CARTO Python package for data scientists
Project description
A Python package for integrating CARTO maps, analysis, and data services into data science workflows.
Python data analysis workflows often rely on the de facto standards pandas and Jupyter notebooks. Integrating CARTO into this workflow saves data scientists time and energy by not having to export datasets as files or retain multiple copies of the data. Instead, CARTOframes give the ability to communicate reproducible analysis while providing the ability to gain from CARTO’s services like hosted, dynamic or static maps and Data Observatory augmentation.
Features
Write pandas DataFrames to CARTO tables
Read CARTO tables and queries into pandas DataFrames
Create customizable, interactive CARTO maps in a Jupyter notebook
Interact with CARTO’s Data Observatory
Use CARTO’s spatially-enabled database for analysis
More info
Complete documentation: http://cartoframes.readthedocs.io/en/latest/
Source code: https://github.com/CartoDB/cartoframes
bug tracker / feature requests: https://github.com/CartoDB/cartoframes/issues
Install Instructions
To install cartoframes (currently in beta) on your machine, do the following to install the latest pre-release version:
$ pip install cartoframesIt is recommended to use cartoframes in Jupyter Notebooks (pip install jupyter). See the example usage section below or notebooks in the examples directory for using cartoframes in that environment.
Virtual Environment
To setup cartoframes and Jupyter in a virtual environment:
$ virtualenv venv
$ source venv/bin/activate
(venv) $ pip install cartoframes
(venv) $ pip install jupyter
(venv) $ jupyter notebookThen create a new notebook and try the example code snippets below with tables that are in your CARTO account.
Example usage
Data workflow
Get table from CARTO, make changes in pandas, sync updates with CARTO:
import cartoframes
# `base_url`s are of the form `http://{username}.carto.com/` for most users
cc = cartoframes.CartoContext(base_url='https://eschbacher.carto.com/',
api_key=APIKEY)
# read a table from your CARTO account to a DataFrame
df = cc.read('brooklyn_poverty_census_tracts')
# do fancy pandas operations (add/drop columns, change values, etc.)
df['poverty_per_pop'] = df['poverty_count'] / df['total_population']
# updates CARTO table with all changes from this session
cc.write(df, 'brooklyn_poverty_census_tracts', overwrite=True)Write an existing pandas DataFrame to CARTO.
import pandas as pd
import cartoframes
df = pd.read_csv('acadia_biodiversity.csv')
cc = cartoframes.CartoContext(base_url=BASEURL,
api_key=APIKEY)
cc.write(df, 'acadia_biodiversity')Map workflow
The following will embed a CARTO map in a Jupyter notebook, allowing for custom styling of the maps driven by TurboCARTO and CARTOColors. See the CARTOColors wiki for a full list of available color schemes.
from cartoframes import Layer, BaseMap, styling
cc = cartoframes.CartoContext(base_url=BASEURL,
api_key=APIKEY)
cc.map(layers=[BaseMap('light'),
Layer('acadia_biodiversity',
color={'column': 'simpson_index',
'scheme': styling.tealRose(5)}),
Layer('peregrine_falcon_nest_sites',
size='num_eggs',
color={'column': 'bird_id',
'scheme': styling.vivid(10)})],
interactive=True)Augment from Data Observatory
Interact with CARTO’s Data Observatory:
import cartoframes
cc = cartoframes.CartoContext(BASEURL, APIKEY)
# total pop, high school diploma (normalized), median income, poverty status (normalized)
# See Data Observatory catalog for codes: https://cartodb.github.io/bigmetadata/index.html
data_obs_measures = [{'numer_id': 'us.census.acs.B01003001'},
{'numer_id': 'us.census.acs.B15003017',
'normalization': 'predenominated'},
{'numer_id': 'us.census.acs.B19013001'},
{'numer_id': 'us.census.acs.B17001002',
'normalization': 'predenominated'},]
df = cc.data('transactions', data_obs_measures)CARTO Credential Management
Save and update your CARTO credentials for later use.
from cartoframes import Credentials, CartoContext
creds = Credentials(username='eschbacher', key='abcdefg')
creds.save() # save credentials for later use (not dependent on Python session)Once you save your credentials, you can get started in future sessions more quickly:
from cartoframes import CartoContext
cc = CartoContext() # automatically loads credentials if previously savedRelease history Release notifications | RSS feed
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