本文整理汇总了TypeScript中xlsx.utils.sheet_to_csv方法的典型用法代码示例。如果您正苦于以下问题:TypeScript utils.sheet_to_csv方法的具体用法?TypeScript utils.sheet_to_csv怎么用?TypeScript utils.sheet_to_csv使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类xlsx.utils
的用法示例。
在下文中一共展示了utils.sheet_to_csv方法的3个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的TypeScript代码示例。
示例1:
import * as XLSX from 'xlsx';
console.log(XLSX.version);
const bookType: string = "xlsb";
const fn: string = "sheetjsfbox." + bookType
const sn: string = "SheetJSFBox";
const aoa: any[][] = [ ["Sheet", "JS"], ["Fuse", "Box"], [72, 62] ];
var wb: XLSX.WorkBook = XLSX.utils.book_new();
var ws: XLSX.WorkSheet = XLSX.utils.aoa_to_sheet(aoa);
XLSX.utils.book_append_sheet(wb, ws, sn);
var payload: string = "";
var w2: XLSX.WorkBook;
if(typeof process != 'undefined' && process.versions && process.versions.node) {
/* server */
XLSX.writeFile(wb, fn);
w2 = XLSX.readFile(fn)
} else {
/* client */
payload = XLSX.write(wb, {bookType: "xlsb", type:"binary"});
w2 = XLSX.read(payload, {type:"binary"});
}
var s2: XLSX.WorkSheet = w2.Sheets[sn];
console.log(XLSX.utils.sheet_to_csv(s2));
示例2:
], {header: ["A", "B", "C", "D", "E", "F", "G"], skipHeader: true, origin: -1});
const tbl = {}; /* document.getElementById('table'); */
const ws3 = XLSX.utils.table_to_sheet(tbl, {
raw: true,
cellDates: true,
dateNF: "yyyy-mm-dd",
sheetRows: 1
});
const obj1 = XLSX.utils.sheet_to_formulae(ws1);
const str1: string = XLSX.utils.sheet_to_csv(ws2, {
FS: "\t",
RS: "|",
dateNF: "yyyy-mm-dd",
strip: true,
blankrows: true,
skipHidden: true
});
const html1: string = XLSX.utils.sheet_to_html(ws3, {
editable: false
});
const arr1: object[] = XLSX.utils.sheet_to_json(ws1, {
raw: true,
range: 1,
header: "A",
dateNF: "yyyy-mm-dd",
defval: 0,
blankrows: true
示例3: importCodebook
async function importCodebook() {
const codebookXLS = XLSX.readFile(CODEBOOK_FILE)
const sheet = codebookXLS.Sheets[codebookXLS.SheetNames[0]]
const codebookCSV = XLSX.utils.sheet_to_csv(sheet)
const now = new Date()
const codebookRows = await parseCSV(codebookCSV)
const vdemVariables = codebookRows.slice(1).map(row => ({
indicatorCode: row[0],
indicatorName: row[1],
shortDefinition: row[2],
longDefinition: row[3],
responses: row[4],
dataRelease: row[5],
aggregationMethod: row[6],
variableSource: row[7].trim()
}))
// Need to handle these fussy subset codes separately
const variablesByCode = _.keyBy(vdemVariables.filter(v => v.shortDefinition), v => v.indicatorCode)
for (const v of vdemVariables) {
const orig = variablesByCode[v.indicatorCode]
if (orig !== v) {
if (v.indicatorName.toLowerCase().indexOf("executive") !== -1) {
v.indicatorCode += "_ex"
} else if (v.indicatorName.toLowerCase().indexOf("legislative") !== -1) {
v.indicatorCode += "_leg"
} else {
throw new Error("Unknown duplicate indicator: " + v.indicatorName)
}
v.shortDefinition = orig.shortDefinition
v.responses = orig.responses
v.dataRelease = orig.dataRelease
v.aggregationMethod = orig.aggregationMethod
v.variableSource = orig.variableSource
}
}
// User responsible for uploading this data
const userId = (await db.get(`SELECT * FROM users WHERE fullName=?`, ["Jaiden Mispy"])).id
await db.transaction(async t => {
const existingDataset = (await t.query("SELECT id FROM datasets WHERE namespace='vdem'"))[0]
if (existingDataset) {
await t.execute(`DELETE d FROM data_values AS d JOIN variables AS v ON d.variableId=v.id WHERE v.datasetId=?`, [existingDataset.id])
await t.execute(`DELETE FROM variables WHERE datasetId=?`, [existingDataset.id])
await t.execute(`DELETE FROM sources WHERE datasetId=?`, [existingDataset.id])
await t.execute(`DELETE FROM datasets WHERE id=?`, [existingDataset.id])
}
const datasetRow = ['vdem', "V-Dem Dataset Version 8 - V-Dem Institute", "", false, now, now, now, userId, now, userId, userId]
const result = await t.query("INSERT INTO datasets (namespace, name, description, isPrivate, createdAt, updatedAt, metadataEditedAt, metadataEditedByUserId, dataEditedAt, dataEditedByUserId, createdByUserId) VALUES (?)", [datasetRow])
const datasetId = result.insertId
const sourceName = "V-Dem Dataset Version 8 (2018)"
for (let i = 0; i < vdemVariables.length; i++) {
const v = vdemVariables[i]
let additionalInfo = "This variable was imported into the OWID database from Version 8 of the V-Dem Dataset. Here is the original metadata given by the V-Dem Codebook:\n\n"
additionalInfo += `Indicator Name: ${v.indicatorName}\n\n`
additionalInfo += `Indicator Code: ${v.indicatorCode}\n\n`
if (v.shortDefinition)
additionalInfo += `Short definition: ${v.shortDefinition}\n\n`
if (v.longDefinition)
additionalInfo += `Long definition: ${v.longDefinition}\n\n`
if (v.responses)
additionalInfo += `Responses: ${v.responses}\n\n`
if (v.dataRelease)
additionalInfo += `Data release: ${v.dataRelease}\n\n`
if (v.aggregationMethod)
additionalInfo += `Aggregation method: ${v.aggregationMethod}`
if (v.indicatorCode === "v2exdfcbhs_rec") {
additionalInfo += "\n| Notes: v2exdfcbhs_rec is a version of v2exdfcbhs, for v2exdfcbhs_rec the answer categories 1 and 2, 3 and 4 has been merged."
v.indicatorName += " (rec)"
}
const sourceDescription = {
dataPublishedBy: "V-Dem Institute",
dataPublisherSource: v.variableSource,
link: findUrlsInText(v.variableSource).join(","),
additionalInfo: additionalInfo
}
const sourceRow = [datasetId, sourceName, now, now, JSON.stringify(sourceDescription)]
const sourceResult = await t.query("INSERT INTO sources (datasetId, name, createdAt, updatedAt, description) VALUES (?)", [sourceRow])
const sourceId = sourceResult.insertId
const variableRow = [datasetId, sourceId, i, v.indicatorName, v.indicatorCode, v.shortDefinition, now, now, JSON.stringify(v), "", "", "", "{}"]
await t.query("INSERT INTO variables (datasetId, sourceId, columnOrder, name, code, description, createdAt, updatedAt, originalMetadata, unit, coverage, timespan, display) VALUES (?)", [variableRow])
}
})
}