Colin Maudry
160 lines
4.5 KiB
Python
160 lines
4.5 KiB
Python
import json
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import plotly.express as px
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import polars as pl
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from dash import dash_table, html
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def get_map_count_marches(lf: pl.LazyFrame):
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lf = lf.with_columns(
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pl.col("lieuExecution_code").str.head(2).str.zfill(2).alias("Département")
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)
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lf = (
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lf.select(["uid", "Département"])
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.drop_nulls()
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.unique(subset="uid")
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.group_by("Département")
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.len("uid")
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)
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# Suppression des infos pour les DOM/TOM pour l'instant
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lf = lf.remove(pl.col("Département").is_in(["97", "98"]))
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with open("./data/departements-1000m.geojson") as f:
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departements = json.load(f)
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# Ajout de feature.id
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for f in departements["features"]:
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f["id"] = f["properties"]["code"]
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df = lf.collect()
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fig = px.choropleth(
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df,
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geojson=departements,
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locations="Département",
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color="uid",
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color_continuous_scale="Reds",
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title="Nombres de marchés attribués par département (lieu d'exécution)",
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range_color=(df["uid"].min(), df["uid"].max()),
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labels={"uid": "Marchés attribués"},
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scope="europe",
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width=1000,
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height=800,
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)
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fig.update_geos(fitbounds="locations", visible=False)
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fig.update_layout(
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mapbox={
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"style": "carto-positron",
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"center": {"lon": 10, "lat": 10},
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"zoom": 1,
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"domain": {"x": [0, 1], "y": [0, 1]},
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}
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)
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return fig
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def get_barchart_sources(lf: pl.LazyFrame, type_date: str):
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labels = {
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"dateNotification": "notification",
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"datePublicationDonnees": "publication des données",
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}
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lf = lf.select("uid", type_date, "sourceDataset")
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lf = lf.unique("uid")
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# Rassemblement des datasets Atexo pour ne pas surcharger le graphique
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lf = lf.with_columns(
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pl.when(pl.col("sourceDataset").str.starts_with("atexo"))
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.then(pl.lit("plateformes atexo"))
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.otherwise(pl.col("sourceDataset"))
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.alias("sourceDataset")
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)
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# Rassemblement des datasets AWS pour ne pas surcharger le graphique
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lf = lf.with_columns(
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pl.when(pl.col("sourceDataset").str.contains(r"aws|marches\-publics.info"))
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.then(pl.lit("aws"))
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.otherwise(pl.col("sourceDataset"))
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.alias("sourceDataset")
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)
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lf = lf.with_columns(pl.col(type_date).dt.year().alias("annee"))
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lf = lf.filter(
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pl.col(type_date).is_not_null() & pl.col("annee").is_between(2019, 2025)
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)
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lf = lf.with_columns(pl.col(type_date).cast(pl.String).str.head(7))
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lf = (
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lf.group_by([type_date, "sourceDataset"])
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.len()
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.sort(by=[type_date, "len"], descending=True)
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)
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# lf = lf.with_columns(
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# pl.when(pl.col("sourceDataset").is_null()).then(
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# pl.lit("Source inconnue")).alias("sourceDataset")
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# )
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lf = lf.sort(by=["sourceDataset"], descending=False)
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df: pl.DataFrame = lf.collect()
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fig = px.bar(
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df,
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x=type_date,
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y="len",
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color="sourceDataset",
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title=f"Nombre de marchés attribués par date de {labels[type_date]} et source de données",
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labels={
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"len": "Nombre de marchés",
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type_date: f"Mois de {labels[type_date]}",
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"sourceDataset": "Source de données",
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},
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)
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return fig
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def get_sources_tables(source_path) -> html.Div:
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df = pl.read_csv(source_path)
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df = df.with_columns(
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(
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pl.lit('<a href = "')
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+ pl.col("url")
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+ pl.lit('">')
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+ pl.col("nom")
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+ pl.lit("</a>")
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).alias("nom")
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)
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df = df.drop("url")
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df = df.sort(by=["nb_marchés"], descending=True)
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datatable = dash_table.DataTable(
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id="source_table",
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columns=[
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{
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"name": i,
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"id": i,
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"presentation": "markdown",
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"type": "text",
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"format": {"nully": "N/A"},
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}
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for i in df.schema.names()
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],
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style_cell_conditional=[
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{
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"if": {"column_id": ["nom", "organisation"]},
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"minWidth": "350px",
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"textAlign": "left",
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"overflow": "hidden",
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"lineHeight": "14px",
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"whiteSpace": "normal",
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},
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],
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sort_action="native",
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markdown_options={"html": True},
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)
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datatable.data = df.to_dicts()
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return html.Div(children=datatable)
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