Data Trends for Investment Professionals


Introduction to Quandl’s Libraries

Quandl aims to give you whatever data you need, in whatever form you need. Stock prices, economic data, commodity futures; R, Python, Excel or JSON API – you choose, and we deliver.

Let’s say you want Facebook’s stock price. The Quandl code for this dataset is WIKI/FB.  To download the data from the Quandl website in CSV, XML or JSON format, just go to and click the red download button.

If you’re a programmer, you can get the same data using the Quandl API. All you have to do is type this into your terminal:


This has the advantage that you can easily loop through, not just FB, but 1000s of other stock tickers.

But most data analysts are not programmers, nor do they want to be. They prefer to get data into the analysis tool of their choice, whether that’s R or Python or Excel or Matlab. Quandl makes that easy.

To get the exact same dataset into Python, you simply have to run this piece of code:

import Quandl
mydata = Quandl.get("WIKI/FB")

If you prefer R, do this:

mydata = Quandl("WIKI/FB")

Ruby is equally simple:

require 'quandl'

In Excel, there’s a QDATA spreadsheet function which grabs a single data point:


And there’s the DOWNLOAD button in the Quandl Excel ribbon which grabs a whole time series.

The above code snippets are just a small sample of the many integrations available on Quandl. We currently have over 25 libraries, for R, Python, Matlab, Excel, Ruby, Stata, Maple, Mathematica, C#, C++, Julia, Clojure, Haskell, PHP and many more. We programmed a few of them ourselves; the wonderful Quandl community programmed the rest.

Check out our full list of integrations here. Here is the documentation for Python, R and Excel.

All our libraries are completely free. Happy data hunting!

Fix This
Created with Sketch.