Tagged: ipynb

Coping With Time Periods in Python Pandas – Weekly, Monthly, Quarterly

One of the more powerful, and perhaps underused (by me at least), features of the Python/pandas data analysis package is its ability to work with time series and represent periods of time as well as simple “pointwise” dates and datetimes.

Here’s a link to a first draft Jupyter notebook showing how to cast weekly, monthly and quarterly periods in pandas from NHS time series datasets: Recipes – Representing Time Periods.ipynb

 

When Identifiers Don’t Align – NHS Digital GP Practice Codes and CQC Location IDs

One of the nice things about NHS Digital datasets is that there is a consistent use of identifier codes across multiple datasets. For example, GP Practice Codes are used to index particular GP practices across multiple datasets listed on both the GP and GP practice related data and General Practice Data Hub directory pages.

Information about GPs is also recorded by the CQC, who publish quality ratings across a wide range of health and social care providers. One of the nice things about the CQC data is that it also contains information about corporate groupings (and Companies House company numbers) and “Brands” with which a particular location is associated, which means you can start to explore the make up of the larger commercial providers.

Unfortunately, the identifier scheme used by the CQC is not the same as the once used by NHS Digital. This wouldn’t provide much of a hurdle if a lookup table was available that mapped the codes for GP practices rated by the CQC against the NHS Digital codes, but such a lookup table doesn’t appear to exist – or at least, is not easily discoverable.

So if we do want to join the CQC and NHS Digital datasets, what are we to do?

One approach is to look for common cribs across both datasets to bring them into partial alignment, and then try to do some  do exact matching within nearly aligned sets. For example, both datasets include postcode data, so if we match on postcode, we can then try to find a higher level of agreement by trying to exactly match location names sharing the same postcode.

This gets us so far, but exact string matching is likely to return a high degree of false negatives (i.e. unmatched items that should be matched). For example, it’s easy enough for us to assume that THE LINTHORPE SURGERY and LINTHORPE SURGERY  are the same, but they aren’t exact matches. We could improve the likelihood of matching by removing common stopwords and stopwords sensitive to this domain – THE, for example, or “CENTRE”, but using partial or fuzzy matching techniques are likely to work better still, albeit with the risk of now introducing false positive matches (that is, strings that are identified as matching at a particular confidence level but that we would probable rule out as a match, for example HIRSEL MEDICAL CENTRE and KINGS MEDICAL CENTRE.

Anyway, here’s a quick sketch of how we might start to go about reconciling the datasets – comments appreciated about how to improve it further either here or in the repo issues: CQC and NHS Code Reconciliation.ipynb

A Recipe for Automatically Going From Data to Text to Reveal.js Slides

Over the last few years, I’ve experimented on and off with various recipes for creating text reports from tabular data sets, (spreadsheet plugins are also starting to appear with a similar aim in mind). There are several issues associated with this, including:

  • identifying what data or insight you want to report from your dataset;
  • (automatically deriving the insights);
  • constructing appropriate sentences from the data;
  • organising the sentences into some sort of narrative structure;
  • making the sentences read well together.

Another approach to humanising the reporting of tabular data is to generate templated webpages that review and report on the contents of a dataset; this has certain similarities to dashboard style reporting, mixing tables and charts, although some simple templated text may also be generated to populate the page.

In a business context, reporting often happens via Powerpoint presentations. Slides within the presentation deck may include content pulled from a templated spreadsheet, which itself may automatically generate tables and charts for such reuse from a new dataset. In this case, the recipe may look something like:

exceldata2slide

#render via: http://blockdiag.com/en/blockdiag/demo.html
{
  X1[label='macro']
  X2[label='macro']

  Y1[label='Powerpoint slide']
  Y2[label='Powerpoint slide']

   data -> Excel -> Chart -> X1 -> Y1;
   Excel -> Table -> X2 -> Y2 ;
}

In the previous couple of posts, the observant amongst you may have noticed I’ve been exploring a couple of components for a recipe that can be used to generate reveal.js browser based presentations from the 20% that account for the 80%.

The dataset I’ve been tinkering with is a set of monthly transparency spending data from the Isle of Wight Council. Recent releases have the form:

iw_transparency_spending_data

So as hinted at previously, it’s possible to use the following sort of process to automatically generate reveal.js slideshows from a Jupyter notebook with appropriately configured slide cells (actually, normal cells with an appropriate metadata element set) used as an intermediate representation.

jupyterslidetextgen

{
  X1[label="text"]
  X2[label="Jupyter notebook\n(slide mode)"]
  X3[label="reveal.js\npresentation"]

  Y1[label="text"]
  Y2[label="text"]
  Y3[label="text"]

  data -> "pandas dataframe" -> X1  -> X2 ->X3
  "pandas dataframe" -> Y1,Y2,Y3  -> X2 ->X3

  Y2 [shape = "dots"];
}

There’s an example slideshow based on October 2016 data here. Note that some slides have “subslides”, that is, slides underneath them, so watch the arrow indicators bottom left to keep track of when they’re available. Note also that the scrolling is a bit hit and miss – ideally, a new slide would always be scrolled to the top, and for fragments inserted into a slide one at a time the slide should scroll down to follow them).

The structure of the presentation is broadly as follows:

demo_-_interactive_shell_for_blockdiag_-_blockdiag_1_0_documentation

For example, here’s a summary slide of the spends by directorate – note that we can embed charts easily enough. (The charts are styled using seaborn, so a range of alternative themes are trivially available). The separate directorate items are brought in one at a time as fragments.

testfullslidenotebook2_slides1

The next slide reviews the capital versus expenditure revenue spend for a particular directorate, broken down by expenses type (corresponding slides are generated for all other directorates). (I also did a breakdown for each directorate by service area.)

The items listed are ordered by value, and taken together account for at least 80% of the spend in the corresponding area. Any further items contributing more than 5%(?) of the corresponding spend are also listed.

testfullslidenotebook2_slides2

Notice that subslides are available going down from this slide, rather than across the mains slides in the deck. This 1.5D structure means we can put an element of flexible narrative design into the presentation, giving the reader an opportunity to explore the data, but in a constrained way.

In this case, I generated subslides for each major contributing expenses type to the capital and revenue pots, and then added a breakdown of the major suppliers for that spending area.

testfullslidenotebook2_slides3

This just represents a first pass at generating a 1.5D slide deck from a tabular dataset. A Pareto (80/20) heurstic is used to try to prioritise to the information displayed in order to account for 80% of spend in different areas, or other significant contributions.

Applying this principle repeatedly allows us to identify major spending areas, and then major suppliers within those spending areas.

The next step is to look at other ways of segmenting and structuring the data in order to produce reports that might actually be useful…

If you have any ideas, please let me know via the comments, or get in touch directly…

PS FWIW, it should be easy enough to run any other dataset that looks broadly like the example at the top through the same code with only a couple of minor tweaks…

Making Music and Embedding Sounds in Jupyter Notebooks

It’s looking as if the new level 1 courses won’t be making use of Jupyter notebooks (unless I can find a way of sneaking them in via the single unit I’be put together!;-) but I still think they’re worth spending time exploring for course material production as well as presentation.

So to this end, as I read through the materials being drafted by others for the course, I’ll be looking for opportunities to do the quickest of quick demos, whenever the opportunity arises, to flag things that might be worth exploring more in future.

So here’s a quick example. One of the nice design features of TM112, the second of the two new first level courses, is that it incorporates some mimi-project activities for students work on across the course. One of the project themes relates to music, so I wondered what doing something musical in a Jupyter notebook might look like.

The first thing I tried was taking the outlines of one of the activities – generating an audio file using python and MIDI – to see how the embedding might work in a notebook context, without the faff of having to generate an audio file from python and then find a means of playing it:

midimusic

Yep – that seems to work… Poking around music related libraries, it seems we can also generate musical notation…

midimusic2

In fact, we can also generate musical notation from a MIDI file too…

midimusic3

(I assume the mappings are correct…)

So there may be opportunities there for creating simple audio files, along with the corresponding score, within the notebooks. Then any changes required to the audio file, as well as the score, can be effected in tandem.

I also had a quick go at generating audio files “from scratch” and then embedding the playable audio file

 

audio

That seems to work too…

We can also plot the waveform:

audio2

This might be handy for a physics or electronics course?

As well as providing an environment for creating “media-ful” teaching resources, the code could also provide the basis of interactive student explorations. I don’t have a demo of any widget powered examples to hand in a musical context (maybe later!), but for now, if you do want to play with the notebooks that generated the above, you can do so on mybinder – http://mybinder.org/repo/psychemedia/ou-tm11n – in the midiMusic.ipynb and Audio.ipynb notebooks. The original notebooks are here: https://github.com/psychemedia/OU-TM11N

Accessible Jupyter Notebooks?

Pondering the extent to which Jupyter notebooks provide an accessible UI, I had a naive play with the Mac VoiceOver app run over Jupyter notebooks the other day: markdown cells were easy enough to convert to speech, but the code cells and their outputs are nested block elements which seemed to take a bit more navigation (I think I really need to learn how to use VoiceOver properly for a proper test!). Suffice to say, I really should learn how to use screen-reader software, because as it stands I can’t really tell how accessible the notebooks are…

A quick search around for accessibility related extensions turned up the jupyter-a11y: reader extension [code], which looks like it could be a handy crib. This extension will speak aloud a the contents of a code cell or markdown cell as well as navigational features such as whether you are in the cell at the top or the bottom of the page. I’m not sure it speaks aloud the output of code cell though? But the code looks simple enough, so this might be worth a play with…

On the topic of reading aloud code cell outputs, I also started wondering whether it would be possible to generate “accessible” alt or longdesc text for matplotlib generated charts and add those to the element inserted into the code cell output. This text could also be used to feed the reader narrator. (See also First Thoughts on Automatically Generating Accessible Text Descriptions of ggplot Charts in R for some quick examples of generating textual descriptions from matplotlib charts.)

Another way of complementing the jupyter-a11y reader extension might be to use the python pindent [code] tool to annotate the contents of code cells with accessible comments (such as comments that identify the end of if/else blocks, and function definitions). Another advantage of having a pindent extension to annotate the content of notebook python code cells is that it might help improve the readability of code for novices. So for example, we could have a notebook toolbar button that will toggle pindent annotations on a selected code cell.

For code read aloud by the reader extension, I wonder if it would be worth running the content of any (python) code cells through pindent first?

PS FWIW, here’s a related issue on Github.

PPS another tool that helps make python code a bit more accessible, in an active sense, in a Jupyter notebook is this pop-up variable inspector widget.

Steps Towards Some Docker IPython Magic – Draft Magic to Call a Contentmine Container from a Jupyter Notebook Container

I haven’t written any magics for IPython before (and it probably shows!) but I started sketching out some magic for the Contentmine command-line container I described in Using Docker as a Personal Productivity Tool – Running Command Line Apps Bundled in Docker Containers,

What I’d like to explore is a more general way of calling command line functions accessed from arbitrary containers via a piece of generic magic, but I need to learn a few things along the way, such as handling arguments for a start!

The current approach provides crude magic for calling the contentmine functions included in a public contentmine container from a Jupyter notebook running inside a container. The commandline contentmine container is started from within the notebook contained and uses a volume-from the notebook container to pass files between the containers. The path to the directory mounted from the notebook is identified by a bit of jiggery pokery , as is the method for spotting what container the notebook is actually running in (I’m all ears if you know of a better way of doing either of these things?:-)

The magic has the form:

%getpapers /notebooks rhinocerous

to run the getpapers query (with fixed switch settings for now) and the search term rhinocerous; files are shared back from the contentmine container into the .notebooks folder of the Jupyter container.

Other functions include:

%norma /notebooks rhinocerous
%cmine /notebooks rhinocerous

These functions are applied to files in the same folder as was created by the search term (rhinocerous).

The magic needs updating so that it will also work in a Jupyter notebook that is not running within a container – this should simply be just of case of switching in a different directory path. The magics also need tweaking so we can pass parameters in. I’m not sure if more flexibility should also be allowed on specifying the path (we need to make sure that the paths for the mounted directories are the correct ones!)

What I’d like to work towards is some sort of line magic along the lines of:

%docker psychemedia/contentmine -mountdir /CALLING_CONTAINER_PATH -v ${MOUNTDIR}:/PATH COMMAND -ARGS etc

or cell magic:

%%docker psychemedia/contentmine -mountdir /CALLING_CONTAINER_PATH -v ${MOUNTDIR}:/PATH
COMMAND -ARGS etc
...
COMMAND -ARGS etc

Note that these go against the docker command line syntax – should they be closer to it?

The code, and a walked through demo, are included in the notebook available via this gist, which should also be embedded below.


Calling an OData Service From Python – UK Parliament Members Data Platform

Whilst having a quick play producing Slack bots and slash commands around the UK Parliament APIs, I noticed (again) that the Members data platform has an OData endpoint.

OData is a data protocol for querying online data services via HTTP requests although it never really seemed to have caught the popular imagination, possibly because Microsoft thought it up, possibly because it seems really fiddly to use…

I had a quick look around for Python client/handler for it, and the closest I came was the pyslet package. I’ve posted a notebook showing my investigations to date here: Handling the UK Parliament Members Data Platform OData Feed, but it seems really clunky and I’m not sure I’ve got it right! (There doesn’t seem to be a lot of tutorial support out there, either?)

Here’s an example of the sort of mess I got myself in:

UK_Parliament_api_test_and_members_data_platform_OData_service_test

To make the Parliament OData service more useful needs a higher level Python wrapper, I think, that abstracts a bit further and provides some function calls that make it a tad easier (and natural) to get at the data. Or maybe I need to step back, have a read of the OData blocks, properly get my head around the pyslet OData calls, and try again!