lets-make-csvs

Let's make CSV's from row objects

What

I wanted to create an iterative CSV, one row at a time. But I also want ot add properties.

This module will make a CSV one row at a time. It will also modify the CSV header as it goes. Because it uses Node pipes and buffers, it can handle super large files.

Usage

npm install lets-make-csvs

Use a github token, because this module is probably private

example

const async = require('async')
let lmcsv = require("lets-make-csvs")

let csv = new lmcsv("test.csv")


let myobj = [{
  "id": 1,
  "first_name": "Hartley",
  "last_name": "Mulvenna",
  "email": "hmulvenna0@delicious.com",
  "gender": "Male",
  "ip_address": "6.25.240.135",
  "hobbies": ["fishing", "camping", "hiking"]
}, {
  "id": 2,
  "first_name": "Antonio",
  "last_name": "Pigne",
  "email": "apigne1@feedburner.com",
  "gender": "Male",
  "ip_address": "102.234.210.24",
  "hobbies": ["bowling", "music"],
  "farm animals":{
    "pigs":3,
    "cows":{
      "male":2,
      "female":1
    },
    "horses":1
  }
}, {
  "id": 3,
  "first_name": "Gamaliel",
  "last_name": "Graser",
  "email": "ggraser2@nymag.com",
  "SAT score": 1420
}, {
  "id": 4,
  "first_name": "Celestyna",
  "last_name": "Pawels",
  "email": "cpawels3@home.pl",
  "gender": "Female",
  "ip_address": "18.17.253.197",
  "age": 22
}, {
  "id": 5,
  "first_name": "Mommy",
  "last_name": "Caitlin",
  "email": "mcaitlin4@smh.com.au",
  "gender": "Female",
  "ip_address": "213.145.160.242"
}, {
  "id": 6,
  "first_name": "Nettle",
  "last_name": "Mordey",
  "email": "nmordey5@ox.ac.uk",
  "gender": "Female",
  "ip_address": "178.58.2.233",
  "occupation":"Engineer"
}]

async.eachSeries(myobj, function eachRow(row, cb) {
  csv.writeRow(row, function(err) {
    if (err) return cb(err)
    return cb(null)
  })
}, function endEachRow(err) {
  if (err) throw err
  console.log("done")
})

output:

"id","first_name","last_name","email","gender","ip_address","hobbies","activites","crimes","age"
"1","Hartley","Mulvenna","hmulvenna0@delicious.com","Male","6.25.240.135","["fishing","camping","hiking"]
"2","Antonio","Pigne","apigne1@feedburner.com","Male","102.234.210.24"," ","["bowling","crime"]
"3","Gamaliel","Graser","ggraser2@nymag.com","Male","32.18.140.241"," "," ","{"theft":"misdemeanor","treason":"felony","attempted murder":"misdemeanor"}
"4","Celestyna","Pawels","cpawels3@home.pl","Female","18.17.253.197"," "," "," ","22"
"5","Mommy","Caitlin","mcaitlin4@smh.com.au","Female","213.145.160.242"," "," "," "," "
"6","Nettle","Mordey","nmordey5@ox.ac.uk","Female","178.58.2.233"," "," "," "," "

CSV result:

Open the result in your Excel or Google Sheets and you should see something like this:


id	first_name	last_name	email	gender	ip_address	hobbies	farm animals	SAT score	age	occupation
1	Hartley	Mulvenna	hmulvenna0@delicious.com	Male	6.25.240.135	["fishing","camping","hiking"]
2	Antonio	Pigne	apigne1@feedburner.com	Male	102.234.210.24	["bowling","music"]	{"pigs":3,"cows":{"male":2,"female":1},"horses":1}
3	Gamaliel	Graser	ggraser2@nymag.com					1420
4	Celestyna	Pawels	cpawels3@home.pl	Female	18.17.253.197				22
5	Mommy	Caitlin	mcaitlin4@smh.com.au	Female	213.145.160.242
6	Nettle	Mordey	nmordey5@ox.ac.uk	Female	178.58.2.233					Engineer

Todo:

Array method