| <cross-domain-policy> | |
| <allow-access-from domain="*.brightcove.com" secure="false"></allow-access-from> | |
| <allow-http-request-headers-from domain="*.brightcove.com" headers="*" secure="false"></allow-http-request-headers-from> | |
| </cross-domain-policy> |
| function bar_chart(height,width,data,mod) { | |
| var height = 220, | |
| width = 400; | |
| function my() { | |
| var bar_chart = d3.select("body").append("svg") | |
| .attr("id", "mod-"+mod+"-bar-chart") | |
| .attr("class", "bar_chart") | |
| .attr("width", width) | |
| .attr("height", height) |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>bar chart example</title> | |
| <meta charset="utf-8"> | |
| <script src="http://d3js.org/d3.v3.min.js"></script> | |
| <script src="../example_d3/reusable_bar_chart.js"></script> | |
| <style> |
| #! /usr/bin/env ruby | |
| require 'csv' | |
| # step 1: import sproducts into an array | |
| @file = "<your path and file>" | |
| @sproducts = [] | |
| CSV.foreach(@file, :headers => false) do |row| | |
| @sproducts << row[0] |
| #! /usr/bin/env ruby | |
| require 'logger' | |
| require 'net/http' | |
| require 'mysql2' | |
| require 'gibbon' | |
| @log = Logger.new("/mailchimp_sender_log.log", 5, 1024000) | |
| @log.level = Logger::INFO |
| import openpyxl | |
| wb = openpyxl.Workbook() | |
| wb.guess_types = True | |
| sheet = wb.active | |
| with open("test_data.csv") as f: | |
| reader = csv.reader(f) | |
| sheet['D48'].number_format = '#,##0' | |
| sheet['M48'].number_format = '($#,##0.00_);[Red]($#,##0.00)' |
This script provides an example of learning a decision tree with scikit-learn. Pandas is used to read data and custom functions are employed to investigate the decision tree after it is learned. Grab the code and try it out.
A blog post about this code is available here, check it out!