Using the Quandl Website
This document is a comprehensive guide to using the Quandl website. It teaches you how to find the data you need; how to get the data in the format you want; and how to make the best use of Quandl's many data-handling features.
Datasets and Sources
The basic unit of data in Quandl is the “dataset”. Here are some examples of datasets:
- GDP of the United States
- Inflation in China
- AAPL stock price
- Kansas City wheat futures
- Deforestation in Brazil
Quandl currently handles only time-series datasets, i.e., datasets where the first column is a date. We don’t handle intraday data yet. Nor do we handle non-numerical data.
Each dataset on Quandl is associated with a “source”. Sources can be data publishers, curators, contributors, vendors or individual users. Here are some examples of popular sources on Quandl:
Each dataset on Quandl has a unique "Quandl code", comprising a source code and a table code. For instance, the dataset named GDP of the United States has the Quandl code FRED/GDP, where FRED is the source code and GDP is the table code. All datasets from the same source will have the same source code.
There are several ways to find datasets on Quandl. The first is to use “Search”.
Quandl search works much like Google or any other search engine. Type in a word or phrase that describes what you’re looking for, and Quandl will try to return the most relevant datasets for your query. Quandl looks at dataset names, descriptions, column headers, Quandl codes, source names, hidden keywords and popularity when determining relevance.
Individual search results on Quandl include rich metadata about each dataset returned:
You can use this metadata to decide which dataset(s) you're interested in.
More About Search
By default, Quandl searches all data sources and all data frequencies. But you can narrow down your search results using the filters on the left. If the source you are interested in does not appear in the filter list, simply type its name or source code into the “Add Source” text box, and Quandl will add it to the list.
Quandl limits its search results to display at most two results from any one source. To view more results from a particular source, click on “Show all results from [source]”. (Note that this is equivalent to selecting that particular source in the filter list on the left).
In addition to dataset results, Quandl shows a few "collection" results. Collections are groups of datasets on specific topics, hand-selected by Quandl curators for quality, accuracy and relevance. In many cases, collections will lead to better (i.e. more useful) datasets than basic search.
You can see all the collections pertaining to your search query by clicking on “Show more collections”.
Sometimes basic search is not sufficient to find the dataset you need. In that case, you can use Quandl’s “Advanced Search”.
Clicking on “Advanced Search” loads a new window where you can customize your search query. Note that you can access the same functionality within Quandl’s main search interface, using the "AND", "OR" and "NOT" operators.
For example, searching for:
crude OR refined AND oil NOT canola
will return results that definitely contain the word "oil", may contain either, both or none of the words "crude" and "refined", and definitely do not contain the word "canola".
In place of "NOT", you can also use the minus operator "-".
You can search for datasets with specific table codes by prefacing the string "code:" to your query. For example, searching for:
will return datasets whose Quandl codes exactly match the string UNRATE.
You can use the wild-card operators "?" and "*" anywhere in your query string. This is especially useful when searching for datasets by partial code match. For example, searching for:
will return all datasets whose Quandl codes contain the string YBHA.
Each and every dataset on Quandl has its own page. "Dataset Pages" have unique URLs in this format:
So if you know a dataset’s Quandl code, you can quickly jump to its dataset page on quandl.com.
Quandl dataset pages share a common layout:
The next few sub-sections describe how to use various features on the dataset page.
Download and Export
Simply click the red "Download" button to get the raw data directly onto your computer, in whatever format you want. Formats currently offered include CSV, XLS, JSON and XML. If there's some other format you'd like us to implement, please email us.
Every dataset on Quandl is available through a single, consistent, unlimited and free API for numerical data. Click on any of the API buttons - JSON, CSV, XML - to see the custom API call for the dataset you're viewing.
In addition to its API, Quandl offers libraries for third-party analysis tools including Python, R, Matlab and Stata. You can use these libraries to get the data you need directly into the tool of your choice; simply click on the relevant button to see the required call.
You can export Quandl data directly to web-based graphing tool Plotly by clicking on the Plotly button. This will open a Plotly page in a new browser tab.
Finally, you can download any dataset directly from within Microsoft Excel, using our free Excel add-in.
Select Series To Plot
For datasets with more than one data column (such as NSE/OIL but not FRED/GDP) you can select which column to plot by simply clicking on the column name, above the graph. You can plot as many columns as you want, at the same time. Quandl automatically adds a secondary Y-axis when necessary.
Hover your mouse over any point on the graph to see the exact date and value for that point.
By default, Quandl graphs all the data available for any dataset. But you can change the displayed date range using the slider control below the graph.
View Full History
Double click anywhere on the slider below the graph to view the full history for your dataset (i.e. to expand the date range to its maximum).
Click on the date buttons to restrict your view to the last 5 days, 3 months, 6 months, 1 year, 3 years or 5 years of data. Or click on "Max" to see all the available data.
Custom Date Range
Type in specific dates into the two date boxes to set a custom date range.
If you have (say) a daily dataset, you can use this control to convert it to a monthly, quarterly or annual dataset. Similarly, you can collapse monthly data to quarterly or annual. But you cannot go in the opposite direction: you cannot convert annual data to monthly or daily.
Note that Quandl always takes the “last available” high-frequency observation when converting from high frequency to low. Thus this function does not work well for datasets that measure percentage changes, period averages or period extremes (highs and lows).
More About Frequency
By default, Quandl displays date stamps that are precisely what the original publisher used. You can change the frequency to anything you want. When you select a frequency, Quandl does two things:
It generates all the dates that should appear between start date and end date given the frequency you have chosen. So, for example, if the data is daily and goes from January 15, 2012 to August 19 2012 and you select "monthly" frequency, then Quandl generates the dates: Jan 31, Feb 28, March 31, ... July 31, Aug 19.
Quandl then selects the last observation for each period as the data value for that month. So, if there is an observation on January 31, it will be used as the January data point. If the last observation in January occurred on January 28th, then that will be the data point used for January. The last observation for August occurs on the 19th, so that will be the value used for August.
Question: What happens if the dataset is originally monthly and I change the frequency to monthly?
Answer: The process above is still followed. This means that you will now see when data is missing. If there is actually no data point for June 2007 you might not see a gap in the original data. On one line you will see May 2007 and on the next you will see July 2007. But when you choose monthly as the frequency, missing data points will become apparent. You will see a date stamp of June 2007 and the data will be noticeably missing.
Use this control to carry out elementary transformations on your data: row-on-row change, percentage change and cumulative sum.
Note that when you download data, the downloaded file will reflect whatever X-axis date range, frequency and transformation you have previously selected using the above controls. However this does NOT apply to the date slider, only to the button, datebox and dropdown controls.
This offers a quick view of the most recent observations for the dataset. Empty columns are marked "N/A".
Click on the "Show Data Table" button to see the full raw data, updated fresh from the source, and presented to you in a canonical format irrespective of how and where it was originally published.
Advanced tip: Click on any column header to toggle the date order between ascending and descending.
This has useful information about the dataset you're looking at, including: how recently Quandl retreived data from the source, a link to help validate the data, a permalink to the data on Quandl, and a brief description.
This shows datasets and data collections on Quandl that are related to the page you're viewing.
Use this button to report problems with a dataset: missing data, bad data, attribution errors, incorrect or incomplete descriptions, or any other issues you might spot. Quandl relies on user contributions for its coverage and accuracy; thank you for taking the trouble to report data problems!
To add a dataset to your favourites, simply click on the "Favourite" star. Note that you need to be signed in in order to use this feature.
To view all your favourites, click on your username in the top navigation bar and then select "Favourites" in the dropdown.
To remove a dataset from your favourites, simply uncheck the "Favourite" star on that dataset's page. Alternatively, go to your list of favourites and uncheck the star there.
You need to be signed in to your Quandl account in order to use favourites. Getting a Quandl account is completely free and takes just a few seconds!
You can merge any 2 or more of our 10,000,000+ datasets to create a brand-new, persistent dataset that updates automatically when its consituents update. We call these merged datasets "supersets".
Supersets function just like any other dataset: you can transform, collapse, trim, download, or API-access a superset just like you would any other Quandl dataset.
To create a superset, you use the Quandl Toolbelt. To create a superset, you simply define it in a text file and send the definition to Quandl using toolbelt. For example, create a file called my_ss.qdf (the file can have any name) that looks like this:
code: SS1 name: Gold and Silver Prices description: Current gold and silver prices in USD. column_codes: - BUNDESBANK.BBK01_WT5511.1 - OFDP.SILVER_5.1 column_names: - Gold Price - Silver Price
Then send this superset to Quandl:
$ quandl superset upload my_ss.qdf
You have now created this superset. If you need to change SS1 in future, just edit the text file and repeat the upload. You don't actually have to keep a copy of the file. You can pull it down from Quandl if you need to:
$ quandl superset download SS1 > my_ss.qdf
Collections are groups of related datasets on specific subjects curated by Quandl users. Here are some examples of popular collections on Quandl:
Collections offer an easy way to find high-quality data on the subject of your interest. You can browse through all of our 75,000+ data collections from our collection index.
Quandl is best accessed by the Chrome, Firefox, and Safari browsers; for Internet Explorer it works with version 9 and higher. If you're not using a recommended browser and are experiencing issues throughout the site it may be due to incompatibilities.
If you are still encountering issues, please email us at email@example.com.