元素过滤arr对象，我尝试使用filter方法，但是由于事先的原因，我不知道该如何实现。

let arr = [
        { id: 1, region: ["china"], year: ["1990"] },
        { id: 2, region: ["china"], year: ["2010"] },
        { id: 3, region: ["taiwan"], year: ["1990"] },
        { id: 4, region: ["corea"], year: ["1990"] },
        { id: 5, region: ["corea"], year: ["2010"] },
      ];

let regions = ["china", "taiwan"];
let years = ["1990"];

let expectedOutput = [{ id: 1, region: ["china"], year: ["1990"] },
                  { id: 2, region: ["china"], year: ["2010"] },
                  { id: 3, region: ["taiwan"], year: ["1990"] },
                  { id: 4, region: ["corea"], year: ["1990"] }];

Answer 1

过滤

let arr = [
        { id: 1, region: ["china"], year: ["1990"] },
        { id: 2, region: ["china"], year: ["2010"] },
        { id: 3, region: ["taiwan"], year: ["1990"] },
        { id: 4, region: ["corea"], year: ["1990"] },
        { id: 5, region: ["corea"], year: ["2010"] },
      ];

let regions = ["china", "taiwan"];
let years = ["1990"];

let result = arr.filter(({region, year}) => region.some(el => regions.includes(el)) || year.some(year => years.includes(year)));

console.log(result);

Answer 2

您可以采用对象键的条目作为具有所需键和可能值的过滤器，而不是采用硬连线的方法。

import pandas as pd
import numpy as np   

from sklearn.naive_bayes import MultinomialNB
from sklearn.multioutput import MultiOutputClassifier

df_train = pd.DataFrame({'feature': [1, 2, 3, 3, 4, 4, 5]})

df_train_labels = pd.DataFrame({'class1': ['1-3', '1-3','1-3', '1-3', '4-5', '4-5', '4-5'], 'class2': ['1-2', '1-2', '3-5', '3-5', '3-5', '3-5', '3-5']})

prediction_model = MultiOutputClassifier(MultinomialNB())

prediction_model.fit(df_train, df_train_labels) # Works fine

prediction_model.partial_fit(df_train,\
                                 df_train_labels,\
                                 np.unique(df_train_labels))  # Error

Answer 3

请尝试

let arr = [
    { id: 1, region: ["china"], year: ["1990"] },
    { id: 2, region: ["china"], year: ["2010"] },
    { id: 3, region: ["taiwan"], year: ["1990"] },
    { id: 4, region: ["corea"], year: ["1990"] },
    { id: 5, region: ["corea"], year: ["2010"] }
  ];

let regions = ["china", "taiwan"];
let years = ["1990"];


let result = arr.filter((row) => {
    if (regions.includes(row.region[0]) ) {
        return row;
    } else if (years.includes(row.year[0])) {
        return row;
    }

});
console.log(result.length);
for (row of result) {
    console.log(row);
}

输出：

4
{ id: 1, region: [ 'china' ], year: [ '1990' ] }
{ id: 2, region: [ 'china' ], year: [ '2010' ] }
{ id: 3, region: [ 'taiwan' ], year: [ '1990' ] }
{ id: 4, region: [ 'corea' ], year: [ '1990' ] }

Answer 4

let arr = [
        { id: 1, region: ["china"], year: ["1990"] },
        { id: 2, region: ["china"], year: ["2010"] },
        { id: 3, region: ["taiwan"], year: ["1990"] },
        { id: 4, region: ["corea"], year: ["1990"] },
        { id: 5, region: ["corea"], year: ["2010"] },
      ];

let regions = ["china", "taiwan"];
let years = ["1990"];  

arr=arr.filter(obj => regions.includes(obj.region[0]) || years.includes(obj.year[0]));

console.log(arr);

对象数组，与其他数组比较并过滤

4 个答案: