Data

API	Description
`list_datasets`	List available scikit-mobility datasets.
`load_dataset`	Load one of the original scikit-mobility datasets.
`get_dataset_info`	Return metadata for a dataset.

Datasets

Name	Type	Description
`foursquare_nyc`	trajectory	Foursquare checkins of individuals in New York City.
`flow_foursquare_nyc`	flow	Movements between 4 km tiles in NYC derived from Foursquare data.
`taxi_san_francisco`	trajectory	GPS traces of ~500 taxis over 30 days in the San Francisco Bay Area. Requires auth.
`parking_san_francisco`	auxiliar	Parking meters in San Francisco.
`nyc_boundaries`	shape	NYC Borough Boundaries shapefile (water areas included).

API Reference

`fastmob.data.list_datasets(details=False, data_types=None)`

List datasets.

List all the names of the datasets available in the data module of scikit-mobility.

Parameters:

Name	Type	Description	Default
`details`	`bool`	Whether to return full metadata for each dataset instead of the name only. The default is `False`.	`False`
`data_types`	`str or list of str`	Specify which dataset types to show. Accepted values: `"trajectory"`, `"flow"`, `"shape"`, `"auxiliar"`. The default is `None` (all types).	`None`

Returns:

Type	Description
`list of str or dict`	A list of dataset names, or a dict mapping name to metadata when `details=True`.

Examples:

>>> from fastmob.data import list_datasets
>>> list_datasets()
['flow_foursquare_nyc', 'foursquare_nyc', 'nyc_boundaries', 'parking_san_francisco', 'taxi_san_francisco']

`fastmob.data.load_dataset(name, drop_columns=False, auth=None, show_progress=False)`

Load dataset.

Load one of the datasets that are present in the repository of scikit-mobility.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the dataset to load (e.g., `foursquare_nyc`).	required
`drop_columns`	`bool`	Whether to keep additional columns when returning a TrajDataFrame. The default is `False`.	`False`
`auth`	`tuple of str`	Pair of strings `(user, password)` used when the dataset requires authentication. The default is `None`.	`None`
`show_progress`	`bool`	If `True`, show a progress bar during download. The default is `False`.	`False`

Returns:

Type	Description
`TrajDataFrame or FlowDataFrame or GeoDataFrame or DataFrame`	An object containing the downloaded dataset.

Examples:

>>> from fastmob.data import load_dataset, list_datasets
>>> tdf_nyc = load_dataset("foursquare_nyc", drop_columns=True)
>>> print(tdf_nyc.head())
   uid        lat        lng                  datetime
0  470  40.719810 -74.002581 2012-04-03 18:00:09+00:00
1  979  40.606800 -74.044170 2012-04-03 18:00:25+00:00
2   69  40.716162 -73.883070 2012-04-03 18:02:24+00:00
3  395  40.745164 -73.982519 2012-04-03 18:02:41+00:00
4   87  40.740104 -73.989658 2012-04-03 18:03:00+00:00

`fastmob.data.get_dataset_info(name)`

Get dataset info.

Return the metadata stored in the JSON file associated with the dataset.

Parameters:

Name	Type	Description	Default
`name`	`str`	The name of the dataset to query (e.g., `foursquare_nyc`).	required

Returns:

Type	Description
`dict`	A dictionary containing the dataset metadata.

Examples:

>>> from fastmob.data import get_dataset_info
>>> get_dataset_info("foursquare_nyc")
{'name': 'Foursquare_NYC',
 'description': 'Dataset containing the Foursquare checkins of individuals moving in New York City',
 'url': 'http://www-public.it-sudparis.eu/~zhang_da/pub/dataset_tsmc2014.zip',
 'hash': 'cbe3fdab373d24b09b5fc53509c8958c77ff72b6c1a68589ce337d4f9a80235b',
 'auth': 'no',
 'data_type': 'trajectory',
 'download_format': 'zip',
 'sep': '\t',
 'encoding': 'ISO-8859-1'}