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Ajoute exemple barycentres
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‎content/manipulation/04c_API_TP.qmd

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The first mode (access via a browser) is primarily used when a web interface allows a user to make choices in order to return results corresponding to those selections. Let’s revisit the example of the geolocation API that we will use in this chapter. Imagine a web interface that offers the user two choices: a postal code and an address. These inputs will be injected into the query, and the server will respond with the appropriate geolocation.
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Une explication interactive est disponible sur [le site du cours](https://pythonds.linogaliana.fr/content/manipulation/04c_API_TP.html).
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If a beautiful display is desired, like the map above, the web browser will need to reprocess this output, which is typically done using `Javascript`, the programming language embedded in web browsers.
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## Comment faire avec `Python` ?
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# Exercices supplémentaires
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::: {.exercise}
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## Exercice bonus
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## Exercice bonus 1: et si on ajoutait des informations sur la valeur ajoutée des lycées ?
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Dans notre exemple sur les écoles, se restreindre aux lycées et ajouter les informations sur la valeur ajoutée des lycées disponibles [ici](https://data.education.gouv.fr/explore/dataset/fr-en-indicateurs-de-resultat-des-lycees-denseignement-general-et-technologique/table/?sort=-annee).
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# Additional Exercises
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# Additional Exercises: et si on ajoutait des informations sur la valeur ajoutée des lycées ?
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::: {.exercise}
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## Bonus Exercise
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::: {.exercise}
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## Exercice bonus 2: on sort où ce soir ?
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Trouver un lieu commun où se retrouver entre amis est toujours l'objet d'âpres négociations: et si on laissait guider par la géographie ?
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1. Créer un `DataFrame` enregistrant une série d'adresses et de codes postaux comme l'exemple ci-dessous
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2. Adapter le code de l'exercice sur l'API BAN, avec l'appui de la documentation, pour géolocaliser ces adresses
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3. En supposant que vos données géolocalisées se nomment `adresses_geocoded`, utiliser le code proposé pour transformer celles-ci en polygone
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4. Calculer le centroid et représenter sur une carte interactive `Folium` comme précédemment
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Vous aviez oublié qu'il y avait un couple dans le groupe... Tenir compte de la variable `poids` pour calculer le barycentre et trouver où vous retrouver ce soir.
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<details>
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<summary>
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Créer le polygone à partir des géolocalisations
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</summary>
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```{.python}
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from shapely.geometry import Polygon
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coordinates = list(zip(adresses_geocoded['longitude'], adresses_geocoded['latitude']))
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polygon = Polygon(coordinates)
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polygon = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry=[polygon])
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polygon
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```
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</details>
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::: {.exercise}
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## Bonus Exercise 2: Where are we going out tonight?
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Finding a common place to meet friends is always a subject of tough negotiations. What if we let geography guide us?
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1. Create a `DataFrame` recording a series of addresses and postal codes, like the example below.
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2. Adapt the code from the exercise on the BAN API, using its documentation, to geolocate these addresses.
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3. Assuming your geolocated data is named `adresses_geocoded`, use the proposed code to transform them into a polygon.
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4. Calculate the centroid and display it on an interactive `Folium` map as before.
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You forgot there’s a couple in the group... Take into account the `poids` variable to calculate the barycenter and find out where to meet tonight.
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<details>
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<summary>
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Create the polygon from the geolocations
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</summary>
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```{.python}
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from shapely.geometry import Polygon
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coordinates = list(zip(adresses_geocoded['longitude'], adresses_geocoded['latitude']))
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polygon = Polygon(coordinates)
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polygon = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry=[polygon])
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polygon
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```
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</details>
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:::
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::::
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Le DataFrame d'exemple:
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The example DataFrame:
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```{python}
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#| echo: true
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adresses_text = pd.DataFrame(
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{
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"adresse": [
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"10 Rue de Rivoli",
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"15 Boulevard Saint-Michel",
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"8 Rue Saint-Honoré",
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"20 Avenue des Champs-Élysées",
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"Place de la Bastille",
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],
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"cp": ["75004", "75005", "75001", "75008", "75011"],
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"poids": [2, 1, 1, 1, 1]
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})
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adresses_text
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```
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```{python}
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import pathlib
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output_path = pathlib.Path("data/output")
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output_path.mkdir(parents=True, exist_ok=True)
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csv_file = output_path / "bpe_before_geoloc.csv"
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adresses_text.loc[:, ["adresse", "poids", "cp"]].to_csv(csv_file, index=False)
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params = {
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"columns": ["adresse"],
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"postcode": "cp",
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"result_columns": ["result_score", "latitude", "longitude"],
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}
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response = requests.post(
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"https://api-adresse.data.gouv.fr/search/csv/",
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data=params,
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files={"data": open(csv_file, "rb")},
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)
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```
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```{python}
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adresses_geocoded = pd.read_csv(io.StringIO(response.text))
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adresses_geocoded
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```
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La géolocalisation obtenue pour cet exemple
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The geolocation obtained for this example
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```{python}
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from shapely.geometry import Polygon
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coordinates = list(zip(adresses_geocoded['longitude'], adresses_geocoded['latitude']))
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polygon = Polygon(coordinates)
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polygon = gpd.GeoDataFrame(index=[0], crs='epsg:4326', geometry=[polygon])
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```
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```{python}
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#| output: false
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import folium
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gdf_points = gpd.GeoDataFrame(
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adresses_geocoded,
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geometry=gpd.points_from_xy(adresses_geocoded.longitude, adresses_geocoded.latitude), crs="EPSG:4326"
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)
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total_weight = adresses_geocoded['poids'].sum()
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barycenter_x = (adresses_geocoded['longitude'] * adresses_geocoded['poids']).sum() / total_weight
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barycenter_y = (adresses_geocoded['latitude'] * adresses_geocoded['poids']).sum() / total_weight
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# Affichage des coordonnées du barycentre
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barycenter = (barycenter_x, barycenter_y)
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barycenter
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# Calculate the centroid of the polygon
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polygon_centroid = polygon.centroid
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# Initialize a Folium map centered on the centroid
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map_center = [polygon_centroid.y, polygon_centroid.x]
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m = folium.Map(location=map_center, zoom_start=13)
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# Add the polygon to the map
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folium.GeoJson(polygon, name="Polygon").add_to(m)
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# Add the points to the map
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for _, row in gdf_points.iterrows():
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folium.Marker(
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location=[row.geometry.y, row.geometry.x],
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popup=row['adresse'],
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).add_to(m)
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# Add the centroid in red
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folium.Marker(
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location=[barycenter_y, barycenter_x],
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popup="Barycentre",
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icon=folium.Icon(color="red")
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).add_to(m)
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# Met à jour le centroïde pour qu'il soit vert
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folium.Marker(
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location=[polygon.centroid.y, polygon.centroid.x],
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popup="Centroid",
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icon=folium.Icon(color="green")
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).add_to(m)
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```
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Voici la carte obtenue sur le jeu d'exemple. On sera peut-être plus au sec avec le barycentre qu'avec le centroid.
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Here is the map obtained from the example dataset. We might stay drier with the barycenter than with the centroid.
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```{python}
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m
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```
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<!--------
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## Ouverture avec les API de code pour consommer des modèles (exemple avec hf)?
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-------->
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<!----------
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API interactive example

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