The good

• I can import data directly from R/Python code without having to go near the ONS website. It’s at easy as Quandl.get('UKONS/ABMI') and I have a properly indexed dataframe ready to go.
• Once I’ve written the import I can re-run the code and it automatically pulls the latest data so I never need to worry about when the last release was. People lucky enough to have Bloomberg/Datastream probably don’t think this is a big deal but I’m not one of those people.
• The Quandl maintainers are incredibly good at adding new data sources and you can even upload your own datasets now.
• It charts the series on the website for you and gives you embeddable charts like this in seconds, without leaving your browser. That is incredibly useful when you want to have a quick look at a series without having to download the data and chart it yourself.

The frustrating

• Quandl are great at adding datasets and great at aggregating data but there’s very little organisation of it so far. For instance, the UK’s GDP series is available from the ONS in three different datasets at two different frequencies and in various forms (raw, SA, %age change, etc). Quandl hosts them all, which is great, but doesn’t draw the links between them. For someone who knows the data that’s fine but it could prove very confusing for a novice. The ONS give the series the same code across all releases so perhaps linking the releases is something that could be done in future.
• Searching for data is a nightmare. If I search ‘UK GDP’ there are 17417 pages of results and the official ONS data isn’t even on the first page. Okay, let’s narrow the search by suggested sources to the ONS. Now there are only nine pages of results but the GDP series still aren’t on the first page. Luckily, I know the ONS code for GDP, YBHA, so let’s search on that. Uh-oh, no results! How am I going to find this series?! Well, I can see that the other series are coded by Quandl as UKONS/[dataset]_[ONS code]_[frequency], so let’s try just pulling the series UKONS/QNA_YBHA_Q from a Python interpreter. Success, I’ve got the series! But I still can’t see the metadata, or the webpage, or embed a chart, or do any of the other great website things. Update: Abraham Thomas of Quandl helpfully points out that I could go to the address to get the metadata and the link will be automatically expanded.

As Quandl has grown it’s added data very quickly but it’s now at the point that it’s becoming a bit unwieldy without effective search and organisational tools. I really hope that’s next on the devs list of things to do so it can become the fantastic repository that it has the potential to be.

Thankfully, there is an amazing website that has recently launched called Quandl, which indexes millions of time series from around the world. Importantly, they offer custom-built packages for interacting with their data in R, Python, Stata, Java etc. So now I can grab the UK’s GDP series in Python like this

import Quandl
Quandl.get('UKONS/ABMI')

The first time I did it was great, but then I realised I only had the Blue Book’s annual series and I really needed the quarterly series. Unfortunately Quandl didn’t scrape the Quarterly National Accounts so I thought I was out of luck. I sent an email to their helpdesk asking about adding new series and, incredibly, got an email back from a dev within a day offering to upload all the datasets I could provide urls for. So now, a couple of weeks later, if you want the quarterly series it’s as easy as

Quandl.get('UKONS/QNA_ABMI_Q')

I think this is the beginning of a beautiful friendship for me, but Quandl really need your help. They’re growing fast and keen to expand so, if you know of a dataset they should index, send them an email and make it happen for all of us!

PS. If you’re a Python user you’ll be thrilled to hear that Quandl’s package returns pandas dataframes! You may be surprised at how happy that made me.