Bonjour Stack Exchange,
Pour la vie de moi je ne peux pas comprendre ce que je fais de mal à ce point. Je suis un programmeur débutant, donc je suis, fier d'arriver ici, mais si il y a d'autres solutions plus efficaces, n'hésitez pas à suggérer.
Les Fichiers De Données:
- gdf_final_serial
- ^Ceci est une version abrégée que le dataset est ~680K Lignes
- geo_school
- ^Json utilisé
Ce que j'ai besoin de le tableau de bord application pour le faire:
- Travailler comme un Multi-Filtrables Carte SIG en ce qui concerne les champs répertoriés (Comté, District, le Nom, l'Âge, RE, Quartiles, le Logement Précaire)
- Basé sur les filtres choisis, la mise à jour de la px.choropleth_mapbox avec le décompte précis des points.
Nous allons sauter dans le code. Je suis également en cours d'exécution sur une Google Colab ordinateur Portable.
filename_1 = root_path+"schoolDistrictBoundaries_Fixed.json"
file = open(filename_1)
schoolDistricts = gpd.read_file(file)
^^ être couru dans une cellule
import dash
import dash.dependencies
import plotly.express as px
import pandas as pd
from jupyter_dash import JupyterDash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import plotly.graph_objects as go
import numpy as np
px.set_mapbox_access_token(mapbox_access_token)
### Creating gdf_final_serial
geo_df = gpd.GeoDataFrame.from_features(geo_school["features"]).merge(joined, on="OBJECTID").set_index("OBJECTID") #GeoJson Join to Joined Dataframe
cleaned_geo_df = geo_df[["geometry_x", "CountyName_x", "RE", "QUARTILES", "HOUSING_INSECURE", "County", "Age", "DistrictName_x"]] #Picks the right columns
geo_df_final = cleaned_geo_df.rename(columns={"CountyName_x": "CountyName", "geometry_x": "geometry", 'DistrictName_x': 'DistrictName'}) #Changing Column Names
geo_df_final.to_pickle(root_path+"geo_df_final") #Write to drive as a pickle to serialize
df_final_serial = pd.read_pickle(root_path+"geo_df_final") #Read as DataFrame
gdf_final_serial = gpd.GeoDataFrame(data = df_final_serial, geometry= "geometry", crs = "epsg:4326") #Turn into GeoPandas DataFrame and set the geometry and crs
re_indicators = gdf_final_serial.RE.unique()
county_indicators = gdf_final_serial.CountyName.unique()
age_indicators = gdf_final_serial.Age.unique()
district_indicators = gdf_final_serial.DistrictName.unique()
external_stylesheets = ["https://codepen.io/chriddyp/pen/bWLwgP.css"]
app = JupyterDash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div(
[
html.Div(
children=[
html.Label("County"),
dcc.Checklist(
id="county",
options=[{"label": i, "value": i} for i in county_indicators],
value=[]
),
html.Label("District Name"),
dcc.Dropdown(
id="dname",
options=[],
value=[],
multi=True,
),
html.Label("Age"),
dcc.Dropdown(id="age",
options=[],
value=[],
multi = True),
html.Label("RE"),
dcc.Dropdown(id="re",
options=[],
value=[],
multi = True),
html.Label("Quartiles"),
dcc.Dropdown(id="quartiles",
options=[],
value=[],
multi=True),
html.Label("Housing"),
dcc.Dropdown(id="housing",
options=[],
value=[],
multi=True),
dcc.Graph(id="my_map", figure={})])
]
)
@app.callback(
dash.dependencies.Output("dname", "options"),
dash.dependencies.Input("county", "value")
)
def choose_county(county_pick):
if len(county_pick) > 0:
dff=gdf_final_serial[gdf_final_serial.CountyName.isin(county_pick)]
else:
raise dash.exceptions.PreventUpdate
return [{"label": i, "value": i} for i in (dff.DistrictName.unique())]
@app.callback(
dash.dependencies.Output("dname", "value"),
dash.dependencies.Input("dname", "options"),
)
def set_city_value(available_options_dname):
return [x["value"] for x in available_options_dname]
@app.callback(
dash.dependencies.Output("age", "options"),
dash.dependencies.Input("dname", "value")
)
def dname_age_picker(choose_dname):
print(choose_dname)
if len(choose_dname) > 0:
dff = gdf_final_serial[gdf_final_serial.DistrictName.isin(choose_dname)]
else:
raise dash.exceptions.PreventUpdate
return [{"label": i, "value": i} for i in (dff.Age.unique())]
@app.callback(
dash.dependencies.Output("age", "value"),
dash.dependencies.Input("age", "options"),
)
def set_age_value(available_options_age):
return [x["value"] for x in available_options_age]
@app.callback(
dash.dependencies.Output("re", "options"),
dash.dependencies.Input("age", "value")
)
def age_re_picker(choose_age):
if len(choose_age) > 0:
dff = gdf_final_serial[gdf_final_serial.Age.isin(choose_age)].dropna(axis = 0, how = 'any', subset = ["RE"])
else:
raise dash.exceptions.PreventUpdate
return [{"label": i, "value": i} for i in (dff.RE.unique())]
@app.callback(
dash.dependencies.Output("re", "value"),
dash.dependencies.Input("re", "options")
)
def set_re_value(available_options_re):
return [x["value"] for x in available_options_re]
@app.callback(
dash.dependencies.Output("quartiles", "options"),
dash.dependencies.Input("re", "value")
)
def re_quartile_picker(choose_re_value):
if len(choose_re_value) >= 0:
dff = gdf_final_serial[gdf_final_serial.RE.isin(choose_re_value)].dropna(axis = 0, how = 'any', subset = ["QUARTILES"])
else:
raise dash.exceptions.PreventUpdate
return [{"label": i, "value": i} for i in (dff.QUARTILES.unique())]
@app.callback(
dash.dependencies.Output("quartiles", "value"),
dash.dependencies.Input("quartiles", "options"),
)
def set_quart_value(available_options_quart):
return [x["value"] for x in available_options_quart]
@app.callback(
dash.dependencies.Output("housing", "options"),
dash.dependencies.Input("quartiles", "value")
)
def quart_picker(choose_quart_value):
if len(choose_quart_value) >= 0:
dff = gdf_final_serial[gdf_final_serial.QUARTILES.isin(choose_quart_value)]
else:
raise dash.exceptions.PreventUpdate
return [{"label": i, "value": i} for i in (dff.HOUSING_INSECURE.unique())]
@app.callback(
dash.dependencies.Output("housing", "value"),
dash.dependencies.Input("housing", "options"),
)
def set_housing_value(available_options_housing):
return [x["value"] for x in available_options_housing]
@app.callback(
dash.dependencies.Output("my_map", "figure"),
[dash.dependencies.Input("housing", "value"),
dash.dependencies.Input("quartiles", "value"),
dash.dependencies.Input("re", "value"),
dash.dependencies.Input("age", "value"),
dash.dependencies.Input("dname", "value"),
dash.dependencies.Input("county", "value")]
)
def update_fig(selected_housing, selected_quartiles, selected_re, selected_age, selected_dname, selected_county):
gdff_1 = gdf_final_serial[gdf_final_serial.CountyName.isin(selected_county) &
gdf_final_serial.DistrictName.isin(selected_dname) &
gdf_final_serial.Age.isin(selected_age) &
gdf_final_serial.RE.isin(selected_re) &
gdf_final_serial.QUARTILES.isin(selected_quartiles) &
gdf_final_serial.HOUSING_INSECURE.isin(selected_housing)]
count_points = gdff_1.groupby("OBJECTID").size().rename("points")
gdf_last = gdff_1.merge(count_points, on="OBJECTID", how="right", right_index = True)
gdf_last.to_pickle(root_path+"gdf_last") #Write to drive as a pickle to serialize
ddf_final_serial = pd.read_pickle(root_path+"gdf_last") #Read as DataFrame
gddf_final_serial = gpd.GeoDataFrame(data = ddf_final_serial, geometry= "geometry", crs = "epsg:4326")
gdff_2 = gddf_final_serial.head(50)
px.set_mapbox_access_token(mapbox_access_token)
fig = px.choropleth_mapbox(data_frame= gdff_2,
geojson= geo_school,
locations= 'DistrictName',
featureidkey = 'properties.DistrictName',
color="points",
center={"lat": 38.5941, "lon": -119.8815},
mapbox_style = 'dark').update_layout(mapbox_accesstoken = mapbox_access_token)
return fig
# Run app and display result inline in the notebook
if __name__ == "__main__":
app.run_server(host="127.0.0.1", port="8888",debug=True, use_reloader=False)
J'ai été en mesure de recevoir diverses conditions où la carte/filtre à produire un seul comté, parfois deux comtés-- mais jamais tout le toutim. Gardez à l'esprit mon but ultime est de créer ce pour l' ENSEMBLE du jeu de données (peut-être à l'aide de RapidsAI de NVIDIA; cependant, je vais avoir du mal à installer aussi bien au sein de Google Colab).
Ce qui est plus intéressant, c'est que lorsque j'exécute ce code à l'extérieur du tableau de bord (comme un échantillon de l'ensemble de données constatées) et la transmettre à la fig.show(), il va se montrer; toutefois, il ne fonctionnera pas dans le tableau de bord de l'app.
Je soupçonne que le problème se soit avec mon json de la manipulation ou de la finale de rappel que le débogueur indique que tandis que les entrées sont là, my_map.la figure n'est pas de la sortie des données. J'apprécie l'aide.