datable

A light weight tool for handle data in table.

Datable是一个轻量级的数据处理工具，它可以将一个二维的数据进行Fiter，Group等操作。 Datable非常灵活，没有太多规则，比如说Group的操作如下：

// This is a GroupBy demo.
d.groupby(['date'] /* 指定按照date这一列来group */, function (res) {
    // date相同的行最后会被合并为一行，这里是对最终要输出的那一行的初始化
    res.successCount = 0
    res.errorCount = 0
}, function (res, item) {
    // 每一行都会被被交给这个函数处理，这里收到两个参数，第一个res是最终要合并输出的结果，item是当前遍历到的数据行
    if (item.code == '200') {
        res.successCount += parseInt(item.cnt)
    } else {
        res.errorCount += parseInt(item.cnt)
    }
})

Installation

npm install datable

Usage

var Datable = require('datable')

var d = new Datable({
    SPLIT_FLAG: ','
})
d.readDataFromFile('./input.csv')

console.log(d.getData())

d.filter(function (item) {
    return item.country != 'TT'
})

console.log(d.getData())

API

Refer to demo/demo.js, This demo will transfer data in demo/input.csv to demo/output.csv

Datable

new Datable([options, data])

options:

• SPLIT_FLAG : default is '\t'

data:

A array.

filter

Filter out some rows according to a condition.

Before:

date,country,code,cnt
2014-01-01,US,500,1001
2014-01-02,CN,500,500
2014-01-02,US,200,1001
2014-01-01,CN,200,500
2014-01-01,CN,200,1001
2014-01-01,TT,500,500

Code:

d.filter(function (item) {
    return item.country != 'TT'
})

After:

date,country,code,cnt
2014-01-01,US,500,1001
2014-01-02,CN,500,500
2014-01-02,US,200,1001
2014-01-01,CN,200,500
2014-01-01,CN,200,1001

groupby

Group by with some colomns, you can handler other colomns with your custom handler.

Before:

date,country,code,cnt
2014-01-01,US,500,1001
2014-01-02,CN,500,500
2014-01-02,US,200,1001
2014-01-01,CN,200,500
2014-01-01,CN,200,1001

Code:

d.groupby(['date'], function (res) {
    res.successCount = 0
    res.errorCount = 0
}, function (res, item) {
    if (item.code == '200') {
        res.successCount += parseInt(item.cnt)
    } else {
        res.errorCount += parseInt(item.cnt)
    }
})

After:

date,successCount,errorCount
2014-01-01,1501,1001
2014-01-02,1001,500

pipeline

pipeline is a helper function for processing data one by one

Before:

date,successCount,errorCount
2014-01-01,1501,1001
2014-01-02,1001,500

Code:

d.pipeline(function (item) {
    item.date += ' 00:00'
})

After:

date,successCount,errorCount
2014-01-01 00:00,1501,1001
2014-01-02 00:00,1001,500

expand

Before:

date,successCount,errorCount
2014-01-01 00:00,1501,1001
2014-01-02 00:00,1001,500

Code:

d.expand('index', ['1','2'], function (val, item) {
    item.name = 'index_' + val
})

After:

date,successCount,errorCount,index,name
2014-01-01 00:00,1501,1001,1,index_1
2014-01-01 00:00,1501,1001,2,index_2
2014-01-02 00:00,1001,500,1,index_1
2014-01-02 00:00,1001,500,2,index_2

readDataFromFile

Read data from file.

d.readDataFromFile('./input.csv')

writeDataToFile

Write Data to file.

d.writeDataToFile('./output.csv')

getData

d.getData()

setData

d.setData([])

License

ISC