A Python package for integrating CARTO maps, analysis, and data services into data science workflows.
This component is still under support but it will not be further developed. We don’t recommend starting new projects with it as it will eventually become deprecated. Instead, learn more about our new Python packages here.
Play with real examples and learn by doing
Check basic use cases in CARTOframes
Learn to solve real-world use cases in CARTOframes with these examples created by our Spatial Data Scientists
Explore working with data both locally and using a CARTO account
Explore the building blocks for creating web-based, dynamic, and interactive map visualizations
Learn how to combine your data and CARTO's Location Data Services for geospatial analyses
Learn how to discover third party datasets and enrich your data through CARTO's Data Observatory
Geocode a dataset in XLS format
Learn how to use the timeseries widget to visualize temperatures
Download and visualize WiFi Services data in JSON format
Transform and visualize data from a JSON file
> Geocode London stations
> Visualize temperatures
> Paris WiFi services
> Paris remarkable trees
Learn how to build a dashboard to plan a marketing campaign leveraging CARTO's Data Observatory
Learn how to use geosocial and financial data to understand retail performance
Learn how to incorporate CARTOframes data services and enrichment capabilities into your modeling workflows
Learn how to use advanced spatial data science techniques on Data Observatory data to optimize sales territories
Learn how to use advanced spatial data science techniques on Data Observatory data to optimize sales territories
> Building a dashboard to create a marketing plan
> Combining multiple datasets to understand retail performance
> Revenue prediction for site selection
> Single-layer Territory Management
> Two-layer Territory Management
Read data from a GeoJSON file
Read data from a Shapefile
Read data from a CSV file
Read data from a JSON file
Read data from a CARTO table
Read data from a CARTO table using a SQL Query
Upload data to CARTO
Change the privacy of a CARTO table
> Read a GeoJSON file
> Read a Shapefile
> Read a CSV file
> Read a JSON file
> Read a CARTO table
> Read a CARTO SQL query
> Upload to CARTO
> Change CARTO table privacy
Add a Layer
Add multiple Layers
Change the default CARTO basemap
Set a custom viewport
Set a background color
Switch to the dark theme
Use the basic_style method to change the basic style properties
Use the color_category_style method to color features by categorical values
Use the color_continuous_style method to color features by continuous numeric values
Use the color_bins_style method to color features by discrete numeric values
Use the size_category_style method to resize each feature by categorical values
Use the size_continuous_style method to resize each feature by continuous numeric values
Use the size_bins_style method to resize each feature by discrete numeric values
Use the cluster_size_style method to aggregate features by continuous numeric values
Use the animation_style helper method to simply animate a visualization
Combine different visualization styles
Set the default legend needed for the style helper
Use the basic_legend method to display a simple default legend
Use the color_category_legend method to represent categorical values by color
Use the color_bins_legend method to represent discrete numeric values by color
Use the color_continuous_legend method to represent continuous numeric values by color
Use the size_category_legend method to represent categorical values by size
Use the size_bins_legend method to represent discrete numeric values by size
Use the size_bins_legend method to represent continuous numeric values by size
Combine different legends in the same visualization
Set the default widget needed for the style helper
Use the basic_widget method to display a simple default widget
Use the category_widget to create a widget to represent categorical values
Use the histogram_widget to create a widget to represent categorical, numeric and date values in a histogram
Use the formula_widget to display the result of a count, avg, max, min or sum operation in a numeric
Use the animation_widget to be able to play, pause and change an animated visualization through animation controls
Use the time_series_widget to represent a sequence of categorical, numeric and date values in a histogram indexed by a numeric or date value
Combine multiple widgets in the same visualization
Set the default popup needed for the style helper
Display a popup triggered by a click event
Display a popup triggered by a hover event
Change the title and format for the popup values
Display popups triggered by both click and hover events
Create a default visualization layout
Use different settings to create a custom visualization layout
Use different custom titles to create a visualization layout
Use different viewport settings to create a visualization layout
Create a static visualization layout
Publish a visualization from a public table
Publish a visualization from a private table
Publish a visualization from a GeoDataFrame
Publish a visualization of a layout
> Single layer
> Multiple layers
> Basemaps
> Viewport
> Background
> Dark theme
> Basic style
> Color category style
> Color continuous style
> Color bins style
> Size category style
> Size continuous style
> Size bins style
> Cluster size style
> Animation style
> Add multiple style helpers
> Default legend
> Basic legend
> Color category legend
> Color bins legend
> Color continuous legend
> Size category legend
> Size bins legend
> Size continuous legend
> Add multiple legends
> Default widget
> Basic widget
> Category widget
> Histogram widget
> Formula widget
> Animation widget
> Time series widget
> Add multiple widgets
> Default popup
> Popup on click
> Popup on hover
> Customize popup title and format
> Add multiple popup elements
> Default layout
> Custom layout
> Add layout titles
> Customize layout viewport
> Make layout static
> Publish a public table Visualization
> Publish a private table visualization
> Publish a GeoDataFrame visualization
> Publish a layout visualization
Using the Geocoding Services to geocode a DataFrame
Using the Isolines Services to calculate isochrones
Using the Isolines Services to calculate isodistances
Use both Geocoding and Isolines services
> Geocoding
> Isochrones
> Isodistances
> Combining geocoding and isolines
How to explore the Data Observatory catalog to find the data that best fits your needs
Access and manage public data from the Data Observatory
Access and manange premium data from the Data Observatory
Use the enrichment to enrich points for a dataset
Use the enrichment to enrich polygons for a dataset
Incorporate the enrichment in your workflows for advanced use cases
Basic steps to request a premium dataset subscription in the Data Observatory
Get and use your Data Observatory's Google Cloud credentials
Learn how to filter a Data Observatory Dataset using a Data Observatory Geography
> Data Discovery in the Data Observatory
> Access Public Data from the Data Observatory
> Access Premium Data from the Data Observatory
> Point Enrichment
> Basic Polygon Enrichment
> Advanced Polygon Enrichment
> Request premium dataset subscription
> Access Public Data from BigQuery
> Filter a Dataset using a Geography
