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[pre-commit.ci] pre-commit autoupdate (#15)
* [pre-commit.ci] pre-commit autoupdate updates: - [github.com/psf/black-pre-commit-mirror: 25.12.0 → 26.1.0](psf/black-pre-commit-mirror@25.12.0...26.1.0) - [github.com/kynan/nbstripout: 0.8.2 → 0.9.0](kynan/nbstripout@0.8.2...0.9.0) * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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‎.pre-commit-config.yaml‎

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@@ -12,7 +12,7 @@ repos:
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args: ["--maxkb=3500"]
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- repo: https://github.com/psf/black-pre-commit-mirror
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rev: 25.12.0
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rev: 26.1.0
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hooks:
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- id: black-jupyter
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@@ -27,6 +27,6 @@ repos:
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]
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- repo: https://github.com/kynan/nbstripout
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rev: 0.8.2
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rev: 0.9.0
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hooks:
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- id: nbstripout

‎book/analytics/buildings.ipynb‎

Lines changed: 24 additions & 48 deletions
Original file line numberDiff line numberDiff line change
@@ -98,13 +98,11 @@
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"outputs": [],
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"source": [
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"# Examine the first few records to understand the schema\n",
101-
"con.sql(\n",
102-
" f\"\"\"\n",
101+
"con.sql(f\"\"\"\n",
103102
"SELECT *\n",
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"FROM read_parquet('{url}')\n",
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"LIMIT 10\n",
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"\"\"\"\n",
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")"
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"\"\"\")"
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]
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},
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{
@@ -122,12 +120,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"con.sql(\n",
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" f\"\"\"\n",
123+
"con.sql(f\"\"\"\n",
127124
"SELECT COUNT(*) as count\n",
128125
"FROM read_parquet('{url}')\n",
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"\"\"\"\n",
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")"
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"\"\"\")"
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]
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},
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{
@@ -178,8 +174,7 @@
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"min_lat, max_lat = 37.0, 41.0\n",
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"\n",
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"# Create a table for the region's buildings\n",
181-
"con.sql(\n",
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" f\"\"\"\n",
177+
"con.sql(f\"\"\"\n",
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"CREATE TABLE IF NOT EXISTS buildings AS\n",
184179
"SELECT\n",
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" id,\n",
@@ -194,8 +189,7 @@
194189
"FROM read_parquet('{url}')\n",
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"WHERE bbox.xmin >= {min_lon} AND bbox.xmax <= {max_lon}\n",
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" AND bbox.ymin >= {min_lat} AND bbox.ymax <= {max_lat}\n",
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"\"\"\"\n",
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")"
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"\"\"\")"
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]
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},
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{
@@ -253,8 +247,7 @@
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"outputs": [],
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"source": [
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"# Calculate comprehensive area statistics\n",
256-
"con.sql(\n",
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" \"\"\"\n",
250+
"con.sql(\"\"\"\n",
258251
"SELECT\n",
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" COUNT(*) as building_count,\n",
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" ROUND(SUM(area_sq_meters) / 1000000, 2) as total_area_sq_km,\n",
@@ -263,8 +256,7 @@
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" ROUND(MAX(area_sq_meters), 2) as max_area,\n",
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" ROUND(MEDIAN(area_sq_meters), 2) as median_area\n",
265258
"FROM buildings\n",
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"\"\"\"\n",
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")"
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"\"\"\")"
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]
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},
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{
@@ -283,8 +275,7 @@
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"outputs": [],
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"source": [
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"# Categorize buildings by size\n",
286-
"con.sql(\n",
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" \"\"\"\n",
278+
"con.sql(\"\"\"\n",
288279
"SELECT\n",
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" CASE\n",
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" WHEN area_sq_meters < 50 THEN 'Small (<50 m²)'\n",
@@ -297,8 +288,7 @@
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"FROM buildings\n",
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"GROUP BY size_category\n",
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"ORDER BY MIN(area_sq_meters)\n",
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"\"\"\"\n",
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")"
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"\"\"\")"
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]
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},
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{
@@ -317,13 +307,11 @@
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"outputs": [],
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"source": [
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"# Sample 10,000 buildings for visualization\n",
320-
"sample_gdf = con.sql(\n",
321-
" \"\"\"\n",
310+
"sample_gdf = con.sql(\"\"\"\n",
322311
"SELECT area_sq_meters, ST_AsText(geometry) as geometry\n",
323312
"FROM buildings\n",
324313
"USING SAMPLE 10000\n",
325-
"\"\"\"\n",
326-
").df()\n",
314+
"\"\"\").df()\n",
327315
"\n",
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"# Convert to GeoDataFrame\n",
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"gdf = leafmap.df_to_gdf(sample_gdf, src_crs=\"EPSG:4326\")"
@@ -542,20 +530,17 @@
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"outputs": [],
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"source": [
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"# First, create a temporary table with county boundaries\n",
545-
"con.sql(\n",
546-
" \"\"\"\n",
533+
"con.sql(\"\"\"\n",
547534
"CREATE OR REPLACE TABLE counties AS\n",
548535
"SELECT\n",
549536
" NAME as county_name,\n",
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" geometry\n",
551538
"FROM read_parquet('https://data.gishub.org/us/us_counties.parquet')\n",
552539
"WHERE STATE = '08'\n",
553-
"\"\"\"\n",
554-
")\n",
540+
"\"\"\")\n",
555541
"\n",
556542
"# Perform spatial join and calculate statistics by county\n",
557-
"comparison_df = con.sql(\n",
558-
" \"\"\"\n",
543+
"comparison_df = con.sql(\"\"\"\n",
559544
"SELECT\n",
560545
" c.county_name,\n",
561546
" COUNT(b.*) as building_count,\n",
@@ -566,8 +551,7 @@
566551
"JOIN counties c ON ST_Intersects(b.geometry, c.geometry)\n",
567552
"GROUP BY c.county_name\n",
568553
"ORDER BY building_count DESC\n",
569-
"\"\"\"\n",
570-
").df()\n",
554+
"\"\"\").df()\n",
571555
"comparison_df.head()"
572556
]
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},
@@ -667,8 +651,7 @@
667651
"outputs": [],
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"source": [
669653
"# Filter to OSM buildings only\n",
670-
"con.sql(\n",
671-
" \"\"\"\n",
654+
"con.sql(\"\"\"\n",
672655
"SELECT\n",
673656
" COUNT(*) as osm_building_count,\n",
674657
" ROUND(AVG(area_sq_meters), 2) as avg_area,\n",
@@ -677,8 +660,7 @@
677660
" COUNT(CASE WHEN class IS NOT NULL THEN 1 END) as buildings_with_class\n",
678661
"FROM buildings\n",
679662
"WHERE source='OpenStreetMap'\n",
680-
"\"\"\"\n",
681-
").show()"
663+
"\"\"\").show()"
682664
]
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},
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{
@@ -841,12 +823,10 @@
841823
"metadata": {},
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"outputs": [],
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"source": [
844-
"con.sql(\n",
845-
" \"\"\"\n",
826+
"con.sql(\"\"\"\n",
846827
"CREATE OR REPLACE TABLE grid AS\n",
847828
"SELECT * FROM ST_Read('grid.geojson')\n",
848-
"\"\"\"\n",
849-
")"
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"\"\"\")"
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]
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},
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{
@@ -874,8 +854,7 @@
874854
"metadata": {},
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"outputs": [],
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"source": [
877-
"con.sql(\n",
878-
" \"\"\"\n",
857+
"con.sql(\"\"\"\n",
879858
"CREATE OR REPLACE TABLE grid_building_counts AS\n",
880859
"SELECT\n",
881860
" g.id,\n",
@@ -897,8 +876,7 @@
897876
" g.geom\n",
898877
"ORDER BY\n",
899878
" g.id;\n",
900-
"\"\"\"\n",
901-
")"
879+
"\"\"\")"
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]
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},
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{
@@ -1047,12 +1025,10 @@
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"metadata": {},
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"outputs": [],
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"source": [
1050-
"con.sql(\n",
1051-
" \"\"\"\n",
1028+
"con.sql(\"\"\"\n",
10521029
"CREATE TABLE IF NOT EXISTS h3_res4_geo AS\n",
10531030
"SELECT * FROM 'https://data.gishub.org/duckdb/h3_res4_geo.parquet';\n",
1054-
"\"\"\"\n",
1055-
")"
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"\"\"\")"
10561032
]
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},
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{

‎book/analytics/dashboards.ipynb‎

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Original file line numberDiff line numberDiff line change
@@ -230,12 +230,10 @@
230230
" output_widget.clear_output()\n",
231231
" if len(m.draw_features_selected) > 0:\n",
232232
" geojson = m.draw_features_selected[0][\"geometry\"]\n",
233-
" df = con.sql(\n",
234-
" f\"\"\"\n",
233+
" df = con.sql(f\"\"\"\n",
235234
" SELECT * EXCLUDE (geometry), ST_AsText(geometry) AS geometry FROM h3_res4_geo\n",
236235
" WHERE ST_Intersects(geometry, ST_GeomFromGeoJSON('{json.dumps(geojson)}'));\n",
237-
" \"\"\"\n",
238-
" ).df()\n",
236+
" \"\"\").df()\n",
239237
" gdf = leafmap.df_to_gdf(df)\n",
240238
" if \"H3 Hexagon\" in m.layer_dict:\n",
241239
" m.remove_layer(\"H3 Hexagon\")\n",

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