CARTOframes

A Python package for integrating CARTO maps, analysis, and data services into data science workflows.

Data Workflows

Create Dataset from CSV

Create a dataset from a CSV file

Create Dataset from JSON

Create a dataset from a JSON file

Create Dataset from GeoJSON

Create a dataset from a GeoJSON file

Create Dataset from SQL Query

Create a dataset from a SQL query

Download Dataset from CARTO

Download a dataset from CARTO

Upload Dataset to CARTO

Upload a dataset to CARTO

Change Dataset Privacy

Change dataset privacy

Data Workflows

> Create Dataset from CSV

Create a Dataset from a CSV file

This example illustrates how to create a Dataset from a CSV file using pandas

In [1]:
import pandas

remote_file_path = 'http://data.sfgov.org/resource/wg3w-h783.csv'

df = pandas.read_csv(remote_file_path)

# Clean latitude and longitude values that are NaN
df = df[df.longitude == df.longitude]
df = df[df.latitude == df.latitude]

df.head()
Out[1]:
incident_datetime incident_date incident_time incident_year incident_day_of_week report_datetime row_id incident_id incident_number cad_number ... point :@computed_region_6qbp_sg9q :@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
2 2019-10-04T14:25:00.000 2019-10-04T00:00:00.000 14:25 2019 Friday 2019-10-04T16:13:00.000 85442603474 854426 190746203 192772728.0 ... POINT (-122.51129492624534 37.77507596005672) 8.0 8.0 4.0 29.0 NaN NaN NaN NaN NaN
3 2019-10-03T19:30:00.000 2019-10-03T00:00:00.000 19:30 2019 Thursday 2019-10-03T23:25:00.000 85419706244 854197 190744514 192764437.0 ... POINT (-122.42746205880601 37.76877049785351) 28.0 3.0 5.0 5.0 5.0 NaN NaN NaN NaN
4 2019-10-04T16:53:00.000 2019-10-04T00:00:00.000 16:53 2019 Friday 2019-10-04T16:53:00.000 85446351040 854463 190746532 192772932.0 ... POINT (-122.5030864538133 37.781176766186576) 6.0 8.0 4.0 29.0 NaN NaN NaN NaN NaN
5 2019-10-02T14:10:00.000 2019-10-02T00:00:00.000 14:10 2019 Wednesday 2019-10-02T22:59:00.000 85425706224 854257 196208142 NaN ... POINT (-122.410497554147 37.80696290988273) 99.0 6.0 3.0 23.0 NaN NaN NaN NaN NaN
6 2019-10-03T23:30:00.000 2019-10-03T00:00:00.000 23:30 2019 Thursday 2019-10-03T23:31:00.000 85422712030 854227 190744586 192764446.0 ... POINT (-122.417715898404 37.807978726080414) 99.0 6.0 3.0 23.0 NaN NaN NaN NaN NaN

5 rows × 35 columns

In [2]:
from cartoframes.data import Dataset

ds = Dataset(df)
In [3]:
from cartoframes.viz import Map, Layer

Map(Layer(ds))
Out[3]: