{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Some timings with GeoPandas new Parquet and Feather file format support" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import geopandas" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# ignore the warnings of it being experimental\n", "import warnings\n", "warnings.filterwarnings(\"ignore\", \"this is an initial implementation of Parquet file support\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test case 1: Natural Earth 1:10m Admin 1 – States, Provinces\n", "\n", "https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-1-states-provinces/" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "df = geopandas.read_file(\"zip+https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_1_states_provinces.zip\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Writing" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.85 s, sys: 67.3 ms, total: 1.91 s\n", "Wall time: 1.91 s\n" ] } ], "source": [ "%time df.to_file(\"test_ne_10m.shp\", driver='ESRI Shapefile')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.53 s, sys: 146 ms, total: 1.68 s\n", "Wall time: 2.45 s\n" ] } ], "source": [ "%time df.to_file(\"test_ne_10m.gpkg\", driver=\"GPKG\")" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 209 ms, sys: 28.2 ms, total: 237 ms\n", "Wall time: 236 ms\n" ] } ], "source": [ "%time df.to_parquet(\"test_ne_10m.parquet\")" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 212 ms, sys: 19.2 ms, total: 231 ms\n", "Wall time: 215 ms\n" ] } ], "source": [ "%time df.to_feather(\"test_ne_10m.feather\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reading" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "825 ms ± 8.42 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit geopandas.read_file(\"test_ne_10m.shp\", driver='ESRI Shapefile')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "729 ms ± 4.76 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n" ] } ], "source": [ "%timeit geopandas.read_file(\"test_ne_10m.gpkg\", driver='GPKG')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "161 ms ± 2.68 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "%timeit geopandas.read_parquet(\"test_ne_10m.parquet\")" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "134 ms ± 839 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n" ] } ], "source": [ "%timeit geopandas.read_feather(\"test_ne_10m.feather\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### File sizes" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 joris joris 10 Mai 20 20:35 test_ne_10m.cpg\n", "-rw-r--r-- 1 joris joris 23M Mai 20 20:35 test_ne_10m.dbf\n", "-rw-r--r-- 1 joris joris 19M Mai 20 20:35 test_ne_10m.feather\n", "-rw-r--r-- 1 joris joris 27M Mai 20 20:35 test_ne_10m.gpkg\n", "-rw-r--r-- 1 joris joris 20M Mai 20 20:35 test_ne_10m.parquet\n", "-rw-r--r-- 1 joris joris 145 Mai 20 20:35 test_ne_10m.prj\n", "-rw-r--r-- 1 joris joris 21M Mai 20 20:35 test_ne_10m.shp\n", "-rw-r--r-- 1 joris joris 36K Mai 20 20:35 test_ne_10m.shx\n" ] } ], "source": [ "!ls test_ne_10m.* -lh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test case 2: OpenStreetMap buildings" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pyrosm" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Download pbf data\n", "fp = pyrosm.get_data(\"London\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Initialize the OSM object\n", "osm = pyrosm.OSM(fp)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "buildings = osm.get_buildings()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df = buildings[[\"id\", \"osm_type\", \"building\", \"amenity\", \"addr:street\", \"timestamp\", \"geometry\"]].rename(columns={\"id\": \"osm_id\"})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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osm_idosm_typebuildingamenityaddr:streettimestampgeometry
02956186wayblockNoneNone0POLYGON ((-0.02162 51.44472, -0.02033 51.44469...
12956187wayyestownhallCatford Broadway0POLYGON ((-0.02110 51.44523, -0.02132 51.44508...
22956188wayyestheatreNone0POLYGON ((-0.02004 51.44536, -0.02006 51.44528...
32956192waystoreNoneNone0POLYGON ((-0.01900 51.44462, -0.01864 51.44458...
42956193waystoreNoneNone0POLYGON ((-0.01752 51.44542, -0.01815 51.44551...
........................
584187266218115929relationresidentialNoneBedford Gardens0POLYGON ((-0.19751 51.50561, -0.19750 51.50562...
584188266229200037relationresidentialNoneBedford Gardens0MULTIPOLYGON (((-0.19738 51.50565, -0.19730 51...
584189266395470798relationyesNoneNone0POLYGON ((-0.11464 51.45445, -0.11467 51.45450...
584190266406556085relationyesNoneNone0POLYGON ((-0.11409 51.45358, -0.11412 51.45362...
584191266417641373relationyesNoneNone0POLYGON ((-0.11420 51.45375, -0.11422 51.45378...
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584192 rows × 7 columns

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" ], "text/plain": [ " osm_id osm_type building amenity addr:street \\\n", "0 2956186 way block None None \n", "1 2956187 way yes townhall Catford Broadway \n", "2 2956188 way yes theatre None \n", "3 2956192 way store None None \n", "4 2956193 way store None None \n", "... ... ... ... ... ... \n", "584187 266218115929 relation residential None Bedford Gardens \n", "584188 266229200037 relation residential None Bedford Gardens \n", "584189 266395470798 relation yes None None \n", "584190 266406556085 relation yes None None \n", "584191 266417641373 relation yes None None \n", "\n", " timestamp geometry \n", "0 0 POLYGON ((-0.02162 51.44472, -0.02033 51.44469... \n", "1 0 POLYGON ((-0.02110 51.44523, -0.02132 51.44508... \n", "2 0 POLYGON ((-0.02004 51.44536, -0.02006 51.44528... \n", "3 0 POLYGON ((-0.01900 51.44462, -0.01864 51.44458... \n", "4 0 POLYGON ((-0.01752 51.44542, -0.01815 51.44551... \n", "... ... ... \n", "584187 0 POLYGON ((-0.19751 51.50561, -0.19750 51.50562... \n", "584188 0 MULTIPOLYGON (((-0.19738 51.50565, -0.19730 51... \n", "584189 0 POLYGON ((-0.11464 51.45445, -0.11467 51.45450... \n", "584190 0 POLYGON ((-0.11409 51.45358, -0.11412 51.45362... \n", "584191 0 POLYGON ((-0.11420 51.45375, -0.11422 51.45378... \n", "\n", "[584192 rows x 7 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Polygon 583953\n", "MultiPolygon 132\n", "LineString 107\n", "dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.geom_type.value_counts()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Select only Polygon/MultiPolygons to have uniform geometry type\n", "df = df[df.geom_type != \"LineString\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Writing" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 49.9 s, sys: 2.16 s, total: 52.1 s\n", "Wall time: 52.2 s\n" ] } ], "source": [ "%time df.to_file(\"test_london_buildings.shp\", driver='ESRI Shapefile')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 54.6 s, sys: 442 ms, total: 55.1 s\n", "Wall time: 55.2 s\n" ] } ], "source": [ "%time df.to_file(\"test_london_buildings.gpkg\", driver=\"GPKG\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.14 s, sys: 104 ms, total: 1.25 s\n", "Wall time: 1.26 s\n" ] } ], "source": [ "%time df.to_parquet(\"test_london_buildings.parquet\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.1 s, sys: 90.8 ms, total: 1.19 s\n", "Wall time: 1.16 s\n" ] } ], "source": [ "%time df.to_feather(\"test_london_buildings.feather\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reading" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 16.7 s, sys: 350 ms, total: 17.1 s\n", "Wall time: 16.9 s\n" ] } ], "source": [ "%time _ = geopandas.read_file(\"test_london_buildings.shp\", driver='ESRI Shapefile')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 17 s, sys: 234 ms, total: 17.2 s\n", "Wall time: 17.2 s\n" ] } ], "source": [ "%time _ = geopandas.read_file(\"test_london_buildings.gpkg\", driver='GPKG')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.04 s, sys: 120 ms, total: 1.16 s\n", "Wall time: 1.03 s\n" ] } ], "source": [ "%time _ = geopandas.read_parquet(\"test_london_buildings.parquet\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 858 ms, sys: 94.6 ms, total: 952 ms\n", "Wall time: 897 ms\n" ] } ], "source": [ "%time _ = geopandas.read_feather(\"test_london_buildings.feather\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### File sizes" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 joris joris 10 Mai 20 21:20 test_london_buildings.cpg\n", "-rw-r--r-- 1 joris joris 199M Mai 20 21:21 test_london_buildings.dbf\n", "-rw-r--r-- 1 joris joris 65M Mai 20 21:22 test_london_buildings.feather\n", "-rw-r--r-- 1 joris joris 147M Mai 20 21:22 test_london_buildings.gpkg\n", "-rw-r--r-- 1 joris joris 56M Mai 20 21:22 test_london_buildings.parquet\n", "-rw-r--r-- 1 joris joris 145 Mai 20 21:20 test_london_buildings.prj\n", "-rw-r--r-- 1 joris joris 95M Mai 20 21:21 test_london_buildings.shp\n", "-rw-r--r-- 1 joris joris 4,5M Mai 20 21:21 test_london_buildings.shx\n" ] } ], "source": [ "!ls test_london_buildings.* -lh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test case 3: OpenStreetMap points of interest" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import pyrosm" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Download pbf data\n", "fp = pyrosm.get_data(\"London\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Initialize the OSM object\n", "osm = pyrosm.OSM(fp)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "pois = osm.get_pois()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df = pois[[\"id\", \"osm_type\", \"amenity\", \"addr:street\", \"timestamp\", \"geometry\"]].rename(columns={\"id\": \"osm_id\"})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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osm_idosm_typeamenityaddr:streettimestampgeometry
0108042nodepubUniversity StreetNaNPOINT (-0.13551 51.52356)
1108539nodebicycle_rentalNoneNaNPOINT (-0.09339 51.52913)
2109575nodeadviceNoneNaNPOINT (-0.14312 51.52826)
3110075nodebicycle_parkingNoneNaNPOINT (-0.14028 51.53426)
4451152nodepubRegents Park RoadNaNPOINT (-0.19461 51.60084)
.....................
134244260535140001relationschoolBrick Lane0.0MULTIPOLYGON (((-0.07169 51.51895, -0.07171 51...
134245261175302034relationcollegeNone0.0MULTIPOLYGON (((0.00889 51.54038, 0.00842 51.5...
134246261594886517relationschoolNone0.0MULTIPOLYGON (((0.03907 51.51750, 0.04075 51.5...
134247262136116245relationschoolNone0.0MULTIPOLYGON (((-0.03010 51.51048, -0.02994 51...
134248262334979711relationschoolNone0.0MULTIPOLYGON (((-0.02641 51.51923, -0.02532 51...
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134249 rows × 6 columns

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" ], "text/plain": [ " osm_id osm_type amenity addr:street timestamp \\\n", "0 108042 node pub University Street NaN \n", "1 108539 node bicycle_rental None NaN \n", "2 109575 node advice None NaN \n", "3 110075 node bicycle_parking None NaN \n", "4 451152 node pub Regents Park Road NaN \n", "... ... ... ... ... ... \n", "134244 260535140001 relation school Brick Lane 0.0 \n", "134245 261175302034 relation college None 0.0 \n", "134246 261594886517 relation school None 0.0 \n", "134247 262136116245 relation school None 0.0 \n", "134248 262334979711 relation school None 0.0 \n", "\n", " geometry \n", "0 POINT (-0.13551 51.52356) \n", "1 POINT (-0.09339 51.52913) \n", "2 POINT (-0.14312 51.52826) \n", "3 POINT (-0.14028 51.53426) \n", "4 POINT (-0.19461 51.60084) \n", "... ... \n", "134244 MULTIPOLYGON (((-0.07169 51.51895, -0.07171 51... \n", "134245 MULTIPOLYGON (((0.00889 51.54038, 0.00842 51.5... \n", "134246 MULTIPOLYGON (((0.03907 51.51750, 0.04075 51.5... \n", "134247 MULTIPOLYGON (((-0.03010 51.51048, -0.02994 51... \n", "134248 MULTIPOLYGON (((-0.02641 51.51923, -0.02532 51... \n", "\n", "[134249 rows x 6 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Point 86677\n", "Polygon 47308\n", "LineString 176\n", "MultiPolygon 88\n", "dtype: int64" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.geom_type.value_counts()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# Select only points to have uniform geometry type\n", "df = df[df.geom_type == \"Point\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Writing" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.66 s, sys: 240 ms, total: 3.9 s\n", "Wall time: 3.91 s\n" ] } ], "source": [ "%time df.to_file(\"test_london_pois.shp\", driver='ESRI Shapefile')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4.09 s, sys: 79.6 ms, total: 4.17 s\n", "Wall time: 4.36 s\n" ] } ], "source": [ "%time df.to_file(\"test_london_pois.gpkg\", driver=\"GPKG\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 122 ms, sys: 8.4 ms, total: 131 ms\n", "Wall time: 128 ms\n" ] } ], "source": [ "%time df.to_parquet(\"test_london_pois.parquet\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 91.8 ms, sys: 11.8 ms, total: 104 ms\n", "Wall time: 91.6 ms\n" ] } ], "source": [ "%time df.to_feather(\"test_london_pois.feather\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Reading" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.95 s, sys: 43.9 ms, total: 2 s\n", "Wall time: 1.99 s\n" ] } ], "source": [ "%time _ = geopandas.read_file(\"test_london_pois.shp\", driver='ESRI Shapefile')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.82 s, sys: 0 ns, total: 1.82 s\n", "Wall time: 1.82 s\n" ] } ], "source": [ "%time _ = geopandas.read_file(\"test_london_pois.gpkg\", driver='GPKG')" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 104 ms, sys: 23.1 ms, total: 127 ms\n", "Wall time: 107 ms\n" ] } ], "source": [ "%time _ = geopandas.read_parquet(\"test_london_pois.parquet\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 80.2 ms, sys: 8.25 ms, total: 88.4 ms\n", "Wall time: 82.3 ms\n" ] } ], "source": [ "%time _ = geopandas.read_feather(\"test_london_pois.feather\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### File sizes" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 joris joris 10 Mai 20 20:33 test_london_pois.cpg\n", "-rw-r--r-- 1 joris joris 24M Mai 20 20:33 test_london_pois.dbf\n", "-rw-r--r-- 1 joris joris 2,9M Mai 20 20:34 test_london_pois.feather\n", "-rw-r--r-- 1 joris joris 24M Mai 20 20:34 test_london_pois.gpkg\n", "-rw-r--r-- 1 joris joris 2,3M Mai 20 20:34 test_london_pois.parquet\n", "-rw-r--r-- 1 joris joris 145 Mai 20 20:33 test_london_pois.prj\n", "-rw-r--r-- 1 joris joris 2,4M Mai 20 20:33 test_london_pois.shp\n", "-rw-r--r-- 1 joris joris 678K Mai 20 20:33 test_london_pois.shx\n" ] } ], "source": [ "!ls test_london_pois.* -lh" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test case 4: USA Census - TIGER/Line Shapefiles\n", "\n", "The ZIP Code Tabulation Area shapefile: https://www2.census.gov/geo/tiger/TIGER2019/ZCTA5/" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "df = geopandas.read_file(\"zip://../Downloads/tl_2019_us_zcta510.zip\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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