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{"config":{"lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Python interface to the FDIC's API for publically available bank data Documentation : https://bankfind.dpguthrie.com Source Code : https://github.com/dpguthrie/bankfind FDIC Documentation : https://banks.data.fdic.gov/docs/ Overview \u00b6 bankfind is a python interface to publically available bank data from the FDIC. There are currently, as of 8/11/20, five endpoints that the FDIC has exposed to the public: failures - returns detail on failed financial institutions institutions - returns a list of financial institutions history - returns detail on structure change events locations - returns locations / branches of financial institutions summary - returns aggregate financial and structure data, subtotaled by year, regarding financial institutions Requirements \u00b6 Python 2.7, 3.5+ Requests - The elegant and simple HTTP library for Python, built for human beings. Installation \u00b6 pip install bankfind Successfully installed bankfind restart \u21bb Example \u00b6 import bankfind as bf # Get Institutions data = bf . get_institutions () # Get Institutions from Colorado with high ROE data = bf . get_institutions ( filters = \"STNAME:Colorado AND ROE:[25 TO *]\" ) # Get Commercial Banks from Colorado that aren't S-Corps data = bf . get_institutions ( filters = \"STNAME:Colorado AND SUBCHAPS:0 AND CB:1\" ) License \u00b6 This project is licensed under the terms of the MIT license.","title":"Home"},{"location":"#overview","text":"bankfind is a python interface to publically available bank data from the FDIC. There are currently, as of 8/11/20, five endpoints that the FDIC has exposed to the public: failures - returns detail on failed financial institutions institutions - returns a list of financial institutions history - returns detail on structure change events locations - returns locations / branches of financial institutions summary - returns aggregate financial and structure data, subtotaled by year, regarding financial institutions","title":"Overview"},{"location":"#requirements","text":"Python 2.7, 3.5+ Requests - The elegant and simple HTTP library for Python, built for human beings.","title":"Requirements"},{"location":"#installation","text":"pip install bankfind Successfully installed bankfind restart \u21bb","title":"Installation"},{"location":"#example","text":"import bankfind as bf # Get Institutions data = bf . get_institutions () # Get Institutions from Colorado with high ROE data = bf . get_institutions ( filters = \"STNAME:Colorado AND ROE:[25 TO *]\" ) # Get Commercial Banks from Colorado that aren't S-Corps data = bf . get_institutions ( filters = \"STNAME:Colorado AND SUBCHAPS:0 AND CB:1\" )","title":"Example"},{"location":"#license","text":"This project is licensed under the terms of the MIT license.","title":"License"},{"location":"filtering/","text":"The API uses the Elastic Search query string syntax for filtering. Overview \u00b6 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 \u00b6 The syntax for filtering will change based on the data-type. strings \u00b6 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 [ 'institutions' ][ '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 \u00b6 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 \u00b6 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 \u00b6 Use brackets [] and the range will 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 \u00b6 Numbers can also be used in ranges with the same syntax as dates exclusive \u00b6 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 The filter above will retrieve institutions with assets greater than 25 million and less than 75 million inclusive \u00b6 Use brackets [] and the range will the include the beginning and end dates used in the range Syntax : <FIELD>:[Number TO Number] >>> data = bf . get_institutions ( filters = 'ASSET:[2000000 TO 5000000]' ) >>> len ( data [ 'data' ]) 685 The filter above will retrieve institutions with assets greater than or equal to 2 billion or less than or equal to 5 billion. wildcard \u00b6 Syntax : <FIELD>:[Number to *] >>> data = bf . get_institutions ( filters = 'ASSET:{5000000 TO *]' ) >>> len ( data [ 'data' ]) 602 The filter above will retrieve institutions with assets greater than 5 billion. Search \u00b6 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 Taking it a little further: >>> data = bf . get_institutions ( search = 'NAME:AMG National' ) >>> len ( data [ 'data' ]) 1","title":"Filtering"},{"location":"filtering/#overview","text":"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.\" }","title":"Overview"},{"location":"filtering/#filters","text":"The syntax for filtering will change based on the data-type.","title":"Filters"},{"location":"filtering/#strings","text":"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 [ 'institutions' ][ '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","title":"strings"},{"location":"filtering/#dates","text":"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:","title":"dates"},{"location":"filtering/#exclusive","text":"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","title":"exclusive"},{"location":"filtering/#inclusive","text":"Use brackets [] and the range will 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","title":"inclusive"},{"location":"filtering/#numbers","text":"Numbers can also be used in ranges with the same syntax as dates","title":"numbers"},{"location":"filtering/#exclusive_1","text":"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 The filter above will retrieve institutions with assets greater than 25 million and less than 75 million","title":"exclusive"},{"location":"filtering/#inclusive_1","text":"Use brackets [] and the range will the include the beginning and end dates used in the range Syntax : <FIELD>:[Number TO Number] >>> data = bf . get_institutions ( filters = 'ASSET:[2000000 TO 5000000]' ) >>> len ( data [ 'data' ]) 685 The filter above will retrieve institutions with assets greater than or equal to 2 billion or less than or equal to 5 billion.","title":"inclusive"},{"location":"filtering/#wildcard","text":"Syntax : <FIELD>:[Number to *] >>> data = bf . get_institutions ( filters = 'ASSET:{5000000 TO *]' ) >>> len ( data [ 'data' ]) 602 The filter above will retrieve institutions with assets greater than 5 billion.","title":"wildcard"},{"location":"filtering/#search","text":"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 Taking it a little further: >>> data = bf . get_institutions ( search = 'NAME:AMG National' ) >>> len ( data [ 'data' ]) 1","title":"Search"},{"location":"functions/","text":"get_failures \u00b6 Details Description : Detail on failed financial institutions Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_failures () data [ 'data' ][ 0 ] Data { 'data' : { 'QBFDEP' : 139526 , 'PSTALP' : 'WV' , 'FIN' : '10536' , 'FAILDATE' : '04/03/2020' , 'RESTYPE' : 'FAILURE' , 'CITYST' : 'BARBOURSVILLE, WV' , 'SAVR' : 'DIF' , 'RESTYPE1' : 'PA' , 'CHCLASS1' : 'NM' , 'NAME' : 'THE FIRST STATE BANK' , 'COST' : None , 'QBFASSET' : 152400 , 'CERT' : 14361 , 'FAILYR' : '2020' , 'ID' : '4102' }, 'score' : 1 } get_history \u00b6 Details Description : Detail on structure change events Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering search Flexible text search against institution records (fuzzy name matching) str None optional See search fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_history () data [ 'data' ][ 0 ] Data { 'data' : { 'REPORT_TYPE' : 711 , 'INSAGENT1' : 'DIF' , 'INSAGENT2' : '' , 'OFF_PCITY' : 'Colorado Springs' , 'EFFDATE' : '2020-07-27T00:00:00' , 'CHARTAGENT' : 'STATE' , 'PSTALP' : 'NE' , 'CLASS' : 'NM' , 'FRM_OFF_SERVTYPE' : 0 , 'OFF_LONGITUDE' : - 104.87635200138965 , 'OFF_PSTATE' : 'COLORADO' , 'BANK_INSURED' : 'Y' , 'CNTYNUM' : 157 , 'INSTNAME' : 'First State Bank' , 'OFF_PADDR' : '3216 W Colorado AVE' , 'FRM_CLCODE' : 0 , 'OFF_SERVTYPE_DESC' : 'FULL SERVICE - BRICK AND MORTAR' , 'TRANSNUM' : 202012234 , 'MZIPREST' : '0000' , 'FDICREGION_DESC' : 'KANSAS CITY' , 'FRM_OFF_CLCODE' : 0 , 'PZIP5' : '69361' , 'OFF_PZIPREST' : '1906' , 'OFF_NAME' : 'First State Bank Colorado Springs West Branch' , 'CERT' : 15586 , 'OFF_PSTALP' : 'CO' , 'PCITY' : 'SCOTTSBLUFF' , 'LATITUDE' : 0 , 'PROCDATE' : '2020-08-05T00:00:00' , 'ACQDATE' : '9999-12-31T00:00:00' , 'CHANGECODE' : 711 , 'PADDR' : '2002 BROADWAY' , 'MZIP5' : '69361' , 'FI_UNINUM' : 9873 , 'LONGITUDE' : 0 , 'FRM_LATITUDE' : 0 , 'STATE' : 'NEBRASKA' , 'MSTALP' : 'NE' , 'CNTYNAME' : 'SCOTTS BLUFF' , 'ACQ_UNINUM' : 0 , 'OFF_CNTYNUM' : 41 , 'FI_EFFDATE' : '2019-06-10T00:00:00' , 'FDICREGION' : 11 , 'MSTATE' : 'NEBRASKA' , 'FRM_LONGITUDE' : 0 , 'OFF_CNTYNAME' : 'EL PASO' , 'CHANGECODE_DESC' : 'BRANCH OPENING' , 'MCITY' : 'SCOTTSBLUFF' , 'MADDR' : 'P.O. BOX 1267' , 'OFF_PZIP5' : '80904' , 'OUT_UNINUM' : 0 , 'PZIPREST' : '0000' , 'ORG_STAT_FLG' : 'Y' , 'FRM_OFF_LONGITUDE' : 0 , 'ENDDATE' : '9999-12-31T00:00:00' , 'UNINUM' : 625952 , 'OFF_NUM' : 6 , 'CLCODE' : 21 , 'OFF_SERVTYPE' : 11 , 'FRM_OFF_CNTYNUM' : 0 , 'ORG_ROLE_CDE' : 'BR' , 'REGAGENT' : 'FDIC' , 'OFF_LATITUDE' : 38.85583298227556 , 'ESTDATE' : '2020-07-27T00:00:00' , 'FRM_OFF_LATITUDE' : 0 , 'TRUST' : 'Full' , 'ID' : '20eb98a36c7c77cf6bc019ce391ba7c9' }, 'score' : 1 } get_institutions \u00b6 Details Description : List of financial institutions Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering search Flexible text search against institution records (fuzzy name matching) str None optional See [search](#filtering.md#search fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_institutions () data [ 'data' ][ 0 ] Data { 'data' : { 'ZIP' : '31087' , 'SASSER' : 0 , 'CHRTAGNT' : 'STATE' , 'CONSERVE' : 'N' , 'REGAGENT2' : '' , 'STNAME' : 'Georgia' , 'ROAQ' : 0.65 , 'INSDATE' : '01/01/1934' , 'TE06N528' : '' , 'TE06N529' : '' , 'OFFOA' : 0 , 'FDICDBS' : '05' , 'NAMEHCR' : '' , 'OCCDIST' : '5' , 'CMSA' : '' , 'DEPDOM' : 59267 , 'CBSA_METRO_FLG' : '0' , 'TE10N528' : '' , 'NETINC' : 124 , 'CBSA_DIV_NO' : '' , 'MUTUAL' : '0' , 'MSA_NO' : '0' , 'OFFFOR' : 0 , 'INSSAVE' : 0 , 'CHARTER' : '0' , 'RSSDHCR' : '' , 'TE04N528' : '' , 'TE04N529' : '' , 'CERT' : '10057' , 'STALP' : 'GA' , 'SPECGRP' : 7 , 'CFPBENDDTE' : '31-Dec-9999' , 'TE09N528' : '' , 'IBA' : 0 , 'INSBIF' : 0 , 'INSFDIC' : 1 , 'ENDEFYMD' : '12/31/9999' , 'MSA' : '' , 'TE02N528' : '' , 'CB' : '1' , 'TE02N529' : '' , 'TE07N528' : '' , 'FDICSUPV' : 'Atlanta' , 'FED' : '6' , 'REGAGNT' : 'FDIC' , 'NEWCERT' : 0 , 'ASSET' : 76416 , 'CBSA_MICRO_FLG' : '1' , 'OFFICES' : 1 , 'STCNTY' : '13141' , 'CSA_FLG' : '0' , 'CITY' : 'Sparta' , 'CLCODE' : '21' , 'INACTIVE' : 0 , 'CMSA_NO' : '0' , 'STALPHCR' : '' , 'INSAGNT1' : 'DIF' , 'BKCLASS' : 'NM' , 'EFFDATE' : '08/31/2009' , 'SUPRV_FD' : '05' , 'DATEUPDT' : '09/02/2009' , 'INSAGNT2' : '' , 'TE05N528' : '' , 'TE05N529' : '' , 'ROEQ' : 2.96 , 'FDICREGN' : 'Atlanta' , 'FLDOFF' : 'Savannah' , 'WEBADDR' : 'http://www.bankofhancock.com' , 'QBPRCOML' : '2' , 'COUNTY' : 'Hancock' , 'DOCKET' : '0' , 'ULTCERT' : '10057' , 'OTSDIST' : '2' , 'LAW_SASSER_FLG' : 'N' , 'PARCERT' : '0' , 'ROA' : 0.65 , 'CFPBFLAG' : 0 , 'RISDATE' : '12/31/2019' , 'ROE' : 2.96 , 'INSCOML' : 1 , 'OTSREGNM' : 'Southeast' , 'EQ' : '17026' , 'RUNDATE' : '08/08/2020' , 'TE03N528' : '' , 'TE03N529' : '' , 'NAME' : 'Bank of Hancock County' , 'HCTMULT' : '' , 'CBSA_DIV' : '' , 'ADDRESS' : '12855 Broad Street' , 'OFFDOM' : 1 , 'SUBCHAPS' : '0' , 'PROCDATE' : '09/02/2009' , 'INSSAIF' : 0 , 'DENOVO' : '0' , 'CBSA_NO' : '33300' , 'ACTIVE' : 1 , 'CFPBEFFDTE' : '31-Dec-9999' , 'STCHRTR' : 1 , 'REPDTE' : '03/31/2020' , 'FORM31' : '0' , 'CSA' : '' , 'INSDIF' : 1 , 'TE01N529' : '' , 'ROAPTX' : 0.65 , 'STNUM' : '13' , 'OAKAR' : 0 , 'SPECGRPN' : 'Other Specialized Under 1 Billion' , 'ROAPTXQ' : 0.65 , 'FED_RSSD' : '37' , 'CSA_NO' : '' , 'CBSA_METRO' : 0 , 'INSTCRCD' : 0 , 'DEP' : 59267 , 'UNINUM' : '6429' , 'INSTAG' : '0' , 'TE01N528' : '' , 'CITYHCR' : '' , 'TRACT' : '0' , 'CBSA' : 'Milledgeville, GA' , 'CBSA_DIV_FLG' : '0' , 'TE08N528' : '' , 'NETINCQ' : 124 , 'CHANGEC1' : 520 , 'CERTCONS' : '0' , 'ESTYMD' : '09/01/1904' , 'FEDCHRTR' : 0 , 'TRUST' : '0' , 'ID' : '10057' }, 'score' : 1 } get_locations \u00b6 Details Description : Detail on failed financial institutions Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_locations () data [ 'data' ][ 0 ] Data { 'data' : { 'ZIP' : '21613' , 'CBSA_NO' : '15700' , 'BKCLASS' : 'SM' , 'FI_UNINUM' : 3221 , 'STNAME' : 'Maryland' , 'CSA' : 'Salisbury-Cambridge, MD-DE' , 'COUNTY' : 'Dorchester' , 'MAINOFF' : 0 , 'OFFNAME' : 'WOODS ROAD BRANCH' , 'CBSA_METRO_FLG' : '0' , 'CBSA_MICRO_FLG' : '1' , 'CSA_NO' : '480' , 'CBSA_METRO' : 0 , 'CBSA_DIV_NO' : '' , 'RUNDATE' : '08/07/2020' , 'NAME' : '1880 Bank' , 'UNINUM' : 204568 , 'SERVTYPE' : 11 , 'CSA_FLG' : '1' , 'STCNTY' : '24019' , 'CBSA' : 'Cambridge, MD' , 'CBSA_DIV' : '' , 'CBSA_DIV_FLG' : '0' , 'CITY' : 'Cambridge' , 'ADDRESS' : '803 Woods Road' , 'CERT' : '4829' , 'STALP' : 'MD' , 'OFFNUM' : 1 , 'ESTYMD' : '12/23/1968' , 'ID' : '204568' }, 'score' : 1 } get_summary \u00b6 Details Description : Detail on failed financial institutions Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_failures () data [ 'data' ][ 0 ] Data { 'data' : { 'INTINC2' : 51722726 , 'EXTRA' : 1316 , 'LNATRES' : 9769341 , 'chrtrest' : 0 , 'STNAME' : 'United States and Other Areas' , 'ILNS' : 39718788 , 'LNAG' : 3306388 , 'EINTEXP2' : 10348941 , 'EPREMAGG' : 2063405 , 'YEAR' : '2019' , 'BKPREM' : 8315925 , 'INTAN' : 12025281 , 'LNRE' : 444072342 , 'chartoth' : 1 , 'IGLSEC' : 482482 , 'OT_BIF' : 0 , 'EAMINTAN' : 456598 , 'newcount' : 0 , 'DEPI' : 840535976 , 'EFHLBADV' : None , 'tofail' : 1 , 'SCMTGBK' : 292023664 , 'NTRTMLG' : 118177154 , 'OEA' : 1483578 , 'EFREPP' : 90846 , 'LNLSGR' : 655127513 , 'NETINC' : 15194171 , 'TOT_OTS' : 334 , 'CONS' : 0 , 'OTHNBORR' : 91479748 , 'LNREMULT' : 68529412 , 'P9LNLS' : 7463014 , 'COUNT' : 659 , 'LNRERES' : 253541537 , 'EQCS' : 790384 , 'SCAGE' : 304050945 , 'LNRECONS' : 23453492 , 'TOT_FDIC' : 325 , 'EINTEXP' : 10348941 , 'TPD' : 13656751 , 'LNCI' : 43378018 , 'EQNM' : 125002942 , 'INTBLIB' : 1007906756 , 'liqasstd' : 0 , 'SC' : 385021771 , 'INTBAST' : 1096557030 , 'EDEPDOM' : 8287049 , 'ILNDOM' : 39718603 , 'NCLNLS' : 11414221 , 'UNINC' : 133264 , 'ISC' : 10339424 , 'LIABEQ' : 1153906385 , 'tochrt' : 7 , 'IFEE' : 865589 , 'TOT_SAVE' : 659 , 'LNRESRE' : None , 'alsonew' : 0 , 'NUMEMP' : 121746 , 'ASSET' : 1153906405 , 'TINTINC' : 11723463 , 'NALNLS' : 3951207 , 'EOTHNINT' : 14513593 , 'TRADES' : 0 , 'ESAL' : 12889946 , 'ILNLS' : 39999263 , 'LIAB' : 1028873691 , 'LNDEP' : 417597 , 'OTHBFHLB' : 75972627 , 'ITAX' : 4361954 , 'EQCDIVP' : 12402 , 'SCRES' : None , 'TRADE' : 356945 , 'MISSADJ' : - 1 , 'FD_BIF' : 0 , 'CRLNLS' : 1422110 , 'LS' : 5575099 , 'tomerg' : 11 , 'ELNATR' : 5247975 , 'LNCRCD' : 99551689 , 'INTINC' : 51722726 , 'EQUPTOT' : 70024149 , 'CHBALI' : 64693509 , 'EQPP' : 282890 , 'PTXNOINC' : 19078526 , 'OINTINC' : 11723463 , 'tortc' : 0 , 'ILS' : 280475 , 'FD_SAIF' : 0 , 'EQNWCERT' : None , 'OINTBOR' : 85304294 , 'SCUST' : 12170713 , 'combos' : 12 , 'P3LNLS' : 6193737 , 'OTLNCNTA' : None , 'OTHLIAB' : 16342193 , 'IFREPO' : 18439 , 'LNLSNET' : 645358172 , 'LNCONOT1' : None , 'EQCDIVC' : 13089203 , 'SCUSA' : 316221658 , 'DRLNLS' : 7211296 , 'OTHBORR' : 1685904 , 'EQCDIV' : 13101605 , 'EDEP' : 8287065 , 'BRWDMONY' : 1685904 , 'comboass' : 0 , 'FREPO' : 1126633 , 'CHBAL' : 73009001 , 'ALLOTHER' : 14950385 , 'FREPP' : 6067807 , 'IRAKEOGH' : 123424709 , 'OT_SAIF' : 0 , 'ORE' : 302690 , 'SCMUNI' : 10308292 , 'ESUBND' : 278 , 'SCUS' : 316221658 , 'ITRADE' : 0 , 'OINTEXP' : 1777028 , 'liqunass' : 1 , 'DDT' : 58550380 , 'EDEPFOR' : 16 , 'LNALLOTH' : 42378773 , 'SCEQ' : 258574 , 'ITAXR' : 19078526 , 'ILNFOR' : 185 , 'ICHBAL' : 1365600 , 'LNRELOC' : 21179492 , 'STNUM' : '0' , 'SUBLLPF' : 26052 , 'OONONII' : 11554071 , 'CORPBNDS' : 55721897 , 'NONIX' : 29466944 , 'NCHGREC' : 5789186 , 'OTHASST' : 28389467 , 'DEP' : 921025698 , 'NIM' : 41373785 , 'LNCON' : 141930462 , 'EQSUR' : 53905519 , 'SAVINGS' : 659 , 'ORET' : 302690 , 'CB_SI' : 'SI' , 'TOINTEXP' : 2061876 , 'LNMUNI' : 1630491 , 'LNRENRES' : 98547901 , 'NONII' : 12419660 , 'BRO' : 104643398 , 'ID' : 'SI_2019_0' }, 'score' : 1 }","title":"Functions"},{"location":"functions/#get_failures","text":"Details Description : Detail on failed financial institutions Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_failures () data [ 'data' ][ 0 ] Data { 'data' : { 'QBFDEP' : 139526 , 'PSTALP' : 'WV' , 'FIN' : '10536' , 'FAILDATE' : '04/03/2020' , 'RESTYPE' : 'FAILURE' , 'CITYST' : 'BARBOURSVILLE, WV' , 'SAVR' : 'DIF' , 'RESTYPE1' : 'PA' , 'CHCLASS1' : 'NM' , 'NAME' : 'THE FIRST STATE BANK' , 'COST' : None , 'QBFASSET' : 152400 , 'CERT' : 14361 , 'FAILYR' : '2020' , 'ID' : '4102' }, 'score' : 1 }","title":"get_failures"},{"location":"functions/#get_history","text":"Details Description : Detail on structure change events Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering search Flexible text search against institution records (fuzzy name matching) str None optional See search fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_history () data [ 'data' ][ 0 ] Data { 'data' : { 'REPORT_TYPE' : 711 , 'INSAGENT1' : 'DIF' , 'INSAGENT2' : '' , 'OFF_PCITY' : 'Colorado Springs' , 'EFFDATE' : '2020-07-27T00:00:00' , 'CHARTAGENT' : 'STATE' , 'PSTALP' : 'NE' , 'CLASS' : 'NM' , 'FRM_OFF_SERVTYPE' : 0 , 'OFF_LONGITUDE' : - 104.87635200138965 , 'OFF_PSTATE' : 'COLORADO' , 'BANK_INSURED' : 'Y' , 'CNTYNUM' : 157 , 'INSTNAME' : 'First State Bank' , 'OFF_PADDR' : '3216 W Colorado AVE' , 'FRM_CLCODE' : 0 , 'OFF_SERVTYPE_DESC' : 'FULL SERVICE - BRICK AND MORTAR' , 'TRANSNUM' : 202012234 , 'MZIPREST' : '0000' , 'FDICREGION_DESC' : 'KANSAS CITY' , 'FRM_OFF_CLCODE' : 0 , 'PZIP5' : '69361' , 'OFF_PZIPREST' : '1906' , 'OFF_NAME' : 'First State Bank Colorado Springs West Branch' , 'CERT' : 15586 , 'OFF_PSTALP' : 'CO' , 'PCITY' : 'SCOTTSBLUFF' , 'LATITUDE' : 0 , 'PROCDATE' : '2020-08-05T00:00:00' , 'ACQDATE' : '9999-12-31T00:00:00' , 'CHANGECODE' : 711 , 'PADDR' : '2002 BROADWAY' , 'MZIP5' : '69361' , 'FI_UNINUM' : 9873 , 'LONGITUDE' : 0 , 'FRM_LATITUDE' : 0 , 'STATE' : 'NEBRASKA' , 'MSTALP' : 'NE' , 'CNTYNAME' : 'SCOTTS BLUFF' , 'ACQ_UNINUM' : 0 , 'OFF_CNTYNUM' : 41 , 'FI_EFFDATE' : '2019-06-10T00:00:00' , 'FDICREGION' : 11 , 'MSTATE' : 'NEBRASKA' , 'FRM_LONGITUDE' : 0 , 'OFF_CNTYNAME' : 'EL PASO' , 'CHANGECODE_DESC' : 'BRANCH OPENING' , 'MCITY' : 'SCOTTSBLUFF' , 'MADDR' : 'P.O. BOX 1267' , 'OFF_PZIP5' : '80904' , 'OUT_UNINUM' : 0 , 'PZIPREST' : '0000' , 'ORG_STAT_FLG' : 'Y' , 'FRM_OFF_LONGITUDE' : 0 , 'ENDDATE' : '9999-12-31T00:00:00' , 'UNINUM' : 625952 , 'OFF_NUM' : 6 , 'CLCODE' : 21 , 'OFF_SERVTYPE' : 11 , 'FRM_OFF_CNTYNUM' : 0 , 'ORG_ROLE_CDE' : 'BR' , 'REGAGENT' : 'FDIC' , 'OFF_LATITUDE' : 38.85583298227556 , 'ESTDATE' : '2020-07-27T00:00:00' , 'FRM_OFF_LATITUDE' : 0 , 'TRUST' : 'Full' , 'ID' : '20eb98a36c7c77cf6bc019ce391ba7c9' }, 'score' : 1 }","title":"get_history"},{"location":"functions/#get_institutions","text":"Details Description : List of financial institutions Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering search Flexible text search against institution records (fuzzy name matching) str None optional See [search](#filtering.md#search fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_institutions () data [ 'data' ][ 0 ] Data { 'data' : { 'ZIP' : '31087' , 'SASSER' : 0 , 'CHRTAGNT' : 'STATE' , 'CONSERVE' : 'N' , 'REGAGENT2' : '' , 'STNAME' : 'Georgia' , 'ROAQ' : 0.65 , 'INSDATE' : '01/01/1934' , 'TE06N528' : '' , 'TE06N529' : '' , 'OFFOA' : 0 , 'FDICDBS' : '05' , 'NAMEHCR' : '' , 'OCCDIST' : '5' , 'CMSA' : '' , 'DEPDOM' : 59267 , 'CBSA_METRO_FLG' : '0' , 'TE10N528' : '' , 'NETINC' : 124 , 'CBSA_DIV_NO' : '' , 'MUTUAL' : '0' , 'MSA_NO' : '0' , 'OFFFOR' : 0 , 'INSSAVE' : 0 , 'CHARTER' : '0' , 'RSSDHCR' : '' , 'TE04N528' : '' , 'TE04N529' : '' , 'CERT' : '10057' , 'STALP' : 'GA' , 'SPECGRP' : 7 , 'CFPBENDDTE' : '31-Dec-9999' , 'TE09N528' : '' , 'IBA' : 0 , 'INSBIF' : 0 , 'INSFDIC' : 1 , 'ENDEFYMD' : '12/31/9999' , 'MSA' : '' , 'TE02N528' : '' , 'CB' : '1' , 'TE02N529' : '' , 'TE07N528' : '' , 'FDICSUPV' : 'Atlanta' , 'FED' : '6' , 'REGAGNT' : 'FDIC' , 'NEWCERT' : 0 , 'ASSET' : 76416 , 'CBSA_MICRO_FLG' : '1' , 'OFFICES' : 1 , 'STCNTY' : '13141' , 'CSA_FLG' : '0' , 'CITY' : 'Sparta' , 'CLCODE' : '21' , 'INACTIVE' : 0 , 'CMSA_NO' : '0' , 'STALPHCR' : '' , 'INSAGNT1' : 'DIF' , 'BKCLASS' : 'NM' , 'EFFDATE' : '08/31/2009' , 'SUPRV_FD' : '05' , 'DATEUPDT' : '09/02/2009' , 'INSAGNT2' : '' , 'TE05N528' : '' , 'TE05N529' : '' , 'ROEQ' : 2.96 , 'FDICREGN' : 'Atlanta' , 'FLDOFF' : 'Savannah' , 'WEBADDR' : 'http://www.bankofhancock.com' , 'QBPRCOML' : '2' , 'COUNTY' : 'Hancock' , 'DOCKET' : '0' , 'ULTCERT' : '10057' , 'OTSDIST' : '2' , 'LAW_SASSER_FLG' : 'N' , 'PARCERT' : '0' , 'ROA' : 0.65 , 'CFPBFLAG' : 0 , 'RISDATE' : '12/31/2019' , 'ROE' : 2.96 , 'INSCOML' : 1 , 'OTSREGNM' : 'Southeast' , 'EQ' : '17026' , 'RUNDATE' : '08/08/2020' , 'TE03N528' : '' , 'TE03N529' : '' , 'NAME' : 'Bank of Hancock County' , 'HCTMULT' : '' , 'CBSA_DIV' : '' , 'ADDRESS' : '12855 Broad Street' , 'OFFDOM' : 1 , 'SUBCHAPS' : '0' , 'PROCDATE' : '09/02/2009' , 'INSSAIF' : 0 , 'DENOVO' : '0' , 'CBSA_NO' : '33300' , 'ACTIVE' : 1 , 'CFPBEFFDTE' : '31-Dec-9999' , 'STCHRTR' : 1 , 'REPDTE' : '03/31/2020' , 'FORM31' : '0' , 'CSA' : '' , 'INSDIF' : 1 , 'TE01N529' : '' , 'ROAPTX' : 0.65 , 'STNUM' : '13' , 'OAKAR' : 0 , 'SPECGRPN' : 'Other Specialized Under 1 Billion' , 'ROAPTXQ' : 0.65 , 'FED_RSSD' : '37' , 'CSA_NO' : '' , 'CBSA_METRO' : 0 , 'INSTCRCD' : 0 , 'DEP' : 59267 , 'UNINUM' : '6429' , 'INSTAG' : '0' , 'TE01N528' : '' , 'CITYHCR' : '' , 'TRACT' : '0' , 'CBSA' : 'Milledgeville, GA' , 'CBSA_DIV_FLG' : '0' , 'TE08N528' : '' , 'NETINCQ' : 124 , 'CHANGEC1' : 520 , 'CERTCONS' : '0' , 'ESTYMD' : '09/01/1904' , 'FEDCHRTR' : 0 , 'TRUST' : '0' , 'ID' : '10057' }, 'score' : 1 }","title":"get_institutions"},{"location":"functions/#get_locations","text":"Details Description : Detail on failed financial institutions Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_locations () data [ 'data' ][ 0 ] Data { 'data' : { 'ZIP' : '21613' , 'CBSA_NO' : '15700' , 'BKCLASS' : 'SM' , 'FI_UNINUM' : 3221 , 'STNAME' : 'Maryland' , 'CSA' : 'Salisbury-Cambridge, MD-DE' , 'COUNTY' : 'Dorchester' , 'MAINOFF' : 0 , 'OFFNAME' : 'WOODS ROAD BRANCH' , 'CBSA_METRO_FLG' : '0' , 'CBSA_MICRO_FLG' : '1' , 'CSA_NO' : '480' , 'CBSA_METRO' : 0 , 'CBSA_DIV_NO' : '' , 'RUNDATE' : '08/07/2020' , 'NAME' : '1880 Bank' , 'UNINUM' : 204568 , 'SERVTYPE' : 11 , 'CSA_FLG' : '1' , 'STCNTY' : '24019' , 'CBSA' : 'Cambridge, MD' , 'CBSA_DIV' : '' , 'CBSA_DIV_FLG' : '0' , 'CITY' : 'Cambridge' , 'ADDRESS' : '803 Woods Road' , 'CERT' : '4829' , 'STALP' : 'MD' , 'OFFNUM' : 1 , 'ESTYMD' : '12/23/1968' , 'ID' : '204568' }, 'score' : 1 }","title":"get_locations"},{"location":"functions/#get_summary","text":"Details Description : Detail on failed financial institutions Return : dict or pandas.DataFrame Arguments Argument Description Type Default Required Options filters Filter(s) for the bank search str None optional See filtering fields Comma delimited list of fields to retrieve str All fields included by default optional sort_by Field name by which to sort returned data str FAILDATE optional See meta_dict sort_order Indicator if ascending or descending str DESC optional ASC DESC limit Number of records to return int 10000 optional 0 to 10,000 offset Offset of page to return int 0 optional output Format of data to return str json optional json pandas limit Number of records to return int 10000 optional 0 to 10,000 friendly_fields Replace keys / column names with friendlier title bool False optional True False Example import bankfind as bf data = bf . get_failures () data [ 'data' ][ 0 ] Data { 'data' : { 'INTINC2' : 51722726 , 'EXTRA' : 1316 , 'LNATRES' : 9769341 , 'chrtrest' : 0 , 'STNAME' : 'United States and Other Areas' , 'ILNS' : 39718788 , 'LNAG' : 3306388 , 'EINTEXP2' : 10348941 , 'EPREMAGG' : 2063405 , 'YEAR' : '2019' , 'BKPREM' : 8315925 , 'INTAN' : 12025281 , 'LNRE' : 444072342 , 'chartoth' : 1 , 'IGLSEC' : 482482 , 'OT_BIF' : 0 , 'EAMINTAN' : 456598 , 'newcount' : 0 , 'DEPI' : 840535976 , 'EFHLBADV' : None , 'tofail' : 1 , 'SCMTGBK' : 292023664 , 'NTRTMLG' : 118177154 , 'OEA' : 1483578 , 'EFREPP' : 90846 , 'LNLSGR' : 655127513 , 'NETINC' : 15194171 , 'TOT_OTS' : 334 , 'CONS' : 0 , 'OTHNBORR' : 91479748 , 'LNREMULT' : 68529412 , 'P9LNLS' : 7463014 , 'COUNT' : 659 , 'LNRERES' : 253541537 , 'EQCS' : 790384 , 'SCAGE' : 304050945 , 'LNRECONS' : 23453492 , 'TOT_FDIC' : 325 , 'EINTEXP' : 10348941 , 'TPD' : 13656751 , 'LNCI' : 43378018 , 'EQNM' : 125002942 , 'INTBLIB' : 1007906756 , 'liqasstd' : 0 , 'SC' : 385021771 , 'INTBAST' : 1096557030 , 'EDEPDOM' : 8287049 , 'ILNDOM' : 39718603 , 'NCLNLS' : 11414221 , 'UNINC' : 133264 , 'ISC' : 10339424 , 'LIABEQ' : 1153906385 , 'tochrt' : 7 , 'IFEE' : 865589 , 'TOT_SAVE' : 659 , 'LNRESRE' : None , 'alsonew' : 0 , 'NUMEMP' : 121746 , 'ASSET' : 1153906405 , 'TINTINC' : 11723463 , 'NALNLS' : 3951207 , 'EOTHNINT' : 14513593 , 'TRADES' : 0 , 'ESAL' : 12889946 , 'ILNLS' : 39999263 , 'LIAB' : 1028873691 , 'LNDEP' : 417597 , 'OTHBFHLB' : 75972627 , 'ITAX' : 4361954 , 'EQCDIVP' : 12402 , 'SCRES' : None , 'TRADE' : 356945 , 'MISSADJ' : - 1 , 'FD_BIF' : 0 , 'CRLNLS' : 1422110 , 'LS' : 5575099 , 'tomerg' : 11 , 'ELNATR' : 5247975 , 'LNCRCD' : 99551689 , 'INTINC' : 51722726 , 'EQUPTOT' : 70024149 , 'CHBALI' : 64693509 , 'EQPP' : 282890 , 'PTXNOINC' : 19078526 , 'OINTINC' : 11723463 , 'tortc' : 0 , 'ILS' : 280475 , 'FD_SAIF' : 0 , 'EQNWCERT' : None , 'OINTBOR' : 85304294 , 'SCUST' : 12170713 , 'combos' : 12 , 'P3LNLS' : 6193737 , 'OTLNCNTA' : None , 'OTHLIAB' : 16342193 , 'IFREPO' : 18439 , 'LNLSNET' : 645358172 , 'LNCONOT1' : None , 'EQCDIVC' : 13089203 , 'SCUSA' : 316221658 , 'DRLNLS' : 7211296 , 'OTHBORR' : 1685904 , 'EQCDIV' : 13101605 , 'EDEP' : 8287065 , 'BRWDMONY' : 1685904 , 'comboass' : 0 , 'FREPO' : 1126633 , 'CHBAL' : 73009001 , 'ALLOTHER' : 14950385 , 'FREPP' : 6067807 , 'IRAKEOGH' : 123424709 , 'OT_SAIF' : 0 , 'ORE' : 302690 , 'SCMUNI' : 10308292 , 'ESUBND' : 278 , 'SCUS' : 316221658 , 'ITRADE' : 0 , 'OINTEXP' : 1777028 , 'liqunass' : 1 , 'DDT' : 58550380 , 'EDEPFOR' : 16 , 'LNALLOTH' : 42378773 , 'SCEQ' : 258574 , 'ITAXR' : 19078526 , 'ILNFOR' : 185 , 'ICHBAL' : 1365600 , 'LNRELOC' : 21179492 , 'STNUM' : '0' , 'SUBLLPF' : 26052 , 'OONONII' : 11554071 , 'CORPBNDS' : 55721897 , 'NONIX' : 29466944 , 'NCHGREC' : 5789186 , 'OTHASST' : 28389467 , 'DEP' : 921025698 , 'NIM' : 41373785 , 'LNCON' : 141930462 , 'EQSUR' : 53905519 , 'SAVINGS' : 659 , 'ORET' : 302690 , 'CB_SI' : 'SI' , 'TOINTEXP' : 2061876 , 'LNMUNI' : 1630491 , 'LNRENRES' : 98547901 , 'NONII' : 12419660 , 'BRO' : 104643398 , 'ID' : 'SI_2019_0' }, 'score' : 1 }","title":"get_summary"},{"location":"release_notes/","text":"","title":"Release notes"}]} |