At Quantego, we do most high-level work that supports energy analysts in Jupyter Notebooks. This allows us to pull several Java and Python packages together for a highly productive work environment.
Sample notebooks are hosted on Github and distributed with our Docker images. Of course we prefer for our sample notebooks to work, when people run them. They also uncover potential problems, by running at a very high level and thus using almost all available features.
If you have a similar setup – for example in a data analytics-driven environment – the following could work for you as well:
Make sure your notebooks run correctly when running “Run All”. When testing you may try different things and run cells out of order. For automatic testing to work, they should run all in sequence.
This will convert your notebooks to HTML. We’re not intersted in the output, only potential errors during conversion. This only works with Jupyter and iPython >=4. Previous versions simply ignore errors.
Next you could just run the same command in an isolated Docker container or in a CI step
If you happen to only have FTP access to a server or account (CPanel) you’re looking after, LFTP is an efficient tool to keep incremental backups. This will make hard links of the previous backup and updated it, copying and storing only changed files.
At Quantego.com we love working with Pandas Dataframes. We use them to store and analyze results from simulation runs. On top of our data matrix and a multi-level index we also need to accommodate custom plotting functions and attributes from the previous simulation run.
pandas.DataFrame for this task was a no-brainer. The new version 0.16.1 (to be released in the next days) includes some fixes to make working with subclasses of complex data-frames (DF) easier. Here an example of what can be done. First define two new classes for
pandas.Series (single col DF) and
pandas.DataFrame . You can define new functions or attributes, as needed.
"My custom dataframe"
_constructor_sliced . They make sure you get the correct class back, when slicing the DF.
self you have convenient access to all Pandas functions and can even roll your own.
I couldn’t find a truly universal regular expression (regex) to match phone numbers, no matter from which country and in which format. They all seemed to be limited in some way. Even named entity extraction APIs require you to set a country to find phone numbers.
In the end I rolled my own regex. It simply looks for a certain amount of numbers and characters generally used to make phone numbers human-readable. If you are looking to match longer or shorter numbers, you can just change the quantifiers. Some examples it will match:
Let me know, if this is useful for you or if you find space for improvement. Currently the biggest issue I see is that the matching ranges between numbers and total chars are unrelated. Due to many filling chars higher values are needed. Those can lead to false negatives. Best test it for yourself.
Most invoices exist in electronic format. They are generated from structured data and need to be entered as structured data. It’s a shame that we still need humans to manually extract data points, like amount, date or issuer from it.
In the last days, I tried a few online invoicing solutions, like shoeboxed, but none of them does a good job at automatically recognizing new invoices. Some do it manually and charge accordingly.
Currently I don’t see a way to automatically get the data. PDFs are simply not made for this. the best we can do is to add templates for a specific invoice format and use that to extract the data. I have created a proof of concept library, which is open source on github.
If you have any thoughts on what to improve or would like to extend this to use it in a production accounting, let me know.
I like to write my notes and reports in Markdown and then send them out in PDF. Gimli worked OK for a while but rasterizes files and doesn’t work with UTF8-characters. I finally came across a similar project in Python and now I’m very happy with it. You can define a custom style sheet in your .profile and md2pdf will use it.
We recently started using the slide function of iPython notebooks. Basically it allows you to partition your notebook onto different slides, slide fragments and subslides. Those can be exported to reveal.js
There is already a great viewer for notebooks on http://nbviewer.ipython.org. To save some steps in exporting, converting and adding reveal.js, I took the idea and added a slide viewer. Anyone can use it to link to their slides on Github, Gist or any other place. We even support Basic Auth. Check it out at:
Ansible is a great automation solution. I mainly use it to provision servers and launch Docker instances on them. Sometimes I need container attributes, like PID or Port to configure Nginx or monitoring tools.
While the Ansible documentation gives you some hints, I didn’t find it 100% obvious on how to solve this. Basically all your newly-created containers will end up in a list called docker_containers. It has the same structure as docker inspect.