To help you get started, we've collected the most common ways that cartoframes is being used within popular public projects.

CartoDB / cartoframes / tests / unit / io / test_carto.py View on Github

def test_read_carto_limit(mocker):
    # Given
    mocker.patch.object(CartoDataFrame, 'set_geometry')
    cm_mock = mocker.patch.object(ContextManager, 'copy_to')

    # When
    read_carto('__source__', CREDENTIALS, limit=1)

    # Then
    cm_mock.assert_called_once_with('__source__', None, 1, 3)

View more ways to use cartoframes

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.

Try it Out

• Stable (1.2.5):
• Latest (develop branch):

If you do not have an API key, you can still use cartoframes for creating maps locally.

> The example context only provides read access, so not all cartoframes > features are available. For full access, Start a free > trial or get free access with a GitHub > Student Developer Pack.

Features

• Create interactive maps from pandas DataFrames (CARTO account not required)
• Publish interactive maps to CARTO's platform
• Write and read pandas DataFrames to/from CARTO tables and queries
• Create customizable, interactive CARTO maps in a Jupyter notebook using DataFrames or hosted data
• Augment your data with CARTO's Data Observatory
• Use CARTO for cloud-based analysis

Common Uses

• Visualize spatial data programmatically as matplotlib images, as notebook-embedded interactive maps, or published map visualizations
• Perform cloud-based spatial data processing using CARTO's analysis tools
• Extract, transform, and Load (ETL) data using the Python ecosystem for getting data into and out of CARTO
• Data Services integrations using CARTO's Location Data Streams

More info

• Complete documentation:

• Source code:

• Bug tracker / feature requests:

  > 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 > . API key access is also given through > GitHub's Student Developer > Pack.