These examples illustrate how to read data from a CSV file using the Pandas and GeoPandas libraries.
from pandas import read_csv
from geopandas import GeoDataFrame, points_from_xy
df = read_csv('https://libs.cartocdn.com/cartoframes/samples/sf_incidents.csv')
gdf = GeoDataFrame(df, geometry=points_from_xy(df['longitude'], df['latitude']))
gdf.head()
incident_datetime | incident_date | incident_time | incident_year | incident_day_of_week | report_datetime | row_id | incident_id | incident_number | cad_number | ... | :@computed_region_qgnn_b9vv | :@computed_region_26cr_cadq | :@computed_region_ajp5_b2md | :@computed_region_nqbw_i6c3 | :@computed_region_2dwj_jsy4 | :@computed_region_h4ep_8xdi | :@computed_region_y6ts_4iup | :@computed_region_jg9y_a9du | :@computed_region_6pnf_4xz7 | geometry | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2020-08-16T03:13:00.000 | 2020-08-16T00:00:00.000 | 03:13 | 2020 | Sunday | 2020-08-16T03:14:00.000 | 95319604083 | 953196 | 200491669 | 202290313.0 | ... | 2.0 | 9 | 26.0 | NaN | NaN | NaN | NaN | NaN | 2.0 | POINT (-122.39773 37.75483) |
1 | 2020-08-16T03:38:00.000 | 2020-08-16T00:00:00.000 | 03:38 | 2020 | Sunday | 2020-08-16T04:56:00.000 | 95326228100 | 953262 | 200491738 | 202290404.0 | ... | 3.0 | 2 | 20.0 | 3.0 | NaN | NaN | NaN | NaN | 2.0 | POINT (-122.42204 37.76654) |
2 | 2020-08-16T13:40:00.000 | 2020-08-16T00:00:00.000 | 13:40 | 2020 | Sunday | 2020-08-16T13:56:00.000 | 95336264020 | 953362 | 200492463 | 202291631.0 | ... | 1.0 | 10 | 8.0 | NaN | NaN | NaN | NaN | NaN | 1.0 | POINT (-122.40371 37.78404) |
3 | 2020-08-16T16:18:00.000 | 2020-08-16T00:00:00.000 | 16:18 | 2020 | Sunday | 2020-08-16T16:18:00.000 | 95335012010 | 953350 | 200492792 | 202292091.0 | ... | 10.0 | 7 | 35.0 | NaN | NaN | NaN | NaN | NaN | 1.0 | POINT (-122.50742 37.75100) |
4 | 2020-08-12T22:00:00.000 | 2020-08-12T00:00:00.000 | 22:00 | 2020 | Wednesday | 2020-08-15T08:30:00.000 | 95300674000 | 953006 | 200489880 | 202280827.0 | ... | 4.0 | 11 | 39.0 | NaN | NaN | NaN | NaN | NaN | 2.0 | POINT (-122.43214 37.78050) |
5 rows × 37 columns
from cartoframes.viz import Layer
Layer(gdf)
from pandas import read_csv
from geopandas import GeoDataFrame
from cartoframes.utils import decode_geometry
df = read_csv('https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.csv')
gdf = GeoDataFrame(df, geometry=decode_geometry(df['the_geom']))
gdf.head()
the_geom | cartodb_id | field_1 | name | address | revenue | id_store | geometry | |
---|---|---|---|---|---|---|---|---|
0 | 0101000020E61000005EA27A6B607D52C01956F146E655... | 1 | 0 | Franklin Ave & Eastern Pkwy | 341 Eastern Pkwy,Brooklyn, NY 11238 | 1321040.772 | A | POINT (-73.95901 40.67109) |
1 | 0101000020E6100000B610E4A0847D52C0B532E197FA49... | 2 | 1 | 607 Brighton Beach Ave | 607 Brighton Beach Avenue,Brooklyn, NY 11235 | 1268080.418 | B | POINT (-73.96122 40.57796) |
2 | 0101000020E6100000E5B8533A587F52C05726FC523F4F... | 3 | 2 | 65th St & 18th Ave | 6423 18th Avenue,Brooklyn, NY 11204 | 1248133.699 | C | POINT (-73.98976 40.61912) |
3 | 0101000020E61000008BA6B393C18152C08D62B9A5D550... | 4 | 3 | Bay Ridge Pkwy & 3rd Ave | 7419 3rd Avenue,Brooklyn, NY 11209 | 1185702.676 | D | POINT (-74.02744 40.63152) |
4 | 0101000020E6100000CEFC6A0E108052C080D4264EEE4B... | 5 | 4 | Caesar's Bay Shopping Center | 8973 Bay Parkway,Brooklyn, NY 11214 | 1148427.411 | E | POINT (-74.00098 40.59321) |
Layer(gdf)