cartoframes package

Module contents

class cartoframes.CartoContext(base_url=None, api_key=None, creds=None, session=None, verbose=0)

Bases: object

CartoContext class for authentication with CARTO and high-level operations such as reading tables from CARTO into dataframes, writing dataframes to CARTO tables, creating custom maps from dataframes and CARTO tables, and augmenting data using CARTO’s Data Observatory. Future methods will interact with CARTO’s services like routing, geocoding, and isolines, PostGIS backend for spatial processing, and much more.

Manages connections with CARTO for data and map operations. Modeled after SparkContext.

There are two ways of authenticating against a CARTO account:

  1. Setting the base_url and api_key directly in CartoContext. This method is easier.:

    cc = CartoContext(
        base_url='https://eschbacher.carto.com',
        api_key='abcdefg')
    
  2. By passing a Credentials instance in CartoContext’s creds keyword argument. This method is more flexible.:

    from cartoframes import Credentials
    creds = Credentials(username='eschbacher', key='abcdefg')
    cc = CartoContext(creds=creds)
    
creds

Credentials instance

Type:Credentials
Parameters:
  • base_url (str) – Base URL of CARTO user account. Cloud-based accounts should use the form https://{username}.carto.com (e.g., https://eschbacher.carto.com for user eschbacher) whether on a personal or multi-user account. On-premises installation users should ask their admin.
  • api_key (str) – CARTO API key.
  • creds (Credentials) – A Credentials instance can be used in place of a base_url/api_key combination.
  • session (requests.Session, optional) – requests session. See requests documentation for more information.
  • verbose (bool, optional) – Output underlying process states (True), or suppress (False, default)
Returns:

A CartoContext object that is authenticated against the user’s CARTO account.

Return type:

CartoContext

Example

Create a CartoContext object for a cloud-based CARTO account.

import cartoframes
# if on prem, format is '{host}/user/{username}'
BASEURL = 'https://{}.carto.com/'.format('your carto username')
APIKEY = 'your carto api key'
cc = cartoframes.CartoContext(BASEURL, APIKEY)

Tip

If using cartoframes with an on premises CARTO installation, sometimes it is necessary to disable SSL verification depending on your system configuration. You can do this using a requests Session object as follows:

import cartoframes
from requests import Session
session = Session()
session.verify = False

# on prem host (e.g., an IP address)
onprem_host = 'your on prem carto host'

cc = cartoframes.CartoContext(
    base_url='{host}/user/{user}'.format(
        host=onprem_host,
        user='your carto username'),
    api_key='your carto api key',
    session=session
)
data(table_name, metadata, persist_as=None, how='the_geom')

Get an augmented CARTO dataset with Data Observatory measures. Use CartoContext.data_discovery to search for available measures, or see the full Data Observatory catalog. Optionally persist the data as a new table.

Example

Get a DataFrame with Data Observatory measures based on the geometries in a CARTO table.

cc = cartoframes.CartoContext(BASEURL, APIKEY)
median_income = cc.data_discovery('transaction_events',
                                  regex='.*median income.*',
                                  time='2011 - 2015')
df = cc.data('transaction_events',
             median_income)

Pass in cherry-picked measures from the Data Observatory catalog. The rest of the metadata will be filled in, but it’s important to specify the geographic level as this will not show up in the column name.

median_income = [{'numer_id': 'us.census.acs.B19013001',
                  'geom_id': 'us.census.tiger.block_group',
                  'numer_timespan': '2011 - 2015'}]
df = cc.data('transaction_events', median_income)
Parameters:
  • table_name (str) – Name of table on CARTO account that Data Observatory measures are to be added to.
  • metadata (pandas.DataFrame) – List of all measures to add to table_name. See CartoContext.data_discovery outputs for a full list of metadata columns.
  • persist_as (str, optional) – Output the results of augmenting table_name to persist_as as a persistent table on CARTO. Defaults to None, which will not create a table.
  • how (str, optional) – Not fully implemented. Column name for identifying the geometry from which to fetch the data. Defaults to the_geom, which results in measures that are spatially interpolated (e.g., a neighborhood boundary’s population will be calculated from underlying census tracts). Specifying a column that has the geometry identifier (for example, GEOID for US Census boundaries), results in measures directly from the Census for that GEOID but normalized how it is specified in the metadata.
Returns:

A DataFrame representation of table_name which has new columns for each measure in metadata.

Return type:

pandas.DataFrame

Raises:
  • NameError – If the columns in table_name are in the suggested_name column of metadata.
  • ValueError – If metadata object is invalid or empty, or if the number of requested measures exceeds 50.
  • CartoException – If user account consumes all of Data Observatory quota
data_boundaries(boundary=None, region=None, decode_geom=False, timespan=None, include_nonclipped=False)

Find all boundaries available for the world or a region. If boundary is specified, get all available boundary polygons for the region specified (if any). This method is espeically useful for getting boundaries for a region and, with CartoContext.data and CartoContext.data_discovery, getting tables of geometries and the corresponding raw measures. For example, if you want to analyze how median income has changed in a region (see examples section for more).

Examples

Find all boundaries available for Australia. The columns geom_name gives us the name of the boundary and geom_id is what we need for the boundary argument.

import cartoframes
cc = cartoframes.CartoContext('base url', 'api key')
au_boundaries = cc.data_boundaries(region='Australia')
au_boundaries[['geom_name', 'geom_id']]

Get the boundaries for Australian Postal Areas and map them.

from cartoframes import Layer
au_postal_areas = cc.data_boundaries(boundary='au.geo.POA')
cc.write(au_postal_areas, 'au_postal_areas')
cc.map(Layer('au_postal_areas'))

Get census tracts around Idaho Falls, Idaho, USA, and add median income from the US census. Without limiting the metadata, we get median income measures for each census in the Data Observatory.

cc = cartoframes.CartoContext('base url', 'api key')
# will return DataFrame with columns `the_geom` and `geom_ref`
tracts = cc.data_boundaries(
    boundary='us.census.tiger.census_tract',
    region=[-112.096642,43.429932,-111.974213,43.553539])
# write geometries to a CARTO table
cc.write(tracts, 'idaho_falls_tracts')
# gather metadata needed to look up median income
median_income_meta = cc.data_discovery(
    'idaho_falls_tracts',
    keywords='median income',
    boundaries='us.census.tiger.census_tract')
# get median income data and original table as new dataframe
idaho_falls_income = cc.data(
    'idaho_falls_tracts',
    median_income_meta,
    how='geom_refs')
# overwrite existing table with newly-enriched dataframe
cc.write(idaho_falls_income,
         'idaho_falls_tracts',
         overwrite=True)
Parameters:
  • boundary (str, optional) – Boundary identifier for the boundaries that are of interest. For example, US census tracts have a boundary ID of us.census.tiger.census_tract, and Brazilian Municipios have an ID of br.geo.municipios. Find IDs by running CartoContext.data_boundaries without any arguments, or by looking in the Data Observatory catalog.
  • region (str, optional) –

    Region where boundary information or, if boundary is specified, boundary polygons are of interest. region can be one of the following:

    • table name (str): Name of a table in user’s CARTO account
    • bounding box (list of float): List of four values (two lng/lat pairs) in the following order: western longitude, southern latitude, eastern longitude, and northern latitude. For example, Switzerland fits in [5.9559111595,45.8179931641,10.4920501709,47.808380127]
  • timespan (str, optional) – Specific timespan to get geometries from. Defaults to use the most recent. See the Data Observatory catalog for more information.
  • decode_geom (bool, optional) – Whether to return the geometries as Shapely objects or keep them encoded as EWKB strings. Defaults to False.
  • include_nonclipped (bool, optional) – Optionally include non-shoreline-clipped boundaries. These boundaries are the raw boundaries provided by, for example, US Census Tiger.
Returns:

If boundary is specified, then all available boundaries and accompanying geom_refs in region (or the world if region is None or not specified) are returned. If boundary is not specified, then a DataFrame of all available boundaries in region (or the world if region is None)

Return type:

pandas.DataFrame

data_discovery(region, keywords=None, regex=None, time=None, boundaries=None, include_quantiles=False)

Discover Data Observatory measures. This method returns the full Data Observatory metadata model for each measure or measures that match the conditions from the inputs. The full metadata in each row uniquely defines a measure based on the timespan, geographic resolution, and normalization (if any). Read more about the metadata response in Data Observatory documentation.

Internally, this method finds all measures in region that match the conditions set in keywords, regex, time, and boundaries (if any of them are specified). Then, if boundaries is not specified, a geographical resolution for that measure will be chosen subject to the type of region specified:

  1. If region is a table name, then a geographical resolution that is roughly equal to region size / number of subunits.
  2. If region is a country name or bounding box, then a geographical resolution will be chosen roughly equal to region size / 500.

Since different measures are in some geographic resolutions and not others, different geographical resolutions for different measures are oftentimes returned.

Tip

To remove the guesswork in how geographical resolutions are selected, specify one or more boundaries in boundaries. See the boundaries section for each region in the Data Observatory catalog.

The metadata returned from this method can then be used to create raw tables or for augmenting an existing table from these measures using CartoContext.data. For the full Data Observatory catalog, visit https://cartodb.github.io/bigmetadata/. When working with the metadata DataFrame returned from this method, be careful to only remove rows not columns as CartoContext.data <cartoframes.context.CartoContext.data> generally needs the full metadata.

Note

Narrowing down a discovery query using the keywords, regex, and time filters is important for getting a manageable metadata set. Besides there being a large number of measures in the DO, a metadata response has acceptable combinations of measures with demonimators (normalization and density), and the same measure from other years.

For example, setting the region to be United States counties with no filter values set will result in many thousands of measures.

Examples

Get all European Union measures that mention freight.

meta = cc.data_discovery('European Union',
                         keywords='freight',
                         time='2010')
print(meta['numer_name'].values)
Parameters:
  • region (str or list of float) –

    Information about the region of interest. region can be one of three types:

    • region name (str): Name of region of interest. Acceptable values are limited to: ‘Australia’, ‘Brazil’, ‘Canada’, ‘European Union’, ‘France’, ‘Mexico’, ‘Spain’, ‘United Kingdom’, ‘United States’.
    • table name (str): Name of a table in user’s CARTO account with geometries. The region will be the bounding box of the table.

      Note

      If a table name is also a valid Data Observatory region name, the Data Observatory name will be chosen over the table.

    • bounding box (list of float): List of four values (two lng/lat pairs) in the following order: western longitude, southern latitude, eastern longitude, and northern latitude. For example, Switzerland fits in [5.9559111595,45.8179931641,10.4920501709,47.808380127]

    Note

    Geometry levels are generally chosen by subdividing the region into the next smallest administrative unit. To override this behavior, specify the boundaries flag. For example, set boundaries to 'us.census.tiger.census_tract' to choose US census tracts.

  • keywords (str or list of str, optional) – Keyword or list of keywords in measure description or name. Response will be matched on all keywords listed (boolean or).
  • regex (str, optional) – A regular expression to search the measure descriptions and names. Note that this relies on PostgreSQL’s case insensitive operator ~*. See PostgreSQL docs for more information.
  • boundaries (str or list of str, optional) – Boundary or list of boundaries that specify the measure resolution. See the boundaries section for each region in the Data Observatory catalog.
  • include_quantiles (bool, optional) – Include quantiles calculations which are a calculation of how a measure compares to all measures in the full dataset. Defaults to False. If True, quantiles columns will be returned for each column which has it pre-calculated.
Returns:

A dataframe of the complete metadata model for specific measures based on the search parameters.

Return type:

pandas.DataFrame

Raises:
  • ValueError – If region is a list and does not consist of four elements, or if region is not an acceptable region
  • CartoException – If region is not a table in user account
delete(table_name)

Delete a table in user’s CARTO account.

Parameters:table_name (str) – Name of table to delete
Returns:True if table is removed
Return type:bool
execute(query)

Runs an arbitrary query to a CARTO account.

This method is specially useful for queries that do not return any data and just perform a database operation like:

  • INSERT, UPDATE, DROP, CREATE, ALTER, stored procedures, etc.

Queries are run using a Batch SQL API job in the user account

The execution of the queries is asynchronous but this method automatically waits for its completion (or failure). The job_id of the Batch SQL API job will be printed. In case there’s any issue you can contact the CARTO support team specifying that job_id.

Parameters:query (str) – An SQL query to run against CARTO user database.
Returns:None
Raises:CartoException – If the query fails to execute

Examples

Drops my_table

cc.execute(
    '''
      DROP TABLE my_table
    '''
)

Updates the column my_column in the table my_table

cc.query(
    '''
      UPDATE my_table SET my_column = 1
    '''
)
fetch(query, decode_geom=False)

Pull the result from an arbitrary SELECT SQL query from a CARTO account into a pandas DataFrame.

Parameters:
  • query (str) – SELECT query to run against CARTO user database. This data will then be converted into a pandas DataFrame.
  • decode_geom (bool, optional) – Decodes CARTO’s geometries into a Shapely object that can be used, for example, in GeoPandas.
Returns:

DataFrame representation of query supplied. Pandas data types are inferred from PostgreSQL data types. In the case of PostgreSQL date types, dates are attempted to be converted, but on failure a data type ‘object’ is used.

Return type:

pandas.DataFrame

Examples

This query gets the 10 highest values from a table and returns a dataframe.

topten_df = cc.query(
    '''
      SELECT * FROM
      my_table
      ORDER BY value_column DESC
      LIMIT 10
    '''
)

This query joins points to polygons based on intersection, and aggregates by summing the values of the points in each polygon. The query returns a dataframe, with a geometry column that contains polygons.

points_aggregated_to_polygons = cc.query(
    '''
      SELECT polygons.*, sum(points.values)
      FROM polygons JOIN points
      ON ST_Intersects(points.the_geom, polygons.the_geom)
      GROUP BY polygons.the_geom, polygons.cartodb_id
    ''',
    decode_geom=True
)
get_default_schema()
map(**kwargs)

Produce a CARTO map visualizing data layers.

Examples

Create a map with two data Layers, and one BaseMap layer:

import cartoframes
from cartoframes import Layer, BaseMap, styling
cc = cartoframes.CartoContext(BASEURL, APIKEY)
cc.map(layers=[BaseMap(),
               Layer('acadia_biodiversity',
                     color={'column': 'simpson_index',
                            'scheme': styling.tealRose(7)}),
               Layer('peregrine_falcon_nest_sites',
                     size='num_eggs',
                     color={'column': 'bird_id',
                            'scheme': styling.vivid(10))],
       interactive=True)

Create a snapshot of a map at a specific zoom and center:

cc.map(layers=Layer('acadia_biodiversity',
                    color='simpson_index'),
       interactive=False,
       zoom=14,
       lng=-68.3823549,
       lat=44.3036906)
Parameters:
  • layers (list, optional) –

    List of zero or more of the following:

    • Layer: cartoframes Layer object for visualizing data from a CARTO table. See Layer for all styling options.
    • BaseMap: Basemap for contextualizng data layers. See BaseMap for all styling options.
    • QueryLayer: Layer from an arbitrary query. See QueryLayer for all styling options.
  • interactive (bool, optional) – Defaults to True to show an interactive slippy map. Setting to False creates a static map.
  • zoom (int, optional) – Zoom level of map. Acceptable values are usually in the range 0 to 19. 0 has the entire earth on a single tile (256px square). Zoom 19 is the size of a city block. Must be used in conjunction with lng and lat. Defaults to a view to have all data layers in view.
  • lat (float, optional) – Latitude value for the center of the map. Must be used in conjunction with zoom and lng. Defaults to a view to have all data layers in view.
  • lng (float, optional) – Longitude value for the center of the map. Must be used in conjunction with zoom and lat. Defaults to a view to have all data layers in view.
  • size (tuple, optional) – List of pixel dimensions for the map. Format is (width, height). Defaults to (800, 400).
  • ax – matplotlib axis on which to draw the image. Only used when interactive is False.
Returns:

Interactive maps are rendered as HTML in an iframe, while static maps are returned as matplotlib Axes objects or IPython Image.

Return type:

IPython.display.HTML or matplotlib Axes

query(query, table_name=None, decode_geom=False, is_select=None)

Pull the result from an arbitrary SQL SELECT query from a CARTO account into a pandas DataFrame. This is the default behavior, when is_select=True

Can also be used to perform database operations (creating/dropping tables, adding columns, updates, etc.). In this case, you have to explicitly specify is_select=False

This method is a helper for the CartoContext.fetch and CartoContext.execute methods. We strongly encourage you to use any of those methods depending on the type of query you want to run. If you want to get the results of a SELECT query into a pandas DataFrame, then use CartoContext.fetch. For any other query that performs an operation into the CARTO database, use CartoContext.execute

Parameters:
  • query (str) – Query to run against CARTO user database. This data will then be converted into a pandas DataFrame.
  • table_name (str, optional) – If set (and is_select=True), this will create a new table in the user’s CARTO account that is the result of the SELECT query provided. Defaults to None (no table created).
  • decode_geom (bool, optional) – Decodes CARTO’s geometries into a Shapely object that can be used, for example, in GeoPandas. It only works for SELECT queries when is_select=True
  • is_select (bool, optional) – This argument has to be set depending on the query performed. True for SELECT queries, False for any other query. For the case of a SELECT SQL query (is_select=True) the result will be stored into a pandas DataFrame. When an arbitrary SQL query (is_select=False) it will perform a database operation (UPDATE, DROP, INSERT, etc.) By default is_select=None that means that the method will return a dataframe if the query starts with a select clause, otherwise it will just execute the query and return None
Returns:

When is_select=True and the query is actually a SELECT query this method returns a pandas DataFrame representation of query supplied otherwise returns None. Pandas data types are inferred from PostgreSQL data types. In the case of PostgreSQL date types, dates are attempted to be converted, but on failure a data type ‘object’ is used.

Return type:

pandas.DataFrame

Raises:

CartoException – If there’s any error when executing the query

Examples

Query a table in CARTO and write a new table that is result of query. This query gets the 10 highest values from a table and returns a dataframe, as well as creating a new table called ‘top_ten’ in the CARTO account.

topten_df = cc.query(
    '''
      SELECT * FROM
      my_table
      ORDER BY value_column DESC
      LIMIT 10
    ''',
    table_name='top_ten'
)

This query joins points to polygons based on intersection, and aggregates by summing the values of the points in each polygon. The query returns a dataframe, with a geometry column that contains polygons and also creates a new table called ‘points_aggregated_to_polygons’ in the CARTO account.

points_aggregated_to_polygons = cc.query(
    '''
      SELECT polygons.*, sum(points.values)
      FROM polygons JOIN points
      ON ST_Intersects(points.the_geom, polygons.the_geom)
      GROUP BY polygons.the_geom, polygons.cartodb_id
    ''',
    table_name='points_aggregated_to_polygons',
    decode_geom=True
)

Drops my_table

cc.query(
    '''
      DROP TABLE my_table
    '''
)

Updates the column my_column in the table my_table

cc.query(
    '''
      UPDATE my_table SET my_column = 1
    '''
)
read(table_name, limit=None, decode_geom=False, shared_user=None, retry_times=3)
Read a table from CARTO into a pandas DataFrames. Column types are inferred from database types, to
avoid problems with integer columns with NA or null values, they are automatically retrieved as float64
Parameters:
  • table_name (str) – Name of table in user’s CARTO account.
  • limit (int, optional) – Read only limit lines from table_name. Defaults to None, which reads the full table.
  • decode_geom (bool, optional) – Decodes CARTO’s geometries into a Shapely object that can be used, for example, in GeoPandas.
  • shared_user (str, optional) – If a table has been shared with you, specify the user name (schema) who shared it.
  • retry_times (int, optional) – If the read call is rate limited, number of retries to be made
Returns:

DataFrame representation of table_name from CARTO.

Return type:

pandas.DataFrame

Example

import cartoframes
cc = cartoframes.CartoContext(BASEURL, APIKEY)
df = cc.read('acadia_biodiversity')
sync(dataframe, table_name)

Depending on the size of the DataFrame or CARTO table, perform granular operations on a DataFrame to only update the changed cells instead of a bulk upload. If on the large side, perform granular operations, if on the smaller side use Import API.

Note

Not yet implemented.

tables(**kwargs)

List all tables in user’s CARTO account

Returns:list of Table
write(df, table_name, temp_dir='/home/docs/.cache/cartoframes', overwrite=False, lnglat=None, encode_geom=False, geom_col=None, **kwargs)

Write a DataFrame to a CARTO table.

Examples

Write a pandas DataFrame to CARTO.

cc.write(df, 'brooklyn_poverty', overwrite=True)

Scrape an HTML table from Wikipedia and send to CARTO with content guessing to create a geometry from the country column. This uses a CARTO Import API param content_guessing parameter.

url = 'https://en.wikipedia.org/wiki/List_of_countries_by_life_expectancy'
# retrieve first HTML table from that page
df = pd.read_html(url, header=0)[0]
# send to carto, let it guess polygons based on the 'country'
#   column. Also set privacy to 'public'
cc.write(df, 'life_expectancy',
         content_guessing=True,
         privacy='public')
cc.map(layers=Layer('life_expectancy',
                    color='both_sexes_life_expectancy'))

Warning

datetime64[ns] column will lose precision sending a dataframe to CARTO because postgresql has millisecond resolution while pandas does nanoseconds

Parameters:
  • df (pandas.DataFrame) – DataFrame to write to table_name in user CARTO account
  • table_name (str) – Table to write df to in CARTO.
  • temp_dir (str, optional) – Directory for temporary storage of data that is sent to CARTO. Defaults are defined by appdirs.
  • overwrite (bool, optional) – Behavior for overwriting table_name if it exits on CARTO. Defaults to False.
  • lnglat (tuple, optional) – lng/lat pair that can be used for creating a geometry on CARTO. Defaults to None. In some cases, geometry will be created without specifying this. See CARTO’s Import API for more information.
  • encode_geom (bool, optional) – Whether to write geom_col to CARTO as the_geom.
  • geom_col (str, optional) – The name of the column where geometry information is stored. Used in conjunction with encode_geom.
  • **kwargs

    Keyword arguments to control write operations. Options are:

    • compression to set compression for files sent to CARTO. This will cause write speedups depending on the dataset. Options are None (no compression, default) or gzip.
    • Some arguments from CARTO’s Import API. See the params listed in the documentation for more information. For example, when using content_guessing=’true’, a column named ‘countries’ with country names will be used to generate polygons for each country. Another use is setting the privacy of a dataset. To avoid unintended consequences, avoid file, url, and other similar arguments.
Returns:

Dataset

Note

DataFrame indexes are changed to ordinary columns. CARTO creates an index called cartodb_id for every table that runs from 1 to the length of the DataFrame.

class cartoframes.Credentials(creds=None, key=None, username=None, base_url=None, cred_file=None)

Bases: object

Credentials class for managing and storing user CARTO credentials. The arguments are listed in order of precedence: Credentials instances are first, key and base_url/username are taken next, and config_file (if given) is taken last. If no arguments are passed, then there will be an attempt to retrieve credentials from a previously saved session. One of the above scenarios needs to be met to successfully instantiate a Credentials object.

Parameters:
  • creds (cartoframes.Credentials, optional) – Credentials instance
  • key (str, optional) – API key of user’s CARTO account
  • username (str, optional) – Username of CARTO account
  • base_url (str, optional) – Base URL used for API calls. This is usually of the form https://eschbacher.carto.com/ for user eschbacher. On premises installations (and others) have a different URL pattern.
  • cred_file (str, optional) – Pull credentials from a stored file. If this and all other args are not entered, Credentials will attempt to load a user config credentials file that was previously set with Credentials(…).save().
Raises:

RuntimeError – If not enough credential information is passed and no stored credentials file is found, this error will be raised.

Example

from cartoframes import Credentials, CartoContext
creds = Credentials(key='abcdefg', username='eschbacher')
cc = CartoContext(creds=creds)
base_url(base_url=None)

Return or set base_url.

Parameters:base_url (str, optional) – If set, updates the base_url. Otherwise returns current base_url.

Note

This does not update the username attribute. Separately update the username with Credentials.username or update base_url and username at the same time with Credentials.set.

Example

>>> from cartoframes import Credentials
# load credentials saved in previous session
>>> creds = Credentials()
# returns current base_url
>>> creds.base_url()
'https://eschbacher.carto.com/'
# updates base_url with new value
>>> creds.base_url('new_base_url')
delete(config_file=None)

Deletes the credentials file specified in config_file. If no file is specified, it deletes the default user credential file.

Parameters:config_file (str) – Path to configuration file. Defaults to delete the user default location if None.

Tip

To see if there is a default user credential file stored, do the following:

>>> creds = Credentials()
>>> print(creds)
Credentials(username=eschbacher, key=abcdefg,
        base_url=https://eschbacher.carto.com/)
key(key=None)

Return or set API key.

Parameters:key (str, optional) – If set, updates the API key, otherwise returns current API key.

Example

>>> from cartoframes import Credentials
# load credentials saved in previous session
>>> creds = Credentials()
# returns current API key
>>> creds.key()
'abcdefg'
# updates API key with new value
>>> creds.key('new_api_key')
save(config_loc=None)

Saves current user credentials to user directory.

Parameters:config_loc (str, optional) – Location where credentials are to be stored. If no argument is provided, it will be send to the default location.

Example

from cartoframes import Credentials
creds = Credentials(username='eschbacher', key='abcdefg')
creds.save()  # save to default location
set(key=None, username=None, base_url=None)

Update the credentials of a Credentials instance instead with new values.

Parameters:
  • key (str) – API key of user account. Defaults to previous value if not specified.
  • username (str) – User name of account. This parameter is optional if base_url is not specified, but defaults to the previous value if not set.
  • base_url (str) – Base URL of user account. This parameter is optional if username is specified and on CARTO’s cloud-based account. Generally of the form https://your_user_name.carto.com/ for cloud-based accounts. If on-prem or otherwise, contact your admin.

Example

from cartoframes import Credentials
# load credentials saved in previous session
creds = Credentials()
# set new API key
creds.set(key='new_api_key')
# save new creds to default user config directory
creds.save()

Note

If the username is specified but the base_url is not, the base_url will be updated to https://<username>.carto.com/.

username(username=None)

Return or set username.

Parameters:username (str, optional) – If set, updates the username. Otherwise returns current username.

Note

This does not update the base_url attribute. Use Credentials.set to have that updated with username.

Example

>>> from cartoframes import Credentials
# load credentials saved in previous session
>>> creds = Credentials()
# returns current username
>>> creds.username()
'eschbacher'
# updates username with new value
>>> creds.username('new_username')
class cartoframes.BaseMap(source='voyager', labels='back', only_labels=False)

Bases: cartoframes.layer.AbstractLayer

Layer object for adding basemaps to a cartoframes map.

Example

Add a custom basemap to a cartoframes map.

import cartoframes
from cartoframes import BaseMap, Layer
cc = cartoframes.CartoContext(BASEURL, APIKEY)
cc.map(layers=[BaseMap(source='light', labels='front'),
               Layer('acadia_biodiversity')])
Parameters:
  • source (str, optional) – One of light or dark. Defaults to voyager. Basemaps come from https://carto.com/location-data-services/basemaps/
  • labels (str, optional) – One of back, front, or None. Labels on the front will be above the data layers. Labels on back will be underneath the data layers but on top of the basemap. Setting labels to None will only show the basemap.
  • only_labels (bool, optional) – Whether to show labels or not.
is_basemap = True
is_basic()

Does BaseMap pull from CARTO default basemaps?

Returns:True if using a CARTO basemap (Dark Matter, Positron or Voyager), False otherwise.
Return type:bool
class cartoframes.QueryLayer(query, time=None, color=None, size=None, opacity=None, tooltip=None, legend=None)

Bases: cartoframes.layer.AbstractLayer

cartoframes data layer based on an arbitrary query to the user’s CARTO database. This layer class is useful for offloading processing to the cloud to do some of the following:

  • Visualizing spatial operations using PostGIS and PostgreSQL, which is the database underlying CARTO
  • Performing arbitrary relational database queries (e.g., complex JOINs in SQL instead of in pandas)
  • Visualizing a subset of the data (e.g., SELECT * FROM table LIMIT 1000)

Used in the layers keyword in CartoContext.map.

Example

Underlay a QueryLayer with a complex query below a layer from a table. The QueryLayer is colored by the calculated column abs_diff, and points are sized by the column i_measure.

import cartoframes
from cartoframes import QueryLayer, styling
cc = cartoframes.CartoContext(BASEURL, APIKEY)
cc.map(layers=[QueryLayer('''
                          WITH i_cte As (
                            SELECT
                                ST_Buffer(the_geom::geography, 500)::geometry As the_geom,
                                cartodb_id,
                                measure,
                                date
                              FROM interesting_data
                             WHERE date > '2017-04-19'
                          )
                          SELECT
                             i.cartodb_id, i.the_geom,
                             ST_Transform(i.the_geom, 3857) AS the_geom_webmercator,
                             abs(i.measure - j.measure) AS abs_diff,
                             i.measure AS i_measure
                            FROM i_cte AS i
                            JOIN awesome_data AS j
                              ON i.event_id = j.event_id
                           WHERE j.measure IS NOT NULL
                             AND j.date < '2017-04-29'
                          ''',
                          color={'column': 'abs_diff',
                                 'scheme': styling.sunsetDark(7)},
                          size='i_measure'),
               Layer('fantastic_sql_table')])
Parameters:
  • query (str) – Query to expose data on a map layer. At a minimum, a query needs to have the columns cartodb_id, the_geom, and the_geom_webmercator for the map to display. Read more about queries in CARTO’s docs.
  • time (dict or str, optional) –

    Time-based style to apply to layer.

    If time is a str, it must be the name of a column which has a data type of datetime or float.

    from cartoframes import QueryLayer
    l = QueryLayer('SELECT * FROM acadia_biodiversity',
                   time='bird_sighting_time')
    

    If time is a dict, the following keys are options:

    • column (str, required): Column for animating map, which must be of type datetime or float.
    • method (str, optional): Type of aggregation method for operating on Torque TileCubes. Must be one of avg, sum, or another PostgreSQL aggregate functions with a numeric output. Defaults to count.
    • cumulative (bool, optional): Whether to accumulate points over time (True) or not (False, default)
    • frames (int, optional): Number of frames in the animation. Defaults to 256.
    • duration (int, optional): Number of seconds in the animation. Defaults to 30.
    • trails (int, optional): Number of trails after the incidence of a point. Defaults to 2.
    from cartoframes import Layer
    l = Layer('acadia_biodiversity',
              time={
                  'column': 'bird_sighting_time',
                  'cumulative': True,
                  'frames': 128,
                  'duration': 15
              })
    
  • color (dict or str, optional) –

    Color style to apply to map. For example, this can be used to change the color of all geometries in this layer, or to create a graduated color or choropleth map.

    If color is a str, there are two options:

    • A column name to style by to create, for example, a choropleth map if working with polygons. The default classification is quantiles for quantitative data and category for qualitative data.
    • A hex value or web color name.
    # color all geometries red (#F00)
    from cartoframes import Layer
    l = Layer('acadia_biodiversity',
              color='red')
    
    # color on 'num_eggs' (using defalt color scheme and quantification)
    l = Layer('acadia_biodiversity',
              color='num_eggs')
    

    If color is a dict, the following keys are options, with values described:

    • column (str): Column used for the basis of styling
    • scheme (dict, optional): Scheme such as styling.sunset(7) from the styling module of cartoframes that exposes CARTOColors. Defaults to mint scheme for quantitative data and bold for qualitative data. More control is given by using styling.scheme.

      If you wish to define a custom scheme outside of CARTOColors, it is recommended to use the styling.custom utility function.

    from cartoframes import QueryLayer, styling
    l = QueryLayer('SELECT * FROM acadia_biodiversity',
                   color={
                       'column': 'simpson_index',
                       'scheme': styling.mint(7, bin_method='equal')
                   })
    
  • size (dict or int, optional) –

    Size style to apply to point data.

    If size is an int, all points are sized by this value.

    from cartoframes import QueryLayer
    l = QueryLayer('SELECT * FROM acadia_biodiversity',
                   size=7)
    

    If size is a str, this value is interpreted as a column, and the points are sized by the value in this column. The classification method defaults to quantiles, with a min size of 5, and a max size of 5. Use the dict input to override these values.

    from cartoframes import Layer
    l = Layer('acadia_biodiversity',
              size='num_eggs')
    

    If size is a dict, the follow keys are options, with values described as:

    • column (str): Column to base sizing of points on
    • bin_method (str, optional): Quantification method for dividing data range into bins. Must be one of the methods in BinMethod (excluding category).
    • bins (int, optional): Number of bins to break data into. Defaults to 5.
    • max (int, optional): Maximum point width (in pixels). Setting this overrides range. Defaults to 25.
    • min (int, optional): Minimum point width (in pixels). Setting this overrides range. Defaults to 5.
    • range (tuple or list, optional): a min/max pair. Defaults to [1, 5] for lines and [5, 25] for points.
    from cartoframes import Layer
    l = Layer('acadia_biodiversity',
              size={
                  'column': 'num_eggs',
                  'max': 10,
                  'min': 2
              })
    
  • opacity (float, optional) – Opacity of layer from 0 to 1. Defaults to 0.9.
  • tooltip (tuple, optional) – Not yet implemented.
  • legendNot yet implemented.
Raises:
  • CartoException – If a column name used in any of the styling options is not in the data source in query (or table if using Layer).
  • ValueError – If styling using a dict and a column key is not present, or if the data type for a styling option is not supported. This is also raised if styling by a geometry column (i.e., the_geom or the_geom_webmercator). Futher, this is raised if requesting a time-based map with a data source that has geometries other than points.
class cartoframes.Layer(table_name, source=None, overwrite=False, time=None, color=None, size=None, opacity=None, tooltip=None, legend=None)

Bases: cartoframes.layer.QueryLayer

A cartoframes Data Layer based on a specific table in user’s CARTO database. This layer class is used for visualizing individual datasets with CartoContext.map’s layers keyword argument.

Example

import cartoframes
from cartoframes import QueryLayer, styling
cc = cartoframes.CartoContext(BASEURL, APIKEY)
cc.map(layers=[Layer('fantastic_sql_table',
                     size=7,
                     color={'column': 'mr_fox_sightings',
                            'scheme': styling.prism(10)})])
Parameters:
  • table_name (str) – Name of table in CARTO account
  • Styling – See QueryLayer
  • a full list of all arguments arguments for styling this map data (for) –
  • layer.
  • source (pandas.DataFrame, optional) – Not currently implemented
  • overwrite (bool, optional) – Not currently implemented
class cartoframes.BinMethod

Data classification methods used for the styling of data on maps.

quantiles

Quantiles classification for quantitative data

Type:str
jenks

Jenks classification for quantitative data

Type:str
headtails

Head/Tails classification for quantitative data

Type:str
equal

Equal Interval classification for quantitative data

Type:str
category

Category classification for qualitative data

Type:str
mapping

The TurboCarto mappings

Type:dict
category = 'category'
equal = 'equal'
headtails = 'headtails'
jenks = 'jenks'
mapping = {'category': '=', 'equal': '>', 'headtails': '<', 'jenks': '>', 'quantiles': '>'}
quantiles = 'quantiles'
class cartoframes.Dataset(table_name=None, schema=None, query=None, df=None, gdf=None, state=None, context=None)

Bases: object

APPEND = 'append'
DEFAULT_RETRY_TIMES = 3
FAIL = 'fail'
GEOM_TYPE_LINE = 'line'
GEOM_TYPE_POINT = 'point'
GEOM_TYPE_POLYGON = 'polygon'
PRIVATE = 'private'
PUBLIC = 'public'
REPLACE = 'replace'
STATE_LOCAL = 'local'
STATE_REMOTE = 'remote'
compute_geom_type()

Compute the geometry type from the data

delete()
download(limit=None, decode_geom=False, retry_times=3)
exists()

Checks to see if table exists

classmethod from_dataframe(df)
classmethod from_geodataframe(gdf)
classmethod from_geojson(geojson)
classmethod from_query(query, context=None)
classmethod from_table(table_name, context=None, schema=None)
get_table_column_names(exclude=None)

Get column names and types from a table

get_table_columns()

Get column names and types from a table or query result

upload(with_lnglat=None, if_exists='fail', table_name=None, schema=None, context=None)