CARTOFrames

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: https://cartodb.github.io/cartoframes
• Source code: https://github.com/CartoDB/cartoframes
• bug tracker / feature requests: https://github.com/CartoDB/cartoframes/issues

Note

cartoframes users must have a CARTO API key for most cartoframes functionality. For example, writing DataFrames to an account, reading from private tables, and visualizing data on maps all require an API key. CARTO provides API keys for education and nonprofit uses, among others. Request access at support@carto.com. API key access is also given through GitHub's Student Developer Pack.

Install Instructions

To install cartoframes (currently in beta) on your machine, do the following to install the latest pre-release version:

$ pip install --pre cartoframes

It 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 --pre cartoframes
(venv) $ pip install jupyter
(venv) $ jupyter notebook

Then 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 Turbo Carto and CartoColors. See the CartoColor 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

Note: This is a provisional function, so the signature may change.

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_augment('transactions', data_obs_measures)
df.head()