Filtering
The API uses the Elastic Search query string syntax for filtering.
Overview¶
First, get an idea of what fields you can use to filter from the meta_dict.
>>> import bankfind as bf
>>> fields = bf.meta_dict.keys()
dict_keys(['failures', 'history', 'institutions', 'locations', 'summary'])
Each of the keys above represent an endpoint. The values corresponding to each of the keys above are dictionaries. The dictionaries contain the fields available as well as the data type, description, and, sometimes, options to filter with.
>>> bf.meta_dict['failures'].keys()
dict_keys(['NAME', 'CERT', 'FIN', 'CITYST', 'FAILDATE', 'FAILYR', 'SAVR', 'RESTYPE1', 'CHCLASS1', 'RESTYPE', 'QBFDEP', 'QBFASSET', 'COST', 'PSTALP'])
>>> bf.meta_dict['failures']['NAME']
{'type': 'string', 'x-elastic-type': 'keyword', 'title': 'Institution Name', 'description': "This is the legal name of the institution. When available, the Institution's name links to useful information for the customers and vendors of these institutions. This information includes press releases, information about the acquiring institution, (if applicable), how your accounts and loans are affected, and how vendors can file claims against the receivership."}
Filters¶
The syntax for filtering will change based on the data-type.
strings¶
Syntax: <FIELD>:<VALUE>
First, let's filter based on cert, which as you can see from the meta_dict is a string field.
>>> bf.meta_dict['failures']['CERT']['type']
'string'
>>> data = bf.get_institutions(filters="CERT:57295")
>>> len(data['data'])
1
Chain filters together with "AND":
>>> data = bf.get_institutions(filters="STNAME:Colorado AND CITY:Denver")
>>> len(data['data'])
108
Filtering with "OR" is easy also:
>>> data = bf.get_institutions(filters='STNAME:("Colorado","Wyoming")')
>>> len(data['data'])
844
dates¶
Dates must be entered in the following format:
Syntax: <FIELD>:yyyy-mm-dd
>>> data = bf.get_institutions(filters='DATEUPDT:2019-12-31')
>>> len(data['data'])
3919
They can also be used as ranges:
exclusive¶
Use curly braces {} and the range will exclude the beginning and end dates used in the range:
Syntax: <FIELD>:{yyyy-mm-dd TO yyyy-mm-dd}
>>> data = bf.get_institutions(filters='DATEUPDT:{2015-01-01 TO 2018-12-31}')
>>> len(data['data'])
1921
inclusive¶
Use brackets [] and the range will the include the beginning and end dates used in the range
Syntax: <FIELD>:[yyyy-mm-dd TO yyyy-mm-dd]
>>> data = bf.get_institutions(filters='DATEUPDT:[2010-01-01 TO 2018-12-31]')
>>> len(data['data'])
4556
numbers¶
Numbers can also be used in ranges with the same syntax as dates
exclusive¶
Use curly braces {} and the range will exclude the beginning and end values in the range. Most of the values are represented in thousands.
Syntax: <FIELD>:{Number TO Number}
>>> data = bf.get_institutions(filters='ASSET:{25000 TO 75000}')
>>> len(data['data'])
5530
inclusive¶
Use brackets [] and the range will the include the beginning and end dates used in the range
Syntax: <FIELD>:[Number TO Number]
The filter below will retrieve institutions with assets greater than or equal to 2 billion or less than or equal to 5 billion.
>>> data = bf.get_institutions(filters='ASSET:[2000000 TO 5000000]')
>>> len(data['data'])
685
wildcard¶
Syntax: <FIELD>:[Number to *]
The filter below will retrieve institutions with assets greater than or equal to 5 billion.
>>> data = bf.get_institutions(filters='ASSET:[5000000 TO *]')
>>> len(data['data'])
602
Search¶
Flexible text search is also available. Search supports text search and fuzzy matching, as opposed to filters that are exact matches. Currently, only two endpoints support the search functionality: get_institutions and get_history.
The only field that currently supports the search functionality is NAME. It's a similar syntax to the string filter.
>>> data = bf.get_institutions(search='NAME:AMG')
>>> len(data['data'])
5
Take it a little further:
>>> data = bf.get_institutions(search='NAME:AMG National')
>>> len(data['data'])
1