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/**
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*
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* @version 0.0.1
* @license MIT
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The API uses the Elastic Search [query string syntax](https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html#query-string-syntax) for filtering.
## Overview
First, get an idea of what fields you can use to filter from the `meta_dict`.
```python
>>> 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.
```python
>>> 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.
```python
>>> bf.meta_dict['failures']['CERT']['type']
'string'
>>> data = bf.get_institutions(filters="CERT:57295")
>>> len(data['data'])
1
```
Chain filters together with "AND":
```python
>>> data = bf.get_institutions(filters="STNAME:Colorado AND CITY:Denver")
>>> len(data['data'])
108
```
Filtering with "OR" is easy also:
```python
>>> 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`
```python
>>> 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}`
```python
>>> 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]`
```python
>>> 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}`
```python
>>> 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.*
```python
>>> 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.*
```python
>>> 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](#strings).
```python
>>> data = bf.get_institutions(search='NAME:AMG')
>>> len(data['data'])
5
```
Take it a little further:
```python
>>> data = bf.get_institutions(search='NAME:AMG National')
>>> len(data['data'])
1
```

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## **get_failures**
=== "Details"
- *Description*: Detail on failed financial institutions
- *Return*: `dict`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------------------------------------|:-----------------------------|:----------|:-----------|:--------------------------------------------------------------------------------------|
| filters | Filter(s) for the bank search | `str` | `None` | optional | |
| 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`<br>`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`<br>`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`<br>`False`
=== "Example"
```python hl_lines="3"
import bankfind as bf
data = bf.get_failures()
data['data'][0]
```
=== "Data"
```python
{
'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**
=== "Details"
- *Description*: Detail on structure change events
- *Return*: `dict`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------------------------------------|:-----------------------------|:----------|:-----------|:--------------------------------------------------------------------------------------|
| filters | Filter(s) for the bank search | `str` | `None` | optional | |
| search | Flexible text search against institution records (fuzzy name matching) | `str` | `None` | optional | |
| 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`<br>`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`<br>`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`<br>`False`
=== "Example"
```python hl_lines="3"
import bankfind as bf
data = bf.get_history()
data['data'][0]
```
=== "Data"
```python
{
'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**
=== "Details"
- *Description*: List of financial institutions
- *Return*: `dict`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------------------------------------|:-----------------------------|:----------|:-----------|:--------------------------------------------------------------------------------------|
| filters | Filter(s) for the bank search | `str` | `None` | optional | |
| search | Flexible text search against institution records (fuzzy name matching) | `str` | `None` | optional | |
| 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`<br>`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`<br>`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`<br>`False`
=== "Example"
```python hl_lines="3"
import bankfind as bf
data = bf.get_institutions()
data['data'][0]
```
=== "Data"
```python
{
'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**
=== "Details"
- *Description*: Detail on failed financial institutions
- *Return*: `dict`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------------------------------------|:-----------------------------|:----------|:-----------|:--------------------------------------------------------------------------------------|
| filters | Filter(s) for the bank search | `str` | `None` | optional | |
| 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`<br>`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`<br>`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`<br>`False`
=== "Example"
```python hl_lines="3"
import bankfind as bf
data = bf.get_locations()
data['data'][0]
```
=== "Data"
```python
{
'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**
=== "Details"
- *Description*: Detail on failed financial institutions
- *Return*: `dict`
- *Arguments*
| Argument | Description | Type | Default | Required | Options |
|:-----------|:-----------------------------------------|:-----------------------------|:----------|:-----------|:--------------------------------------------------------------------------------------|
| filters | Filter(s) for the bank search | `str` | `None` | optional | |
| 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`<br>`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`<br>`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`<br>`False`
=== "Example"
```python hl_lines="3"
import bankfind as bf
data = bf.get_failures()
data['data'][0]
```
=== "Data"
```python
{
'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,
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'comboass': 0,
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'SCUS': 316221658,
'ITRADE': 0,
'OINTEXP': 1777028,
'liqunass': 1,
'DDT': 58550380,
'EDEPFOR': 16,
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'SCEQ': 258574,
'ITAXR': 19078526,
'ILNFOR': 185,
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'SAVINGS': 659,
'ORET': 302690,
'CB_SI': 'SI',
'TOINTEXP': 2061876,
'LNMUNI': 1630491,
'LNRENRES': 98547901,
'NONII': 12419660,
'BRO': 104643398,
'ID': 'SI_2019_0'
},
'score': 1
}
```

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<p align="center">
<a href="#"><img src="img/logo.png"></a>
</p>
<p align="center">
<em>Python interface to the FDIC's API for publically available bank data</em>
</p>
<p align="center">
<a href="https://travis-ci.com/dpguthrie/bankfind" target="_blank">
<img src="https://travis-ci.com/dpguthrie/bankfind.svg?branch=master" alt="Build Status">
</a>
<a href="https://codecov.io/gh/dpguthrie/bankfind" target="_blank">
<img src="https://img.shields.io/codecov/c/github/dpguthrie/bankfind" alt="Coverage">
</a>
<a href="https://pypi.org/project/bankfind" target="_blank">
<img src="https://badge.fury.io/py/bankfind.svg" alt="Package version">
</a>
</p>
---
**Documentation**: <a target="_blank" href="https://bankfind.dpguthrie.com">https://bankfind.dpguthrie.com</a>
<!-- **Interactive Demo**: <a target="_blank" href="https://bankfind-streamlit.dpguthrie.com">https://bankfind-streamlit.herokuapp.com</a> -->
**Source Code**: <a target="_blank" href="https://github.com/dpguthrie/bankfind">https://github.com/dpguthrie/bankfind</a>
**FDIC Documentation**: <a target="_blank" href="https://banks.data.fdic.gov/docs/">https://banks.data.fdic.gov/docs/</a>
---
## Overview
**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
Python 2.7, 3.5+
- [Requests](https://requests.readthedocs.io/en/master/) - The elegant and simple HTTP library for Python, built for human beings.
## Installation
<div class="termynal" data-termynal data-ty-typeDelay="40" data-ty-lineDelay="700">
<span data-ty="input">pip install bankfind</span>
<span data-ty="progress"></span>
<span data-ty>Successfully installed bankfind</span>
<a href="#" data-terminal-control="">restart ↻</a>
</div>
## Example
```python
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
This project is licensed under the terms of the MIT license.

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/**
* termynal.js
* A lightweight, modern and extensible animated terminal window, using
* async/await.
*
* @author Ines Montani <ines@ines.io>
* @version 0.0.1
* @license MIT
*/
'use strict';
/** Generate a terminal widget. */
class Termynal {
/**
* Construct the widget's settings.
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*/
if (document.currentScript.hasAttribute('data-termynal-container')) {
const containers = document.currentScript.getAttribute('data-termynal-container');
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# Project Information
site_name: bankfind
site_description: Python wrapper allowing developers access to the FDIC’s publically available bank data
site_author: Doug Guthrie
# Repository
repo_name: bankfind
repo_url: https://github.com/dpguthrie/bankfind
# Configuration
theme:
name: material
palette:
primary: black
icon:
repo: fontawesome/brands/github-alt
logo: img/small_logo.png
favicon: img/favicon.png
language: en
# Extras
extra:
social:
- icon: fontawesome/brands/github-alt
link: https://github.com/dpguthrie
- icon: fontawesome/brands/linkedin
link: https://www.linkedin.com/in/douglas-guthrie-07994a48/
- icon: fontawesome/brands/medium
link: https://medium.com/@douglas.p.guthrie
- icon: fontawesome/solid/globe
link: https://dpguthrie.com
extra_css:
- css/termynal.css
extra_javascript:
- js/termynal.js
- js/custom.js
# Extensions
markdown_extensions:
- admonition
- codehilite:
guess_lang: false
- toc:
permalink: true
- pymdownx.superfences
- pymdownx.tabbed
- pymdownx.details
- pymdownx.emoji:
emoji_index: !!python/name:materialx.emoji.twemoji
emoji_generator: !!python/name:materialx.emoji.to_svg
# Google Analytics
google_analytics:
- UA-175202147-1
- auto
nav:
- index.md
- functions.md
- filtering.md
- release_notes.md