z = require('https://bundle.run/zebras@0.0.11')
rainAmounts = [
18.3,
19,
14.1
]
rainDates = Array.from({length: 3}, (_, i) => Date.now() - 1000 * 60 * 60 * 24 * i)
newRainfall = {
let rainArray = []
for (var i = 0; i < rainAmounts.length; i++) {
let currRow = {rainAmount: rainAmounts[i],
date: rainDates[i]
}
rainArray.push(currRow) //appends the currRow just created onto the end of the table rainArray
//there is also option to specify an index you want it to go to if you don't want it at the end
}
return rainArray
}
//another way to do the same as above
rainfall = {
let rainDF = []
for (var i=0; i<rainAmounts.length; i++) {
rainDF.push({precipitation: rainAmounts[i], date: rainDates[i]}) //same as block above but you push directly
}
return rainDF
}
md`### 1.3 Simple Zebras Methods
Say we want to add a column, its easy. Data in this new table can be manipulated, queried, sorted, and munged. This would not transform the object in place. Instead, it returns a NEW OBJECT that contains the transformation that was just performed.
`
daySeries = ["Monday", "Tuesday", "Wednesday"]
addedCol = z.addCol("Day of the Week", daySeries, newRainfall)
//assign to new df so that the transformation is actually stored and accessible
newRainfall
//note that newRainfall was not modified by the column addition above
z.addCol("day", daySeries, rainfall) //day names the unnamed 3rd column
columns = Object.keys(rainfall[0])
shape = ({rows: rainfall.length, columns:Object.keys(rainfall[0]).length})
migrationUrl = "https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/90951b60444376eebfcbbee9beca00c083f489f6/API_SM.POP.NETM_DS2_en_csv_v2_10473747.csv"
migration = d3.csv(migrationUrl)
z.head(5, migration)
migration.length
({rows: migration.length, columns:Object.keys(migration[0]).length})
Object.values(migration[5])
Object.keys(migration[5])
firstRow = Object.values(migration[0])
lastRow = Object.values(migration[migration.length-1])
randomRow = Object.values(migration[Math.floor(Math.random() * migration.length)])
migrationColumns = Object.keys(migration[0])
numericalColumns = migrationColumns.slice(0, migrationColumns.length-4)
//this gives you the column NAMES of your desired columns, not the columns themselves
migrationParsed = z.parseNums(numericalColumns, migration)
//parseNums takes as parameters the NAMES of the columns you want to parse, and the df name
//therefore the result is the full migrations dataframe, with numbers parsed and strings still as strings
//this returns A NEW DF, not changes to the old one
z.head(5, migrationParsed)
countryName = z.getCol('Country Name', migrationParsed)
z.describe(z.getCol('1962', migrationParsed))
//why are these descriptive stats returned in strings? How do we parse them into #s for actual use?
z.valueCounts(z.getCol('Indicator Code', migrationParsed))
z.print([
{
minReturn: z.min(dailyReturns).toFixed(5),
maxReturn: z.max(dailyReturns).toFixed(5),
meanDailyReturn: z.mean(dailyReturns).toFixed(5),
medianDailyReturn: z.median(dailyReturns).toFixed(5),
dailyVolatility: z.std(dailyReturns).toFixed(5)
}
])
vegalite({
data: {values: dataRolling},
width: 700,
height: 350,
mark: {type: "line", strokeWidth: .9},
encoding: {
x: {field: "Date", type: "temporal"},
y: {field: "rollingVolatility", type: "quantitative", scale: {type:'linear'}}
}
})
dates = z.deriveCol(r=>Date.parse(r.Date), dataWithReturns)
dataDates = z.addCol('DateStamp', dates, dataWithReturns)
days = ['Sunday','Monday','Tuesday','Wednesday','Thursday','Friday','Saturday']
daysOfWeek = z.deriveCol(x=>days[(new Date(x.DateStamp)).getDay()], dataDates)
dataDays = z.addCol('Day', daysOfWeek, dataDates)
dataGrouped = z.groupBy(x=>x.Day, dataDays)
dataPiped = z.pipe(
[
z.parseNums(['Open','High','Low','Close', 'Adj Close', 'Volume']),
z.getCol('Close'),
z.pctChange(),
z.mean()
]
)(data)
data = d3.csv("https://gist.githubusercontent.com/nickslevine/b518a5fc788099d8955e77b4a4dc0628/raw/60edf99d70141a44c05fc73dff758c219f1684b4/sandp.csv")
dataParsed = z.parseNums(['Open','High','Low','Close', 'Adj Close', 'Volume'], data)
vegalite = require("@observablehq/vega-lite")
prices = z.getCol('Close', dataParsed)
dailyReturns = z.pctChange(prices)
dataWithReturns = z.addCol('DailyReturns', dailyReturns, dataParsed)
dataSorted = z.sortByCol('DailyReturns', 'asc', dataWithReturns)
rollingVolatility = z.rolling(z.std, 252, dailyReturns)
rollingMean = z.rolling(z.mean, 252, dailyReturns)
dataRolling = z.pipe([
z.addCol('rollingVolatility', rollingVolatility),
z.addCol('rollingMean', rollingMean),
])(dataWithReturns)
annualisedVolatility = z.deriveCol((x=>x.rollingVolatility * Math.sqrt(252)), dataRolling)
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
