Owner of Hammies, former Jupyter core developer and co-lead of Jupyter Notebook and JupyterLab teams
// Plot.plot({
// marks: [
// Plot.ruleY([0]),
// Plot.lineY(getRegion(homeValues, region), {
// x: "date",
// y: "price"
// }),
// Plot.lineY(getRegion(homeValuesForecast, region), {
// x: "date",
// y: "price"
// }),
// Plot.lineY(getRegion(rentals, region), {
// x: "date",
// y: "price"
// }),
// Plot.lineY(getRegion(inventory, region), {
// x: "date",
// y: "price"
// }),
// Plot.lineY(getRegion(listAndSalesPrices, region), {
// x: "date",
// y: "price"
// }),
// Plot.lineY(getRegion(salesCountPriceCuts, region), {
// x: "date",
// y: "price"
// })
// // Plot.lineY(getRegion(homeValues, "United States"), {
// // x: "date",
// // y: "price",
// // stroke: "silver"
// // })
// ]
// })
lineChart(homeValues, region)
lineChart(homeValuesForecast, region)
lineChart(rentals, region)
lineChart(inventory, region)
lineChart(listAndSalesPrices, region)
lineChart(salesCountPriceCuts, region)
regions = homeValues.map(({ RegionName }) => RegionName)
getRegion(homeValues, region)
getRegion = (data, region) =>
Object.entries(
data.find(({ RegionName }) => RegionName.match(new RegExp(region, "i"))) ||
data.find(({ RegionName }) => RegionName === "United States")
).reduce((result, [key, value]) => {
if (key.startsWith("20")) {
return [...result, { date: new Date(key), price: parseFloat(value) }];
}
return result;
}, [])
lineChart = (data, region) =>
Plot.plot({
marks: [
Plot.ruleY([0]),
Plot.lineY(getRegion(data, region), {
x: "date",
y: "price"
}),
// Plot.lineY(getRegion(data, "United States"), {
// x: "date",
// y: "price",
// stroke: "silver"
// })
]
})
// homeValues = FileAttachment(
// "Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv"
// ).csv()
// homeValues = d3.csv(
// "https://files.zillowstatic.com/research/public_csvs/zhvi/Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv?t=1669903116"
// )
homeValues = d3.csv(
"https://files.zillowstatic.com/research/public_csvs/zhvi/City_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv"
)
// homeValuesForecast = FileAttachment("Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv").csv()
homeValuesForecast = d3.csv(
"https://files.zillowstatic.com/research/public_csvs/zhvf_growth/Metro_zhvf_growth_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv"
)
// rentals = FileAttachment("Metro_ZORI_AllHomesPlusMultifamily_Smoothed.csv").csv()
rentals = d3.csv(
"https://files.zillowstatic.com/research/public_csvs/zori/Metro_zori_sm_month.csv"
)
// rentals = d3.csv(
// "https://files.zillowstatic.com/research/public_csvs/zori/City_zori_sm_month.csv?t=1669903116"
// )
// inventory = FileAttachment("Metro_invt_fs_uc_sfrcondo_sm_month.csv").csv()
inventory = d3.csv(
"https://files.zillowstatic.com/research/public_csvs/invt_fs/Metro_invt_fs_uc_sfrcondo_sm_month.csv?t=1669903116"
)
// listAndSalesPrices = FileAttachment("Metro_mlp_uc_sfrcondo_sm_month.csv").csv()
listAndSalesPrices = d3.csv(
"https://files.zillowstatic.com/research/public_csvs/mlp/Metro_mlp_uc_sfrcondo_sm_month.csv"
)
// salesCountPriceCuts = FileAttachment(
// "Metro_sales_count_now_uc_sfrcondo_month.csv"
// ).csv()
salesCountPriceCuts = d3.csv(
"https://files.zillowstatic.com/research/public_csvs/sales_count_now/Metro_sales_count_now_uc_sfrcondo_month.csv"
)
import { zillow_data, map } from "@mcmcclur/zillow-home-prices"
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.
