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Data expedition tutorial: UK and US video game magazines

Cédric Lombion - February 3, 2015 in Data Cleaning, HowTo, Spreadsheets, Storytelling, Workshop Methods, Блог, Интернационален

Data Pipeline

This article is part tutorial, part demonstration of the process I go through to complete a data expedition alone, or as a participant during a School of Data event. Each of the following steps will be detailed: Find, Get, Verify, Clean, Explore, Analyze, Visualize, Publish

Depending on your data, your source or your tools, the order in which you will be going through these steps might be different. But the process is globally the same.


A data expedition can start from a question (e.g. how polluted are european cities?) or a data set that you want to explore. In this case, I had a question: Has the dynamic of the physical video game magazine market been declining in the past few years ? I have been studying the video game industry for the past few weeks and this is one the many questions that I set myself to answer. Obviously, I thought about many more questions, but it’s generally better to start focused and expand your scope at a later stage of the data expedition.

A search returned Wikipedia as the most comprehensive resource about video game magazines. They even have some contextual info, which will be useful later (context is essential in data analysis).

Screenshot of the Wikipedia table about video game magazines


The wikipedia data is formatted as a table. Great! Scraping it is as simple as using the importHTML function in Google spreadsheet. I could copy/paste the table, but that would be cumbersome with a big table and the result would have some minor formatting issues. LibreOffice and Excel have similar (but less seamless) web import features.

importHTML asks for 3 variables: the link to the page, the formatting of the data (table or list), and the rank of the table (or the list) in the page. If no rank is indicated, as seen below, it will grab the first one.

Once I got the table, I do two things to help me work quicker:

  • I change the font and cell size to the minimum so I can see more at once
  • I copy everything, then go to Edit→Paste Special→Paste values only. This way, the table is not linked to importHTML anymore, and I can edit it at will.


So, will this data really answer my question completely? I do have the basic data (name, founding data, closure date), but is it comprehensive? A double check with the French wikipedia page about video game magazines reveals that many French magazines are missing from the English list. Most of the magazines represented are from the US and the UK, and probably only the most famous. I will have to take this into account going forward.


Editing your raw data directly is never a good idea. A good practice is to work on a copy or in a nondestructive way – that way, if you make a mistake and you’re not sure where, or want to go back and compare to the original later, it’s much easier. Because I want to keep only the US and UK magazines, I’m going to:

  • rename the original sheet as “Raw Data”
  • make a copy of the sheet and name it “Clean Data”
  • order alphabetically the Clean Data sheet according to the “Country” column
  • delete all the lines corresponding to non-UK or US countries.

Making a copy of your data is important

Tip: to avoid moving your column headers when ordering the data, go to Display→Freeze lines→Freeze 1 line.

Ordering the data to clean it

Some other minor adjustments have to be made, but they’re light enough that I don’t need to use a specialized cleaning tool like Open Refine. Those include:

  • Splitting the lines where 2 countries are listed (e.g. PC Gamer becomes PC Gamer UK and PC Gamer US)
  • Delete the ref column, which adds no information
  • Delete one line where the founding data is missing


I call “explore” the phase where I start thinking about all the different ways my cleaned data could answer my initial question[1]. Your data story will become much more interesting if you attack the question from several angles.

There are several things that you could look for in your data:

  • Interesting Factoids
  • Changes over time
  • Personal experiences
  • Surprising interactions
  • Revealing comparisons

So what can I do? I can:

  • display the number of magazines in existence for each year, which will show me if there is a decline or not (changes over time)
  • look at the number of magazines created per year, to see if the market is still dynamic (changes over time)

For the purpose of this tutorial, I will focus on the second one, looking at the number of magazines created per year Another tutorial will be dedicated to the first, because it requires a more complex approach due to the formatting of our data.

At this point, I have a lot of other ideas: Can I determine which year produced the most enduring magazines (surprising interactions)? Will there be anything to see if I bring in video game website data for comparison (revealing comparisons)? Which magazines have lasted the longest (interesting factoid)? This is outside of the scope of this tutorial, but those are definitely questions worth exploring. It’s still important to stay focused, but writing them down for later analysis is a good idea.


Analysing is about applying statistical techniques to the data and question the (usually visual) results.

The quickest way to answer our question “How many magazines have been created each year?” is by using a pivot table.

  1. Select the part of the data that answers the question (columns name and founded)
  2. Go to Data->Pivot Table
  3. In the pivot table sheet, I select the field “Founded” as the column. The founding years are ordered and grouped, allowing us to count the number of magazines for each year starting from the earliest.
  4. I then select the field “Name” as the values. Because the pivot tables expects numbers by default (it tries to apply a SUM operation), nothing shows. To count the number of names associated with each year, the correct operation is COUNTA. I click on SUM and select COUNT A from the drop down menu.

This data can then be visualized with a bar graph.

Video game magazine creation every year since 1981

The trendline seems to show a decline in the dynamic of the market, but it’s not clear enough. Let’s group the years by half-decade and see what happens:

The resulting bar chart is much clearer:

The number of magazines created every half-decade decreases a lot in the lead up to the 2000s. The slump of the 1986-1990 years is perhaps due to a lagging effect of the North american video game crash of 1982-1984

Unlike what we could have assumed, the market is still dynamic, with one magazine founded every year for the last 5 years. That makes for an interesting, nuanced story.


In this tutorial the initial graphs created during the analysis are enough to tell my story. But if the results of my investigations required a more complex, unusual or interactive visualisation to be clear for my public, or if I wanted to tell the whole story, context included, with one big infographic, it would fall into the “visualise” phase.


Where to publish is an important question that you have to answer at least once. Maybe the question is already answered for you because you’re part of an organisation. But if you’re not, and you don’t already have a website, the answer can be more complex. Medium, a trendy publishing platform, only allows images at this point. WordPress might be too much for your need. It’s possible to customize the Javascript of tumblr posts, so it’s a solution. Using a combination of Github Pages and Jekyll, for the more technically inclined, is another. If a light database is needed, take a look at tabletop.js, which allows you to use a google spreadsheet as a quasi-database.

Any data expedition, of any size or complexity, can be approached with this process. Following it helps avoiding getting lost in the data. More often than not, there will be a need to get and analyze more data to make sense of the initial data, but it’s just a matter of looping the process.

[1] I formalized the “explore” part of my process after reading the excellent blog from MIT alumni Rahoul Bhargava

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Web scraping in under 60 seconds: the magic of

Escuela de Datos - December 9, 2014 in HowTo, Scraping, Tech

This post was written by Rubén Moya, School of Data fellow in Mexico, and originally posted on Escuela de Datos. is a very powerful and easy-to-use tool for data extraction that has the aim of getting data from any website in a structured way. It is meant for non-programmers that need data (and for programmers who don’t want to overcomplicate their lives).

I almost forgot!! Apart from everything, it is also a free tool (o_O)

The purpose of this post is to teach you how to scrape a website and make a dataset and/or API in under 60 seconds. Are you ready?

It’s very simple. You just have to go to; post the URL of the site you want to scrape, and push the “GET DATA” button. Yes! It is that simple! No plugins, downloads, previous knowledge or registration are necessary. You can do this from any browser; it even works on tablets and smartphones.

For example: if we want to have a table with the information on all items related to Chewbacca on MercadoLibre (a Latin American version of eBay), we just need to go to that site and make a search – then copy and paste the link ( on, and push the “GET DATA” button.

Screen Shot 2014-12-08 at 20.30.28

You’ll notice that now you have all the information on a table, and all you need to do is remove the columns you don’t need. To do this, just place the mouse pointer on top of the column you want to delete, and an “X” will appear.

Screen Shot 2014-12-08 at 20.31.02

You can also rename the titles to make it easier to read; just click once on the column title.

Screen Shot 2014-12-08 at 20.32.30

Finally, it’s enough for you to click on “download” to get it in a csv file.

Screen Shot 2014-12-08 at 20.33.26

Now: you’ll notice two options – “Download the current page” and “Download # pages”. This last option exists in case you need to scrape data that is spread among different results pages of the same site.

Screen Shot 2014-12-08 at 20.34.21

In our example, we have 373 pages with 48 articles each. So this option will be very useful for us.

Screen Shot 2014-12-08 at 20.35.13

Screen Shot 2014-12-08 at 20.35.18
Good news for those of us who are a bit more technically-oriented! There is a button that says “GET API” and this one is good to, well, generate an API that will update the data on each request. For this you need to create an account (which is also free of cost).


Screen Shot 2014-12-08 at 20.36.07

As you saw, we can scrape any website in under 60 seconds, even if it includes tons of results pages. This truly is magic, no?
For more complex things that require logins, entering subwebs, automatized searches, et cetera, there is downloadable software… But I’ll explain that in a different post.

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User Experience Design – Skillshare

Lucy Chambers - November 28, 2014 in HowTo, User Experience

“User Experience Design is the process of enhancing user satisfaction and loyalty by improving usability, ease of use and pleasure provided in the the interaction between the user and the product.”

This week Siyabonga Africa, one of our fellows in South Africa, led an amazing introduction to how to think about your users when designing a project to make sure they get the most out of it. In case you missed it – you can watch the entire skillshare online and get Siya’s slides.



Where can I learn more?

For more in the skillshare series – keep your eye on the Open Knowledge Google Plus page and follow @SchoolofData.

For more from Siyabonga – poke @siyafrica on Twitter.

Image Credits: Glen Scarborough (CC-BY-SA) .

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Tool Review: WebScraper

Nisha Thompson - October 13, 2014 in Community, HowTo, Resources

Crosspost from

Usually when I have any scraping to do I ask Thej  if he can do it and then take a nap. However, Thej is on vacation so I was stuck either waiting for him to come back or I could try to do it myself. It was basic text, not much html, no images, and a few pages, so I went for it with some non coder tools.

I checked the School of Data scraping section for some tools and they have a nice little section on using browser based scraping tools. I did a chrome store search and came across WebScraper.

I glanced through the video sort of paying attention got the gist of it and started to play with the tool.  It took awhile for me to figure out.  I highly recommend very carefully going through the tutorials.  The videos take you through the process but are not very clear for complete newbies like me so it took a few views to understand the hierarchy concept and how to adapt their example to the site I was scraping.

I got the hang of doing one page and then figuring out how to tell it to go to another page, again I had to spend quite a bit of time rewatching the tutorial. At the end of the day I got the data in neat columns in CSV without too much trouble.  I would recommend WebScraper for people who want to do some basic scraping.

It is as visual as you can get though the terminology is still very technical.   You have to do into the developer tools folder which can feel intimidating but ultimately satisfying in the end.

Though I’ll probably still call Thej.

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Mapping Skillshare with Codrina

Heather Leson - October 10, 2014 in Community, Events, Geocoding, HowTo, Mapping, School_Of_Data

Why maps are useful visualization tools? What doesn’t work with maps? Today we hosted a School of Data skillshare with Codrina Ilie, School of data Fellow.

Codrina Ilie shares perspectives on building a map project

What makes a good map? How can perspective, assumptions and even colour change the quality of the map? This is a one-hour video skillshare to learn all about map making from our School of Data fellow:

Learn some basic mapping skills with slides

Codrina prepared these slides with some extensive notes and resources. We hope that it helps you on your map journey.

Hand drawn map


(Note: the hand drawn map was created at School of Data Summer Camp. Photo by Heather Leson CCBY)

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Breaking the Knowledge Barrier: The #OpenData Party in Northern Nigeria

olubabayemi - October 1, 2014 in Community, Data Expeditions, Data for CSOs, Events, Follow the Money, Geocoding, Mapping, Spreadsheets, Storytelling, Uncategorized, Visualisation

If the only news you have been watching or listening to about Northern Nigeria is of the Boko Haram violence in that region of Nigeria, then you need to know that other news exist, like the non-government organizations and media, that are interested in using the state and federal government budget data in monitoring service delivery, and making sure funds promised by government reach the community it was meant for.

This time around, the #OpenData party moved from the Nigeria Capital – Abuja to Gusau, Zamfara and was held at the Zamfara Zakat and Endowment Board Hall between September Thursday, 25 and Friday, 26, 2014. With 40 participant all set for this budget data expedition, participants included the state Budget Monitoring Group (A coalition of NGOs in Zamfara) coordinated by the DFID (Development for International Development) State Accountability and Voice Initiative (SAVI),other international NGOs such as Society for Family Health (SFH), Save the Children, amongst others.


Group picture of participants at the #OpenData Party in Zamfara

But how do you teach data and its use in a less-technology savvy region? We had to de-mystify teaching data to this community, by engaging in traditional visualization and scraping – which means the use of paper artworks in visualizing the data we already made available on the Education Budget Tracker. “I never believed we could visualize the education budget data of the federal government as easy as what was on the wall” exclaimed Ahmed Ibrahim of SAVI


Visualization of the Education Budget for Federal Schools in Zamfara

As budgets have become a holy grail especially with state government in Nigeria, of most importance to the participants on the first day, was how to find budget data, and processes involved in tracking if services were really delivered, as promised in the budget. Finding the budget data of the state has been a little bit hectic, but with much advocacy, the government has been able to release dataset on the education and health sector. So what have been the challenges of the NGOs in tracking or using this data, as they have been engaged in budget tracking for a while now?

Challenges of Budget Tracking Highlighted by participants

Challenges of Budget Tracking Highlighted by participants

“Well, it is important to note that getting the government to release the data took us some time and rigorous advocacy, added to the fact that we ourselves needed training on analysis, and telling stories out of the budget data” explained Joels Terks Abaver of the Christian Association of Non Indigenes. During one of the break out session, access to budget information and training on how to use this budget data became a prominent challenge in the resolution of the several groups.

The second day took participants through the data pipelines, while running an expedition on the available education and health sector budget data that was presented on the first day. Alas! We found out a big challenge on this budget data – it was not location specific! How does one track a budget data that does not answer the question of where? When involved in budget tracking, it is important to have a description data that states where exactly the funds will go. An example is Construction of Borehole water pump in Kaura Namoda LGA Primary School, or we include the budget of Kaura Namoda LGA Primary School as a subtitle in the budget document.

Taking participants through the data pipelines and how it relates to the Monitoring and Evaluation System

Taking participants through the data pipelines and how it relates to the Monitoring and Evaluation System

In communities like this, it is important to note that soft skills are needed to be taught – , like having 80% of the participants not knowing why excel spreadsheets are been used for budget data; like 70% of participants not knowing there is a Google spreadsheet that works like Microsoft Excel; like all participants not even knowing where to get the Nigeria Budget data and not knowing what Open Data means. Well moving through the school of data through the Open Data Party in this part of the world, as changed that notion.”It was an interesting and educative 2-day event taking us through the budget cycle and how budget data relates to tracking” Babangida Ummar, the Chairman of the Budget Working Group said.

Going forward, this group of NGO and journalist has decided to join trusted sources that will be monitoring service delivery of four education institutions in the state, using the Education Budget Tracker. It was an exciting 2-day as we now hope to have a monthly engagement with this working group, as a renewed effort in ensuring service delivery in the education sector. Wondering where the next data party will happen? We are going to the South – South of Nigeria in the month of October – Calabar to be precise, and on the last day of the month, we will be rocking Abuja!

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Data Visualization and Design – Skillshare

Heather Leson - September 26, 2014 in Community, Events, HowTo, Resources, School_Of_Data, Storytelling, Visualisation

Observation is 99 % of great design. We were recently joined by School of Data/Code for South Africa Fellow Hannah Williams for a skillshare all about the data visualization and design. We all know dataviz plays a huge part in our School of Data workshops as a fundamental aspect of the data pipeline. But how do you know that, beyond using D3 or the latest dataviz app, you are helping people actually communicate visually?

In this 40 minute video, Hannah shares some tips and best practices:

Design by slides

The world is a design museum – what existing designs achieve similar things? How specifically do they do this? How can this inform your digital storytelling?


Want to learn more? Here are some great resources from Hannah and the network:

Hannah shared some of her other design work. It is great to see how data & design can be used in urban spaces: Project Busart.

We are planning more School of Data Skillshares. In the coming weeks, there will be sessions about impact & evaluation as well as best practices for mapping.

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Data Playlists

Nisha Thompson - September 25, 2014 in HowTo, Spreadsheets

Finding ways to learn new ways to play and work with data is always a challenge. Workshops, courses, and sprints are always a really great way to learn from people, and at the School of Data Fellow are doing that all over the world. In India there are lots of languages, different levels of literacy and technology adaption so we want experiment with different ways of sharing data skills.  It can be difficult put on a workshop or do a course, so we thought let’s start creating videos that can be accessed by people . It was important that the videos be easy to replicate and bite size, and that the format was flexible enough to accommodate different ways of teaching.  So we and others can experiment with different types of videos.

So instead of a single 10 minute video on how to use Excel we are asking people to create playlists of videos that are between 2 to 5 minutes long that are one concept or process presented i neach video.

Our first video is about formatting in Excel:

Don’t like excel? Do one for Open Spreadsheets or Fusion Tables.  English is not useful for your audience? Translate each video or put in subtitles, or do your own version. Have a new way to do this same skill? Create a 2 minute video and we can add it to the playlist.  Sharing your favorite tools and tricks for working with data is the main goal of this project.

If you want to do one there a few rules:

  1. Introduce yourself
  2. Break up the lesson by technique and make each video no more than 2 to 5 minutes.
  3. Make sure they are a playlist.
  4. Upload them to youtube and tag them DataMeet and School of Data
  5. Let us know!

If you have any feedback or a video request please feel free to leave it in the comments. We will hopefully release 2 playlists every month.

Adapting from post on

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A Weekend of Data, Hacks and Maps in Nigeria

olubabayemi - September 16, 2014 in charity data, Data Cleaning, Data Expeditions, event, Mapping, maps, School_Of_Data, Spreadsheets, Visualisation

It was another weekend of hacking for good all around the world, and Abuja, Nigeria was not left out of the weekend of good, as 30 participants gathered at the Indigo Trust funded space of Connected Development [CODE] on 12 – 14 September, scraping datasets, brainstorming creating technology for good, and not leaving one thing out – talking soccer (because it was a weekend, and Nigeria “techies” love soccer especially the English premiership).

Participants at the Hack4Good 2014 in Nigeria

Participants at the Hack4Good 2014 in Nigeria

Leading the team, was Dimgba Kalu (Software Architect with Integrated Business Network and founder TechNigeria), who kick started the 3 day event that was built around 12 coders with other 18 participants that worked on the Climate Change adaptation stream of this year #Hack4Good. So what data did we explore and what was hacked over the weekend in Nigeria? Three streams were worked :

  1. Creating a satellite imagery tagging/tasking system that can help the National Space Research Development Agency deploy micromappers to tag satellite imageries from the NigeriaSat1 and NigeriaSat2
  2. Creating an i-reporting system that allows citizen reporting during disasters to Nigeria Emergency Management Agency
  3. Creating an application that allows citizens know the next water point and its quality within their community and using the newly released dataset from the Nigeria Millennium Development Goal Information System on water points in the country.

Looking at the three systems that was proposed to be developed by the 12 coders, one thing stands out, that in Nigeria application developers still find it difficult to produce apps that can engage citizens – a particular reason being that Nigerians communicate easily through the radio, followed by SMS as it was confirmed while I did a survey during the data exploration session.

Coders Hackspace

Coders Hackspace

Going forward, all participants agreed that incorporating the above medium (Radio and SMS) and making games out of these application could arouse the interest of users in Nigeria.  “It doesn’t mean that Nigerian users are not interested in mobile apps, what we as developers need is to make our apps more interesting” confirmed Jeremiah Ageni, a participant.

The three days event started with the cleaning of the water points data, while going through the data pipelines, allowing the participants to understand how these pipelines relates to mapping and hacking. While the 12 hackers were drawn into groups, the second day saw thorough hacking – into datasets and maps! Some hours into the second day, it became clear that the first task wouldn’t be achievable; so much energy should be channelled towards the second and third task.

SchoolofData Fellow - Oludotun Babayemi taking on the Data Exploration session

SchoolofData Fellow – Oludotun Babayemi taking on the Data Exploration session

Hacking could be fun at times, when some other side attractions and talks come up – Manchester United winning big (there was a coder, that was checking every minutes and announcing scores)  , old laptops breaking (seems coders in Abuja have old  ones), coffee and tea running out (seems we ran out of coffee, like it was a sprint), failing operating systems (interestingly, no coders in the house had a Mac operating system), fear of power outage (all thanks to the power authority – we had 70 hours of uninterrupted power supply) , and no encouragement from the opposite sex (there was only two ladies that strolled into the hack space).

Bring on the energy to the hackspace

Bring on the energy to the hackspace

As the weekend drew to a close, coders were finalizing and preparing to show their great works.  A demo and prototype of streams 2 and 3 were produced. The first team (working on stream 2), that won the hackathon developed EMERGY, an application that allows citizens to send geo-referenced reports disasters such as floods, oil spills, deforestation to the National Emergency Management Agency of Nigeria, and also create a situation awareness on disaster tagged/prone communities, while the second team, working on stream 3, developed KNOW YOUR WATER POINT an application that gives a geo-referenced position of water points in the country. It allows communities; emergency managers and international aid organizations know the next community where there is a water source, the type, and the condition of the water source.

(The winning team of the Hack4Good Nigeria) From Left -Ben; Manga; SchoolofData Fellow -Oludotun Babayemi; Habib; Chief Executive, CODE - Hamzat

(The winning team of the Hack4Good Nigeria) From Left -Ben; Manga; SchoolofData Fellow -Oludotun Babayemi; Habib; Chief Executive, CODE – Hamzat

Living with coders all through the weekend, was mind blowing, and these results and outputs would not be scaled without its challenges. “Bringing our EMERGY application live as an application that cuts across several platforms such as java that allows it to work on feature phones can be time consuming and needs financial and ideology support” said Manga, leader of the first team. Perhaps, if you want to code, do endeavour to code for good!


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Easy Access to World Bank and UN Development Data from IPython Notebooks

Tony Hirst - September 12, 2014 in Open Development Toolkit, Scraping

Although more and more data is being published in an open format, getting hold of it in a form that you can quickly start to work with can often be problematic. In this post, I’ll describe one way in which we can start to make it easier to work with data sets from remote data sources such as the World Bank, the UN datastore and the UN Population division from an IPython Notebook data analysis environment.

For an example of how to run an IPython Notebook in a Chrome browser as a browser extension, see Working With Data in the Browser Using python – coLaboratory. Unfortunately, of the wrappers described in this post, only the tools for accessing World Bank Indicators will work – the others currently require libraries to be installed that are not available within the coLaboratory extension.

The pandas Python library is a programming library that provides powerful support for working with tabular datasets. Data is loaded into a dataframe, the rows and columns of which can be manipulated in much the same way as the rows or columns of a spreadsheet in a spreadsheet application. For example, we can easily find the sum of values in a column of numbers, or the mean value; or we can add values from two or more columns together. We can also run grouping operations, a bit like pivot tables, summing values from all rows associated with a particular category as described by a particular value in a category column.

Dataframes can also be “reshaped” so we can get the data into a form that looks like the form we want to be.

But how do we get the data into this environment? One way is to load in the data from a CSV file or Excel spreadsheet file, either one that has been downloaded to our desktop, or one that lives on the web and can be identified by a URL. Another approach is to access the data directly from a remote API – that is, a machine readable interface that allows the data to be grabbed directly from a data source as a data feed – such as the World Bank indicator data API.

On most occasions, some work is needed to transform the data received from the remote API into a form that we can actually work with it, such as a pandas dataframe. However, programming libraries may also be provided that handle this step for you – so all you need to do is load in the programming library and then simply call the data in to a dataframe.

The pandas library offers native support for pulling data from several APIs, including the World Bank Development Indicators API. You can see an example of it in action in this example IPython notebook: World Bank Indicators API – IPython Notebook/pandas demo.


Whilst the World Bank publishes a wide range of datasets, there are plenty of other datasets around that deal with other sorts of development related data. So it would be handy if we could access data from those sources just as easily as we can the World Bank Development Indicators data.

In some cases, the data publishers may offer an API, in which case we can write a library a bit like the pandas remote data access library for the World Bank API. Such a library would “wrap” the API and allow us to make calls directly to it from an IPython notebook, getting the data back in the form of a pandas dataframe we can work with directly.

Many websites, however, do not publish an API – or occasionally, if they do, the API may be difficult to work with. On the other hand, the sites may publish a web interface that allows us to find a dataset, select particular items, and then download the corresponding data file so that we can work with it.

This can be quite a laborious process though – rather than just pulling a dataset in to a notebook, we have to go to the data publisher’s site, find the data, download it to our desktop and then upload it into a notebook.

One solution to this is to write a wrapper that acts as a screenscraper, which does the work of going to the data publisher’s website, find the data we want, downloading it automatically and then transforming it into a pandas dataframe we can work with.

In other words, we can effectively create our own ad hoc data APIs for data publishers who have published the data via a set of human useable webpages, rather than a machine readable API.

A couple of examples of how to construct such wrappers are linked to below – they show how the ad hoc API can be constructed, as well as demonstrating their use – a use as simple as using the pandas remote data access functions show above.

  • The UN Department of Social and Economic Affairs Population Division on-line database makes available a wide range of data relating to population statistics. Particular indicators and the countries you require the data for are selected from two separate listboxes, and the data is then downloaded as a CSV file. By scraping the contents of the list boxes, we can provide a simple command based interface for selecting a dataset containing data fro the desired indicators and countries, automatically download the data and parse it into a pandas dataframe: UN Population Division Data API.

    So for example, we can get a list of indicators:

    We can also get a list of countries (that we can search on) and then pull back the required data for the specified countries and indicators.


    Note that the web interface limits how many countries and indicators can be specified in any single data download request. We could cope with this in our ad hoc API by making repeated calls to the UN website if we want to get a much wider selection of data, aggregating the results into a a single dataframe before presenting them back to the user.

  • The UNdata website publishes an official XML API, but I couldn’t make much (quick) sense of it when I looked at it, so I made a simple scraper for the actual website that allows me to request data by searching for an indicator, pulling back the list of results, and then downloading the data I want as a CSV file from a URL contained within the search results and parsing it into a pandas dataframe: UNdata Informal API.


By using such libraries, we can make it much easier to pull data into the environments within which we actually want to work with the data. We can also imagine creating “linked” data access libraries that can pull datasets from multiple sources and then merge them together – for example, we might pull back data from both the World Bank and the UN datastore into a single dataframe.

If there are any data sources that you think are good candidates for opening up in this sort of way, so that the data can be pulled more easily from them, please let us know via the comments below.

And if you create any of your own notebooks to analyse the data from any of the sources described above, please let us know about those too:-)

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