# all¶

bandicoot.utils.all(user, groupby='week', summary='default', network=False, split_week=False, split_day=False, filter_empty=True, attributes=True, flatten=False)

Returns a dictionary containing all bandicoot indicators for the user, as well as reporting variables.

Relevant indicators are defined in the 'individual', and 'spatial' modules.

Reporting variables Description
antennas_path path of the CSV file containing antennas locations
attributes_path directory where attributes were loaded
version bandicoot version
groupby grouping method ('week' or None)
split_week whether or not indicators are also computed for weekday and weekend
split_day whether or not indicators are also computed for day and night
start_time time of the first record
end_time time of the last record
night_start, night_end start and end time to define nights
weekend days used to define the weekend ([6, 7] by default, where 1 is Monday)
bins number of weeks if the record are grouped
has_call whether or not records include calls
has_text whether or not records include texts
has_home whether or not a home location has been found
has_network whether or not correspondents where loaded
percent_records_missing_location percentage of records without location
antennas_missing_locations number of antennas missing a location
percent_outofnetwork_texts percentage of texts with contacts not loaded in the network
percent_outofnetwork_contacts percentage of contacts not loaded in the network
percent_outofnetwork_call_durations percentage of minutes of calls where the contact was not loaded in the network
number_of_records total number of records
number_of_weeks number of weeks with records

We also include a last set of reporting variables, for the records ignored at load-time. Values can be ignored due to missing or inconsistent fields (e.g., not including a valid 'datetime' value).

{
'all': 0,
'interaction': 0,
'direction': 0,
'correspondent_id': 0,
'datetime': 0,
'call_duration': 0
}


with the total number of records ignored (key 'all'), as well as the number of records with faulty values for each columns.