feat([2fd88f42]): display attributes in point tooltip

This commit is contained in:
2026-02-04 13:22:47 +00:00
parent 66438fd6d0
commit 2c05412dfb
3 changed files with 56 additions and 6 deletions

View File

@@ -116,8 +116,24 @@ async def get_heatmap_data(request: FilterRequest):
if filtered_df.empty:
return []
# Aggregate data by PLZ
plz_counts = filtered_df.groupby(plz_column_name).size().reset_index(name='count')
# Aggregate data by PLZ, and also collect attribute summaries
plz_grouped = filtered_df.groupby(plz_column_name)
plz_counts = plz_grouped.size().reset_index(name='count')
# Collect unique attributes for each PLZ
attribute_summaries = {}
for plz_val, group in plz_grouped:
summary = {}
for col in filtered_df.columns:
if col != plz_column_name and col != 'lat' and col != 'lon': # Exclude lat/lon if they somehow exist
unique_attrs = group[col].unique().tolist()
# Limit to top 3 unique values for readability
summary[col] = unique_attrs[:3]
attribute_summaries[plz_val] = summary
# Convert summaries to a DataFrame for merging
summary_df = pd.DataFrame.from_dict(attribute_summaries, orient='index')
summary_df.index.name = plz_column_name
# --- Geocoding Step ---
# Merge the aggregated counts with the geocoding dataframe
@@ -129,17 +145,45 @@ async def get_heatmap_data(request: FilterRequest):
how='inner'
)
# Merge with attribute summaries
merged_df = pd.merge(
merged_df,
summary_df,
left_on=plz_column_name,
right_index=True,
how='left'
)
# Rename columns to match frontend expectations ('lon' and 'lat')
merged_df.rename(columns={'x': 'lon', 'y': 'lat'}, inplace=True)
# Also rename the original PLZ column to the consistent name 'plz'
merged_df.rename(columns={plz_column_name: 'plz'}, inplace=True)
# Convert to the required JSON format
heatmap_data = merged_df[['plz', 'lat', 'lon', 'count']].to_dict(orient='records')
# Convert to the required JSON format, including all remaining columns (which are the attributes)
# We'll dynamically collect attribute columns for output
output_columns = ['plz', 'lat', 'lon', 'count']
for col in merged_df.columns:
if col not in output_columns and col != plz_column_name: # Ensure we don't duplicate PLZ or coords
output_columns.append(col)
print(f"Generated heatmap data with {len(heatmap_data)} PLZ points.")
return heatmap_data
heatmap_data = merged_df[output_columns].to_dict(orient='records')
# The frontend expects 'attributes_summary' as a single field, so let's restructure for that
# For each record, pick out the attributes that are not 'plz', 'lat', 'lon', 'count'
final_heatmap_data = []
for record in heatmap_data:
attrs = {k: v for k, v in record.items() if k not in ['plz', 'lat', 'lon', 'count']}
final_heatmap_data.append({
"plz": record['plz'],
"lat": record['lat'],
"lon": record['lon'],
"count": record['count'],
"attributes_summary": attrs
})
print(f"Generated heatmap data with {len(final_heatmap_data)} PLZ points.")
return final_heatmap_data
except Exception as e:
print(f"ERROR generating heatmap: {e}")

View File

@@ -16,6 +16,7 @@ export interface HeatmapPoint {
lat: number;
lon: number;
count: number;
attributes_summary?: Record<string, string[]>;
}
export type MapMode = 'points' | 'heatmap';

View File

@@ -43,6 +43,11 @@ const MapDisplay: React.FC<MapDisplayProps> = ({ heatmapData, radiusMultiplier,
<Tooltip>
PLZ: {point.plz} <br />
Count: {point.count}
{point.attributes_summary && Object.entries(point.attributes_summary).map(([attr, values]) => (
<div key={attr}>
<strong>{attr}:</strong> {values.join(', ')}
</div>
))}
</Tooltip>
</CircleMarker>
))