--- agentTools: projectIndex: https://docs.thinknum.com/llms.txt --- # Function [block:api-header] { "title": "Query Functions" } [/block] There a few functions that you can apply to queries to gather even more insight into the data. You can retrieve a listing of the available functions in a dataset with the following request: [block:code] { "codes": [ { "code": "GET /dataset/ HTTP/1.1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130", "language": "http" } ] } [/block] A sample response is as follows: [block:code] { "codes": [ { "code": "{\n ...\n \"id\": \"job_listings\",\n \"truncate_limit\": 500000,\n \"functions\": {\n \"genderdecoder\": {\n \"description\": \"Analyze potential gender bias in job listings.\",\n \"blog_url\": \"https://blog.thinknum.com/job-listings/\"\n },\n \"sales\": {\n \"description\": \"See which individual jobs have been filled within the defined Lookahead Days. \",\n \"blog_url\": \"https://blog.thinknum.com/job-listings/#recruitment-analysis-function\"\n },\n \"changeovertime\": {\n \"description\": \"Calculate the difference between a data point today and a user defined frequency.\",\n \"blog_url\": \"https://blog.thinknum.com/change-over-time/\"\n }\n },\n ...\n}", "language": "json" } ] } [/block] Note: Currently, you can only apply functions against the “Store Locations” dataset. [block:api-header] { "title": "Nearby Competitors" } [/block] This is a function that can be applied to queries that contain coordinate data. For each item in the query, It provides a count of the numbers of competitors (provided in a list either by ticker and/or entity) within a specified distance. The nearby function has the following parameters: [block:parameters] { "data": { "h-2": "Required?", "h-1": "Description", "h-0": "Parameter", "0-0": "dataset", "0-2": "Yes", "1-0": "tickers", "1-2": "No, if entities is defined;\nyes, otherwise.", "2-0": "entities", "2-2": "No, if tickers is defined;\nyes, otherwise.", "3-0": "distance", "3-2": "Yes", "0-1": "The dataset of the competitors", "1-1": "List of competitor tickers", "2-1": "List of competitor entities", "3-1": "The radius in miles to consider as \"nearby\"" }, "cols": 3, "rows": 4 } [/block] For example, if you are analyzing the "Store Locations" dataset, and you would like to see how many Ralph Lauren stores are within 5 miles of each Nordstrom store, you can run the following query: [block:code] { "codes": [ { "code": "POST /dataset/store/query/ HTTP/1.1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\n\n{\n \"tickers\": [\n \"nyse:jwn\"\n ],\n \"functions\": [\n {\n \"function\": \"nearby\",\n \"parameters\": {\n \"dataset\": \"store\",\n \"dataset_type\": \"dataset\",\n \"distance\": 5,\n \"entities\": [],\n \"is_include_closed\": false,\n \"tickers\": [\n \"nyse:rl\"\n ]\n }\n }\n ]\n}", "language": "http" }, { "code": "curl -X POST \"https://data.thinknum.com/datasets/store/query/\" \\\n-H \"Accept: application/json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\" \\\n-d $'{\n \"tickers\": [\n \"nyse:jwn\"\n ],\n \"functions\": [\n {\n \"function\": \"nearby\",\n \"parameters\": {\n \"dataset\": \"store\",\n \"dataset_type\": \"dataset\",\n \"distance\": 5,\n \"entities\": [],\n \"is_include_closed\": false,\n \"tickers\": [\n \"nyse:rl\"\n ]\n }\n }\n ]\n}'\n", "language": "curl" }, { "code": "response = requests.post(\n url='https://data.thinknum.com/datasets/store/query/', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/json'\n }, \n data=json.dumps({\n \"tickers\": [\n \"nyse:jwn\"\n ],\n \"functions\": [\n {\n \"function\": \"nearby\",\n \"parameters\": {\n \"dataset\": \"store\",\n \"dataset_type\": \"dataset\",\n \"distance\": 5,\n \"entities\": [],\n \"is_include_closed\": false,\n \"tickers\": [\n \"nyse:rl\"\n ]\n }\n }\n ]\n })\n)\nresults = json.loads(response.text)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "{\n \"state\": \"running\",\n \"total\": 0,\n \"id\": \"33afc36bb52fcb5e26aaf94ddfb2c91341a2a673bb1210341dcc1764a955c9cd\",\n \"formats\": [\n \"application/vnd.thinknum.table+json\",\n \"application/vnd.thinknum.map+json\"\n ]\n}", "language": "json" } ] } [/block] To check if query is done: [block:code] { "codes": [ { "code": "HEAD datasets/store/query/33afc36bb52fcb5e26aaf94ddfb2c91341a2a673bb1210341dcc1764a955c9cd HTTP/1.1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\nAccept: application/vnd.thinknum.table+json\nContent-Type: application/x-www-form-urlencoded; charset=utf-8", "language": "http" }, { "code": "curl -I HEAD \"https://data.thinknum.com/datasets/store/query/33afc36bb52fcb5e26aaf94ddfb2c91341a2a673bb1210341dcc1764a955c9cd\" \\\n-H \"Accept: application/vnd.thinknum.table+json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\" ", "language": "curl" }, { "code": "response = requests.head(\n url='https://data.thinknum.com/datasets/store/query/33afc36bb52fcb5e26aaf94ddfb2c91341a2a673bb1210341dcc1764a955c9cd', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/vnd.thinknum.table+json'\n }\n)\nresults = dict(response.headers)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "HTTP/1.1 200 OK\nServer: nginx\nDate: Wed, 28 Apr 2021 16:23:07 GMT\nContent-Type: application/json\nConnection: keep-alive\nX-Truncated: false\nX-Formats: application/vnd.thinknum.table+json, application/vnd.thinknum.map+json, text/csv, application/vnd.ms-excel, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\nX-Total: 963\nX-State: complete\nStrict-Transport-Security: max-age=15768000", "language": "json" } ] } [/block] To retrieve data: [block:code] { "codes": [ { "code": "GET /datasets/store/query/33afc36bb52fcb5e26aaf94ddfb2c91341a2a673bb1210341dcc1764a955c9cd?limit=10&start=1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\nAccept: application/vnd.thinknum.table+json\nContent-Type: application/json", "language": "http" }, { "code": "curl -X GET \"https://data.thinknum.com/datasets/store/query/33afc36bb52fcb5e26aaf94ddfb2c91341a2a673bb1210341dcc1764a955c9cd?limit=10&start=1\" \\\n-H \"Accept: application/vnd.thinknum.table+json\" \\\n-H \"Content-Type: application/json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\"\n", "language": "curl" }, { "code": "response = requests.get(\n url='https://data.thinknum.com/datasets/store/query/33afc36bb52fcb5e26aaf94ddfb2c91341a2a673bb1210341dcc1764a955c9cd', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/vnd.thinknum.table+json'\n }\n)\nresults = json.loads(response.text)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "{\n \"group_fields\": [\n {\n \"length\": 320,\n \"options\": [],\n \"display_name\": \"Ticker Symbol\",\n \"format\": \"ticker\",\n \"metric\": false,\n \"type\": \"string\",\n \"id\": \"dataset__entity__entity_ticker__ticker__ticker\",\n \"summary\": \"The full ticker symbol, defined as the financial market of the company, followed by a colon (:), and then the identifier of the company. ex. NASDAQ:AAPL\"\n },\n {\n \"length\": 255,\n \"options\": [],\n \"display_name\": \"Entity Name\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"string\",\n \"id\": \"dataset__entity__name\",\n \"summary\": \"The name of a specific entity belonging to a ticker within this dataset.\"\n },\n ...\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Competition #1\",\n \"format\": null,\n \"metric\": true,\n \"type\": \"number\",\n \"id\": \"$nearby_1@count\",\n \"summary\": \"\"\n }\n ],\n \"total\": 963,\n \"id\": \"33afc36bb52fcb5e26aaf94ddfb2c91341a2a673bb1210341dcc1764a955c9cd\",\n \"count\": 10,\n \"rows\": [\n [\n \"nyse:jwn\",\n \"Nordstrom\",\n ...\n 0\n ],\n ...\n [\n \"nyse:jwn\",\n \"Nordstrom\",\n ...\n 2\n ]\n ],\n \"fields\": [\n {\n \"display_name\": \"Ticker Symbol\",\n \"format\": \"ticker\",\n \"metric\": false,\n \"id\": \"dataset__entity__entity_ticker__ticker__ticker\",\n \"length\": 320,\n \"summary\": \"The full ticker symbol, defined as the financial market of the company, followed by a colon (:), and then the identifier of the company. ex. NASDAQ:AAPL\",\n \"type\": \"string\",\n \"options\": []\n },\n {\n \"display_name\": \"Entity Name\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"dataset__entity__name\",\n \"length\": 255,\n \"summary\": \"The name of a specific entity belonging to a ticker within this dataset.\",\n \"type\": \"string\",\n \"options\": []\n },\n ...\n {\n \"display_name\": \"Competition #1\",\n \"format\": null,\n \"metric\": true,\n \"id\": \"$nearby_1@count\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n }\n ],\n \"sort_fields\": [],\n \"start\": 1,\n \"state\": \"complete\",\n \"limit\": 10,\n \"last_date_updated\": \"2021-03-26T16:00:35Z\"\n}", "language": "json" } ] } [/block] [block:api-header] { "title": "Nearest Competitor" } [/block] While the Nearby Competitors function returns a count of the competitors within a specified distance, the Nearest Competitor returns the closest competitor for each item in the query. The Nearest Competitor function has the following parameters: [block:parameters] { "data": { "h-0": "Parmeter", "0-0": "dataset", "h-1": "Description", "h-2": "Required?", "1-0": "tickers", "2-0": "entities", "0-1": "The dataset of the competitors", "0-2": "Yes", "1-1": "List of competitor tickers", "1-2": "No, if entities is defined;\nyes, otherwise.", "2-1": "List of competitor entities", "2-2": "No, if tickers is defined;\nyes, otherwise." }, "cols": 3, "rows": 3 } [/block] For example, if you are analyzing the "Store Locations" dataset, and you would like to see how close each Nordstrom store is to a Ralph Lauren store, you can run the following query: [block:code] { "codes": [ { "code": "POST /dataset/store/query/ HTTP/1.1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\n\n{\n \"tickers\": [\n \"nyse:jwn\"\n ],\n \"functions\": [\n {\n \"function\": \"nearest\",\n \"parameters\": {\n \"dataset_type\": \"dataset\",\n \"dataset\": \"store\",\n \"tickers\": [\n \"nyse:rl\"\n ],\n \"entities\": [],\n \"ranks\": [\n 1\n ],\n \"is_include_closed\": false\n }\n }\n ]\n}", "language": "http" }, { "code": "curl -X POST \"https://data.thinknum.com/datasets/store/query/\" \\\n-H \"Accept: application/json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\" \\\n-d $'{\n \"tickers\": [\n \"nyse:jwn\"\n ],\n \"functions\": [\n {\n \"function\": \"nearest\",\n \"parameters\": {\n \"dataset_type\": \"dataset\",\n \"dataset\": \"store\",\n \"tickers\": [\n \"nyse:rl\"\n ],\n \"entities\": [],\n \"ranks\": [\n 1\n ],\n \"is_include_closed\": false\n }\n }\n ]\n}'\n", "language": "curl" }, { "code": "response = requests.post(\n url='https://data.thinknum.com/datasets/store/query/', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/json'\n }, \n data=json.dumps({\n \"tickers\": [\n \"nyse:jwn\"\n ],\n \"functions\": [\n {\n \"function\": \"nearest\",\n \"parameters\": {\n \"dataset_type\": \"dataset\",\n \"dataset\": \"store\",\n \"tickers\": [\n \"nyse:rl\"\n ],\n \"entities\": [],\n \"ranks\": [\n 1\n ],\n \"is_include_closed\": false\n }\n }\n ]\n })\n)\nresults = json.loads(response.text)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "{\n \"state\": \"complete\",\n \"total\": 963,\n \"id\": \"dfe76829a25074868ee8018ac2c2d0cdc074c3e86ec80f9897ee41570950f441\",\n \"formats\": [\n \"application/vnd.thinknum.table+json\",\n \"application/vnd.thinknum.map+json\"\n ]\n}", "language": "json" } ] } [/block] To check if query is completed: [block:code] { "codes": [ { "code": "HEAD datasets/store/query/dfe76829a25074868ee8018ac2c2d0cdc074c3e86ec80f9897ee41570950f441 HTTP/1.1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\nAccept: application/vnd.thinknum.table+json\nContent-Type: application/x-www-form-urlencoded; charset=utf-8", "language": "http" }, { "code": "curl -I HEAD \"https://data.thinknum.com/datasets/store/query/dfe76829a25074868ee8018ac2c2d0cdc074c3e86ec80f9897ee41570950f441\" \\\n-H \"Accept: application/vnd.thinknum.table+json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\"", "language": "curl" }, { "code": "response = requests.head(\n url='https://data.thinknum.com/datasets/store/query/dfe76829a25074868ee8018ac2c2d0cdc074c3e86ec80f9897ee41570950f441', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/vnd.thinknum.table+json'\n }\n)\nresults = dict(response.headers)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "HTTP/1.1 200 OK\nServer: nginx\nDate: Wed, 28 Apr 2021 16:29:50 GMT\nContent-Type: application/json\nConnection: keep-alive\nX-Truncated: false\nX-Formats: application/vnd.thinknum.table+json, application/vnd.thinknum.map+json, text/csv, application/vnd.ms-excel, application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\nX-Total: 963\nX-State: complete\nStrict-Transport-Security: max-age=15768000", "language": "json" } ] } [/block] To retrieve data: [block:code] { "codes": [ { "code": "GET /datasets/store/query/dfe76829a25074868ee8018ac2c2d0cdc074c3e86ec80f9897ee41570950f441?limit=10&start=1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\nAccept: application/vnd.thinknum.table+json\nContent-Type: application/json", "language": "http" }, { "code": "curl -X GET \"https://data.thinknum.com/datasets/store/query/dfe76829a25074868ee8018ac2c2d0cdc074c3e86ec80f9897ee41570950f441?limit=10&start=1\" \\\n-H \"Accept: application/vnd.thinknum.table+json\" \\\n-H \"Content-Type: application/json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\"\n", "language": "curl" }, { "code": "response = requests.get(\n url='https://data.thinknum.com/datasets/store/query/dfe76829a25074868ee8018ac2c2d0cdc074c3e86ec80f9897ee41570950f441', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/vnd.thinknum.table+json'\n }\n)\nresults = json.loads(response.text)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "{\n \"group_fields\": [\n {\n \"length\": 320,\n \"options\": [],\n \"display_name\": \"Ticker Symbol\",\n \"format\": \"ticker\",\n \"metric\": false,\n \"type\": \"string\",\n \"id\": \"dataset__entity__entity_ticker__ticker__ticker\",\n \"summary\": \"The full ticker symbol, defined as the financial market of the company, followed by a colon (:), and then the identifier of the company. ex. NASDAQ:AAPL\"\n },\n {\n \"length\": 255,\n \"options\": [],\n \"display_name\": \"Entity Name\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"string\",\n \"id\": \"dataset__entity__name\",\n \"summary\": \"The name of a specific entity belonging to a ticker within this dataset.\"\n },\n ...\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Store #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.rank\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Ticker #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.ticker\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Entity #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.entity\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Distance #1\",\n \"format\": null,\n \"metric\": true,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.dist\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Store Name #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.storefront_name\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Store Street #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.street\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Store City #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.city\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Store State #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.state\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Store Country #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.country\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Store Postal Code #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"number\",\n \"id\": \"$nearest_1_1.postal_code\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Nearest Store Is Closed #1\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"boolean\",\n \"id\": \"$nearest_1_1.is_closed\",\n \"summary\": \"\"\n }\n ],\n \"total\": 963,\n \"id\": \"dfe76829a25074868ee8018ac2c2d0cdc074c3e86ec80f9897ee41570950f441\",\n \"count\": 10,\n \"rows\": [\n [\n \"nyse:jwn\",\n \"Nordstrom\",\n ...\n 1,\n \"nyse:rl\",\n \"Ralph Lauren\",\n 29.2719080003088,\n \"RALPH LAUREN\",\n \"Houston Galleria, 5015 Westheimer\",\n \"Houston, TX\",\n \"TX\",\n \"USA\",\n \"77056\",\n false\n ],\n ...\n [\n \"nyse:jwn\",\n \"Nordstrom\",\n ...\n 1,\n \"nyse:rl\",\n \"Ralph Lauren\",\n 41.8697883953577,\n \"POLO RALPH LAUREN FACTORY STORE\",\n \"Edinburgh Premium Outlets, 3026 Outlet Drive, Suite G - 040\",\n \"Edinburgh, IN\",\n \"IN\",\n \"USA\",\n \"46124\",\n false\n ]\n ],\n \"fields\": [\n {\n \"display_name\": \"Ticker Symbol\",\n \"format\": \"ticker\",\n \"metric\": false,\n \"id\": \"dataset__entity__entity_ticker__ticker__ticker\",\n \"length\": 320,\n \"summary\": \"The full ticker symbol, defined as the financial market of the company, followed by a colon (:), and then the identifier of the company. ex. NASDAQ:AAPL\",\n \"type\": \"string\",\n \"options\": []\n },\n {\n \"display_name\": \"Entity Name\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"dataset__entity__name\",\n \"length\": 255,\n \"summary\": \"The name of a specific entity belonging to a ticker within this dataset.\",\n \"type\": \"string\",\n \"options\": []\n },\n ...\n {\n \"display_name\": \"Nearest Store #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.rank\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Ticker #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.ticker\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Entity #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.entity\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Distance #1\",\n \"format\": null,\n \"metric\": true,\n \"id\": \"$nearest_1_1.dist\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Store Name #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.storefront_name\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Store Street #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.street\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Store City #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.city\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Store State #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.state\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Store Country #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.country\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Store Postal Code #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.postal_code\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Nearest Store Is Closed #1\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$nearest_1_1.is_closed\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"boolean\",\n \"options\": []\n }\n ],\n \"sort_fields\": [],\n \"start\": 1,\n \"state\": \"complete\",\n \"limit\": 10,\n \"last_date_updated\": \"2021-03-26T16:00:35Z\"\n}", "language": "json" } ] } [/block] [block:api-header] { "title": "Sales Analyzer" } [/block] This is a function that can be applied to "Car Inventory" dataset to see which cars have been sold. Since each vehicle has a unique identification number, users are able to see when a vehicle is added or removed. Tracking the number of vehicles leaving inventory allows users to predict sales figures The sales function has the following parameters: [block:parameters] { "data": { "0-0": "lookahead_day_count", "1-0": "start_date", "2-0": "end_date", "h-0": "Parameter", "h-1": "Description", "h-2": "Required", "0-2": "Yes", "0-1": "The number of days in the future that will be checked to see if same VIN returns", "1-2": "No", "2-2": "No", "1-1": "Start date", "2-1": "End date" }, "cols": 3, "rows": 1 } [/block] For example, if you are analyzing the "Car Inventory" dataset, and you would like to see which car have been sold from Carvana, you can run the following query: [block:code] { "codes": [ { "code": "POST /dataset/store/query/ HTTP/1.1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\n\n{\n \"tickers\": [\n \"nyse:cvna\"\n ],\n \"filters\": [\n {\n \"column\": \"as_of_date\",\n \"type\": \"[]\",\n \"value\": [\n \"2021-01-01\",\n \"2021-01-31\"\n ]\n }\n ],\n \"functions\": [\n {\n \"function\": \"sales\",\n \"parameters\": {\n \"lookahead_day_count\": 2\n }\n }\n ]\n}", "language": "http" }, { "code": "curl -X POST \"https://data.thinknum.com/datasets/store/query/\" \\\n-H \"Accept: application/json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\" \\\n-d $'{\n \"tickers\": [\n \"nyse:cvna\"\n ],\n \"filters\": [\n {\n \"column\": \"as_of_date\",\n \"type\": \"[]\",\n \"value\": [\n \"2021-01-01\",\n \"2021-01-31\"\n ]\n }\n ],\n \"functions\": [\n {\n \"function\": \"sales\",\n \"parameters\": {\n \"lookahead_day_count\": 2\n }\n }\n ]\n}'\n", "language": "curl" }, { "code": "response = requests.post(\n url='https://data.thinknum.com/datasets/store/query/', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/json'\n }, \n data=json.dumps({\n \"tickers\": [\n \"nyse:cvna\"\n ],\n \"filters\": [\n {\n \"column\": \"as_of_date\",\n \"type\": \"[]\",\n \"value\": [\n \"2021-01-01\",\n \"2021-01-31\"\n ]\n }\n ],\n \"functions\": [\n {\n \"function\": \"sales\",\n \"parameters\": {\n \"lookahead_day_count\": 2\n }\n }\n ]\n })\n)\nresults = json.loads(response.text)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "{\n \"state\": \"running\",\n \"total\": 0,\n \"id\": \"bb6a3c405cbe2fff1c5522892aecabf7a7a8b83549d3567ce16d4146cc930d23\",\n \"formats\": [\n \"application/vnd.thinknum.table+json\"\n ]\n}", "language": "json" } ] } [/block] To check if query is completed: [block:code] { "codes": [ { "code": "HEAD datasets/store/query/bb6a3c405cbe2fff1c5522892aecabf7a7a8b83549d3567ce16d4146cc930d23 HTTP/1.1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\nAccept: application/vnd.thinknum.table+json\nContent-Type: application/x-www-form-urlencoded; charset=utf-8", "language": "http" }, { "code": "curl -I HEAD \"https://data.thinknum.com/datasets/store/query/bb6a3c405cbe2fff1c5522892aecabf7a7a8b83549d3567ce16d4146cc930d23\" \\\n-H \"Accept: application/vnd.thinknum.table+json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\"", "language": "curl" }, { "code": "response = requests.head(\n url='https://data.thinknum.com/datasets/store/query/bb6a3c405cbe2fff1c5522892aecabf7a7a8b83549d3567ce16d4146cc930d23', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/vnd.thinknum.table+json'\n }\n)\nresults = dict(response.headers)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "HTTP/1.1 200 OK\nServer: nginx\nDate: Thu, 29 Apr 2021 17:53:59 GMT\nContent-Type: application/json\nConnection: keep-alive\nX-Truncated: true\nX-Formats: application/vnd.thinknum.table+json, text/csv\nX-Total: 500000\nX-State: complete\nStrict-Transport-Security: max-age=15768000", "language": "json" } ] } [/block] To retrieve data: [block:code] { "codes": [ { "code": "GET /datasets/store/query/bb6a3c405cbe2fff1c5522892aecabf7a7a8b83549d3567ce16d4146cc930d23?limit=10&start=1\nAuthorization: token 01234567890123456789\nX-API-Version: 20151130\nAccept: application/vnd.thinknum.table+json\nContent-Type: application/json", "language": "http" }, { "code": "curl -X GET \"https://data.thinknum.com/datasets/store/query/bb6a3c405cbe2fff1c5522892aecabf7a7a8b83549d3567ce16d4146cc930d23?limit=10&start=1\" \\\n-H \"Accept: application/vnd.thinknum.table+json\" \\\n-H \"Content-Type: application/json\" \\\n-H \"X-API-Version: 20151130\" \\\n-H \"Authorization: token 01234567890123456789\"", "language": "curl" }, { "code": "response = requests.get(\n url='https://data.thinknum.com/datasets/store/query/bb6a3c405cbe2fff1c5522892aecabf7a7a8b83549d3567ce16d4146cc930d23', \n headers={\n 'Authorization': 'token 01234567890123456789', \n 'X-API-Version': '20151130', \n 'Accept': 'application/vnd.thinknum.table+json'\n }\n)\nresults = json.loads(response.text)", "language": "python" } ] } [/block] A sample response follows: [block:code] { "codes": [ { "code": "{\n \"group_fields\": [\n {\n \"length\": 320,\n \"options\": [],\n \"display_name\": \"Ticker Symbol\",\n \"format\": \"ticker\",\n \"metric\": false,\n \"type\": \"string\",\n \"id\": \"dataset__entity__entity_ticker__ticker__ticker\",\n \"summary\": \"The full ticker symbol, defined as the financial market of the company, followed by a colon (:), and then the identifier of the company. ex. NASDAQ:AAPL\"\n },\n ...\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Is Sold\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"boolean\",\n \"id\": \"is_sold\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Sold (2 Lookahead Days)\",\n \"format\": null,\n \"metric\": false,\n \"type\": \"boolean\",\n \"id\": \"$sales_1#sold\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Days filled\",\n \"format\": null,\n \"metric\": true,\n \"type\": \"number\",\n \"id\": \"$sales_1#days_filled\",\n \"summary\": \"\"\n },\n {\n \"length\": null,\n \"options\": [],\n \"display_name\": \"Days since posted\",\n \"format\": null,\n \"metric\": true,\n \"type\": \"number\",\n \"id\": \"$sales_1#days_since_posted\",\n \"summary\": \"\"\n }\n ],\n \"total\": 500000,\n \"id\": \"58caab53e58c7a4affafb703cc693065cff94043f21b5ba7814fce0a575cd305\",\n \"count\": 10,\n \"rows\": [\n [\n \"nyse:cvna\",\n ...\n null,\n false,\n null,\n 89\n ],\n ...\n [\n \"nyse:cvna\",\n ...\n null,\n false,\n null,\n 89\n ]\n ],\n \"fields\": [\n {\n \"display_name\": \"Ticker Symbol\",\n \"format\": \"ticker\",\n \"metric\": false,\n \"id\": \"dataset__entity__entity_ticker__ticker__ticker\",\n \"length\": 320,\n \"summary\": \"The full ticker symbol, defined as the financial market of the company, followed by a colon (:), and then the identifier of the company. ex. NASDAQ:AAPL\",\n \"type\": \"string\",\n \"options\": []\n },\n ...\n {\n \"display_name\": \"Is Sold\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"is_sold\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"boolean\",\n \"options\": []\n },\n {\n \"display_name\": \"Sold (2 Lookahead Days)\",\n \"format\": null,\n \"metric\": false,\n \"id\": \"$sales_1#sold\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"boolean\",\n \"options\": []\n },\n {\n \"display_name\": \"Days filled\",\n \"format\": null,\n \"metric\": true,\n \"id\": \"$sales_1#days_filled\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n },\n {\n \"display_name\": \"Days since posted\",\n \"format\": null,\n \"metric\": true,\n \"id\": \"$sales_1#days_since_posted\",\n \"length\": null,\n \"summary\": \"\",\n \"type\": \"number\",\n \"options\": []\n }\n ],\n \"sort_fields\": [\n {\n \"column\": \"as_of_date\",\n \"order\": \"desc\"\n }\n ],\n \"start\": 1,\n \"state\": \"complete\",\n \"limit\": 10,\n \"last_date_updated\": \"2021-04-25T00:07:33Z\"\n}", "language": "json" } ] } [/block]