Lbatson

import _ from 'lodash';
import React, { Component } from 'react';

class Table extends Component {
  render() {
    const { columns, data } = this.props;
    // spread two-dimensional array to arguments for zip
    // destructure resulting array elements from zip
    let [names, props] = _.zip(...columns);
    // build column headers with name values

Lbatson

func setTimeForDate (date: NSDate, hour: Int, minute: Int, second: Int) -> NSDate {
  let unitFlags = [.Year, .Month, .Day, .Hour, .Minute, .Second] as NSCalendarUnit
  // calendar here is NSCalendar.currentCalendar()
  let dateComponents = calendar.components(unitFlags, fromDate: date)
  dateComponents.hour = hour
  dateComponents.minute = minute
  dateComponents.second = second
  let newDate = calendar.dateFromComponents(dateComponents)
  print(newDate)
  return newDate!

Lbatson

var spreadsheet = SpreadsheetApp.getActiveSpreadsheet(),
    sheet = SpreadsheetApp.getActiveSheet(),
    rows = sheet.getDataRange(),
    numRows = rows.getNumRows(),
    values = rows.getValues(),
    endDate = new Date(),
    endDateCell = sheet.getRange("J2"),
    archiveCounter = sheet.getRange("K2").getValue(),
    archiveFolder = '',
    emailCell = sheet.getRange("L2").getValue(),

Lbatson

var sheet = SpreadsheetApp.getActiveSheet(),
    rows = sheet.getDataRange(),
    numRows = rows.getNumRows(),
    values = rows.getValues(),
    startTimeCol = 3,
    endTimeCol = 4,
    re = ' at';
 
/**
 * Retrieves all the rows in the active spreadsheet that contain data and converts Date/Time if necessary

Lbatson

import sys
import getopt
import os
import string as str

def findDuplicateFilenames(dir):
  unique, dupes = [], []
  try:
    for filename in os.listdir(dir):
      name = str.split(filename, '.')[0]

Lbatson

#converts csv data to mysql insert statements
import csv

#prompt user for file name
filename = raw_input("Enter the csv file name: ")
tablename = raw_input("Enter db table name for insert statements: ")

# open csv file
with open(filename + '.csv', 'rb') as csvfile:
  # store in dict