31:[] 2f:{"title":"Atelier Parquet SSPHub","cells":[{"id":0,"value":"# Atelier Parquet SSPHub\n\nUne application réactive dont le _back office_ s'appuie sur `DuckDB`. Cet exemple accompagne [ce tutoriel `R` et `Python`](https://ssphub.github.io/ssphub-ateliers-parquet)","pinned":false,"hidden":false,"mode":"md"},{"id":80,"value":"container = html`\n

\n\n \n

${viewof search}

\n\n \n

${produce_histo(dvf)}

${viewof table_dvf}

\n\n\n

\n`\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":143,"value":"{\n const container = html`\n \n

`\n\n yield container\n\n const map = L.map('map');\n\n L.tileLayer('https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png', {\n attribution: '© OpenStreetMap contributors'\n }).addTo(map);\n\n const geoLayer = L.geoJSON(dvf_geojson, {\n onEachFeature: (feature, layer) => {\n const props = feature.properties;\n const popupContent = `\n Date: ${props.date}
\n Valeur foncière: ${props.valeur_fonciere} €
\n Commune: ${props.code_commune}\n `;\n layer.bindPopup(popupContent);\n }\n }).addTo(map);\n\n // 👇 Automatically fit view to all points\n map.fitBounds(geoLayer.getBounds());\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":78,"value":"

\n## Code","pinned":false,"hidden":false,"mode":"md"},{"id":177,"value":"viewof table_dvf = Inputs.table(dvf, {columns: [\"date\", \"valeur_fonciere\"], maxWidth: 300, rows: 15})","pinned":false,"hidden":false,"mode":"ojs"},{"id":133,"value":"dvf_geojson = {\n \n const geojson = {\n type: \"FeatureCollection\",\n features: dvf.map(row => ({\n type: \"Feature\",\n geometry: {\n type: \"Point\",\n coordinates: [row.longitude, row.latitude],\n },\n properties: {\n date: row.date,\n valeur_fonciere: row.valeur_fonciere,\n code_commune: row.code_commune,\n valeur_fonciere_bar: row.valeur_fonciere_bar,\n },\n })),\n };\n\n return geojson\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":57,"value":"viewof search = Inputs.select(cog, {format: x => x.LIBELLE, value: cog.find(t => t.LIBELLE == \"Grasse\"), label: \"Commune\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":23,"value":"cog = db.query(`SELECT * FROM read_csv_auto(\"https://minio.lab.sspcloud.fr/lgaliana/data/python-ENSAE/cog_2023.csv\") WHERE DEP == '06'`)","pinned":true,"hidden":false,"mode":"ojs"},{"id":10,"value":"dvf = db.query(query)","pinned":true,"hidden":false,"mode":"ojs"},{"id":7,"value":"query = `\n FROM read_parquet('https://minio.lab.sspcloud.fr/projet-formation/nouvelles-sources/data/geoparquet/dvf.parquet')\n SELECT\n CAST(date_mutation AS date) AS date,\n valeur_fonciere, code_commune,\n longitude, latitude, valeur_fonciere AS valeur_fonciere_bar\n WHERE code_commune = '${search.COM}'\n`","pinned":true,"hidden":false,"mode":"ojs"},{"id":5,"value":"db = DuckDBClient.of({})","pinned":true,"hidden":false,"mode":"ojs"},{"id":94,"value":"produce_histo = function(dvf){\n const histo = Plot.plot({\n marks: [\n Plot.rectY(dvf, Plot.binX({y: \"count\"}, {x: \"valeur_fonciere\"})),\n Plot.ruleY([0])\n ]\n})\n return histo\n}\n","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}