使用 papaparse 遍历特定数据后如何解析结果?
How to unparse result after iterating through specific data with papaparse?
我正在尝试使用 PapaParse 解析本地 CSV 文件并遍历 JSON 以便仅从文件输出特定数据。
示例:我只希望第 "Mill Description" 列匹配 adidas && Champion,所有其他数据都将被丢弃。
如果您需要更多说明,请随时询问,我会定期查看。
CSV 文件:https://ufile.io/ze7xl
papa.parse(file, {
worker: true,
header: true,
transformHeader: true,
step: function(result) {
var data = result.data;
for (var i = 0; i < data.length; i++) {
var millDescription = JSON.stringify(data[i]["Mill Description"]);
if (
millDescription.includes("adidas") ||
millDescription.includes("Champion")
) {
// This is where I need help
}
}
},
complete: function(result, csv) {
console.log("parsing complete read: ", result, csv); // Nothing is passed to here yet.
}
});
试试这个
papa.parse(file, {
worker: true,
header: true,
transformHeader: true,
step: function(result) {
let data = result.data;
data = data.filter(d => d["Mill Description"].includes('adidas') || d["Mill Description"].includes('adidaChampions'));
},
complete: function(result, csv) {
console.log("parsing complete read: ", result, csv); // Nothing is passed to here yet.
}
});
输出看起来像这样,多个 JSON 数组,然后我的问题是如何解析所有数据以转换回 csv 文件?
[{
"Item Number": "B12704533",
"GTIN Number": "190311332942",
"Mill Code": "04",
"Mill Description": "adidas Golf",
"Style Number": "A262",
"Mill Style Number": "TWA262S8",
"Style Name": "AD LADS MICRO STRIPE POLO",
"Color Code": "53",
"Color Description": "ROYAL",
"Size Code": "3",
"Size Description": "S",
"Unit Weight": ".7258",
"Cost": "0",
"CC": "0",
"CD": "0",
"FO": "0",
"KC": "0",
"MA": "0",
"PH": "0",
"TD": "0",
"CN": "0",
"WA": "0",
"GD": "316",
"Total Inventory": "316"
}]
[{
"Item Number": "B12704534",
"GTIN Number": "190311332966",
"Mill Code": "04",
"Mill Description": "adidas Golf",
"Style Number": "A262",
"Mill Style Number": "TWA262S8",
"Style Name": "AD LADS MICRO STRIPE POLO",
"Color Code": "53",
"Color Description": "ROYAL",
"Size Code": "4",
"Size Description": "M",
"Unit Weight": ".7717",
"Cost": "0",
"CC": "0",
"CD": "0",
"FO": "0",
"KC": "0",
"MA": "0",
"PH": "0",
"TD": "1",
"CN": "0",
"WA": "0",
"GD": "540",
"Total Inventory": "541"
}]
我正在尝试使用 PapaParse 解析本地 CSV 文件并遍历 JSON 以便仅从文件输出特定数据。
示例:我只希望第 "Mill Description" 列匹配 adidas && Champion,所有其他数据都将被丢弃。
如果您需要更多说明,请随时询问,我会定期查看。
CSV 文件:https://ufile.io/ze7xl
papa.parse(file, {
worker: true,
header: true,
transformHeader: true,
step: function(result) {
var data = result.data;
for (var i = 0; i < data.length; i++) {
var millDescription = JSON.stringify(data[i]["Mill Description"]);
if (
millDescription.includes("adidas") ||
millDescription.includes("Champion")
) {
// This is where I need help
}
}
},
complete: function(result, csv) {
console.log("parsing complete read: ", result, csv); // Nothing is passed to here yet.
}
});
试试这个
papa.parse(file, {
worker: true,
header: true,
transformHeader: true,
step: function(result) {
let data = result.data;
data = data.filter(d => d["Mill Description"].includes('adidas') || d["Mill Description"].includes('adidaChampions'));
},
complete: function(result, csv) {
console.log("parsing complete read: ", result, csv); // Nothing is passed to here yet.
}
});
输出看起来像这样,多个 JSON 数组,然后我的问题是如何解析所有数据以转换回 csv 文件?
[{
"Item Number": "B12704533",
"GTIN Number": "190311332942",
"Mill Code": "04",
"Mill Description": "adidas Golf",
"Style Number": "A262",
"Mill Style Number": "TWA262S8",
"Style Name": "AD LADS MICRO STRIPE POLO",
"Color Code": "53",
"Color Description": "ROYAL",
"Size Code": "3",
"Size Description": "S",
"Unit Weight": ".7258",
"Cost": "0",
"CC": "0",
"CD": "0",
"FO": "0",
"KC": "0",
"MA": "0",
"PH": "0",
"TD": "0",
"CN": "0",
"WA": "0",
"GD": "316",
"Total Inventory": "316"
}]
[{
"Item Number": "B12704534",
"GTIN Number": "190311332966",
"Mill Code": "04",
"Mill Description": "adidas Golf",
"Style Number": "A262",
"Mill Style Number": "TWA262S8",
"Style Name": "AD LADS MICRO STRIPE POLO",
"Color Code": "53",
"Color Description": "ROYAL",
"Size Code": "4",
"Size Description": "M",
"Unit Weight": ".7717",
"Cost": "0",
"CC": "0",
"CD": "0",
"FO": "0",
"KC": "0",
"MA": "0",
"PH": "0",
"TD": "1",
"CN": "0",
"WA": "0",
"GD": "540",
"Total Inventory": "541"
}]