These examples illustrate how to load data from a CSV file using the Pandas and GeoPandas libraries.
from pandas import read_csv
from geopandas import GeoDataFrame, points_from_xy
remote_file_path = 'http://data.sfgov.org/resource/wg3w-h783.csv'
df = read_csv(remote_file_path)
# Clean rows where the `longitude` column is NULL
df = df[df['longitude'].notna()]
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 | 2019-05-01T01:00:00.000 | 2019-05-01T00:00:00.000 | 01:00 | 2019 | Wednesday | 2019-06-12T20:27:00.000 | 81097515200 | 810975 | 190424067 | 191634131.0 | ... | 10.0 | 7.0 | 35.0 | NaN | NaN | NaN | NaN | NaN | 1.0 | POINT (-122.49963 37.76257) |
| 1 | 2019-06-22T07:45:00.000 | 2019-06-22T00:00:00.000 | 07:45 | 2019 | Saturday | 2019-06-22T08:05:00.000 | 81465564020 | 814655 | 190450880 | 191730737.0 | ... | 1.0 | 10.0 | 34.0 | 1.0 | NaN | 1.0 | NaN | NaN | 2.0 | POINT (-122.40816 37.78054) |
| 2 | 2019-06-03T16:16:00.000 | 2019-06-03T00:00:00.000 | 16:16 | 2019 | Monday | 2019-06-03T16:16:00.000 | 80769875000 | 807698 | 190397016 | 191533509.0 | ... | 2.0 | 9.0 | 1.0 | NaN | NaN | NaN | NaN | NaN | 2.0 | POINT (-122.39075 37.72160) |
| 3 | 2018-11-16T16:34:00.000 | 2018-11-16T00:00:00.000 | 16:34 | 2018 | Friday | 2018-11-16T16:34:00.000 | 73857915041 | 738579 | 180870806 | 183202539.0 | ... | 6.0 | 3.0 | 6.0 | NaN | 18.0 | NaN | NaN | NaN | 2.0 | POINT (-122.40488 37.79486) |
| 4 | 2019-05-27T02:25:00.000 | 2019-05-27T00:00:00.000 | 02:25 | 2019 | Monday | 2019-05-27T02:55:00.000 | 80509204134 | 805092 | 190378555 | 191470256.0 | ... | 4.0 | 6.0 | 13.0 | NaN | NaN | NaN | NaN | NaN | 1.0 | POINT (-122.43056 37.79772) |
5 rows × 37 columns
from cartoframes.viz import Layer
Layer(gdf)
from cartoframes.utils import decode_geometry
remote_file_path='http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.csv'
df = read_csv(remote_file_path)
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)