-

// # Reading in Data

// The first step in any data processing is getting the data!

// Here is how to parse in and prepare common input formats using D3.js

// ## Parsing CSV Files

// [D3 has a bunch](https://github.com/mbostock/d3/wiki/Requests) of filetypes it can support when loading data, and one of the most common is probably plain old CSV (comma separated values).

// Let's say you had a csv file with some city data in it:
//

// ```
// cities.csv:
//
//city,state,population,land area
//seattle,WA,652405,83.9
//new york,NY,8405837,302.6
//boston,MA,645966,48.3
//kansas city,MO,467007,315.0
// ```

// Use [d3.csv](https://github.com/mbostock/d3/wiki/CSV) to convert it into an array of objects

d3.csv("/data/cities.csv", function(data) {
  console.log(data[0]);
});
// ```
//=> {city: "seattle", state: "WA", population: "900000", square miles: "12.3"}
// ```
//
// You can see that the headers of the original CSV have been used as the property names for the data objects. 
// Thus, using `d3.csv` in this manner requires that your CSV file has a header row.