CorrelationBot is a simple way to find patterns in your data. It speaks HTTP, and has just one endpoint.
CorrelationBot speaks JSON over HTTP at http://correlationbot.com
. Use GET
for instructions, POST
for results. Enjoy!
>>> import json >>> import requests >>> headers = {'Content-type': 'application/json', } >>> resp = requests.post("http://correlationbot.com", headers=headers, data=json.dumps({ "data": [ [1, 2, 3], [412, 5, 6], [45, -125, 6.334], # Or any number of equally-sized columns. ] }) >>> print resp.json() { "correlations": [ { "column_1": 1, "column_2": 2, "correlation": -0.86495821895559954, "pearson": -0.86495821895559954, # As you might guess, spearman, kendall, and others coming soon. }, { "column_1": 1, "column_2": 3, "correlation": -0.21695628247970969, "pearson": -0.21695628247970969, }, { "column_1": 2, "column_2": 3, "correlation": 0.67754874098457696, "pearson": 0.67754874098457696, } ] ]
$ curl -X POST -H "Content-Type: application/json" -d '{"data": [[1,2,3,4,6,7,8,9],[2,4,6,8,10,12,13,15]]}' http://correlationbot.com {"correlations": [{"pearson": 0.99535001355530017, "column_2": 2, "column_1": 1, "correlation": 0.99535001355530017}]}
CorrelationBot was made by Steven Skoczen in the GreenKahuna Skunkworks. Send us a note if you enjoy it! It source code lives on github, and is MIT licensed.