EuroPython: EuroPython 2019: SIM cards for attendees

Switzerland is often not included in European cell provider’s roaming packages and also not covered by the EU roaming regulation, so you can potentially incur significant charges when going online with your mobile or notebook.

Please do check your mobile package to see whether it includes Switzerland in your roaming package.

Some providers offer special packages which can be bought as option to also cover Switzerland.

Swiss SIM cards available in ticket shop

In order to make things easier for you, we have purchased 300 SIM cards from a local Swiss cell provider, which we will make available in our ticket shop. After purchase, you can then pick up the cards at the registration desk (please bring your receipt).


These cards include 1 GB data with high-speed 4G/LTE and costs EUR 13.50, incl. 7.7% Swiss VAT.

Please check our SIM card page for more details.


EuroPython 2019 Team

Planet Python

Doug Hellmann: Dependencies between Python Standard Library modules

Glyph’s post about a “kernel python” from the 13th based on Amber’s presentation at PyCon made me start thinking about how minimal standard library could really be. Christian had previously started by nibbling around the edges, considering which modules are not frequently used, and could be removed. I started thinking about a more extreme change, …

Planet Python

Weekly Python StackOverflow Report: (clxxxiv) stackoverflow python report

These are the ten most rated questions at Stack Overflow last week.
Between brackets: [question score / answers count]
Build date: 2019-06-29 20:53:35 GMT

  1. Why was p[:] designed to work differently in these two situations? – [29/6]
  2. How do I create a new column in a dataframe from an existing column using conditions? – [11/5]
  3. Anaconda 4.7.5 – Warning about conda-build <3.18.3 and issues with python packages – [10/3]
  4. How to connect the ends of edges in order to close the holes between them? – [10/2]
  5. Why is NumPy sometimes slower than NumPy + plain Python loop? – [9/3]
  6. Find first and last non-zero column in each row of a pandas dataframe – [7/4]
  7. Why does * work differently in assignment statements versus function calls? – [7/1]
  8. How to vectorize a loop through a matrix numpy – [6/3]
  9. Pandas expanding/rolling window correlation calculation with p-value – [6/2]
  10. Using Pandas df.where on multiple columns produces unexpected NaN values – [6/1]

Planet Python

Ned Batchelder: Changelog podcast: me, double-dipping

I had a great conversation with Jerod Santo on the Changelog podcast: The Changelog 351: Maintainer spotlight! Ned Batchelder. We talked about Open edX, and, and maintaining open source software.

One of Jerod’s questions was unexpected: what other open source maintainers do I appreciate? Two people that came to mind were Daniel Hahler and Julian Berman. Some people are well-known in the Python community because they are the face of large widely used projects. Daniel and Julian are known to me for a different reason: they seem to make small contributions to many projects. I see their names in the commits or issues of many repos I wander through, including my own.

This is a different kind of maintainership: not guiding large efforts, but providing little pushes in lots of places. If I had had the presence of mind, I would have also mentioned Anthony Sottile for the same reason.

And I would have mentioned Mariatta, for a different reason: her efforts are focused on CPython, but on the contribution process and tooling around it, rather than the core code itself. A point I made in the podcast was that people and process challenges are often the limiting factor to contribution, not technical challenges. Mariatta has been at the forefront of the efforts to open up CPython contribution, and I wish I had mentioned her in the podcast.

And I am sure there are people I am overlooking that should be mentioned in these appreciations. My apologies to you if you are in that category…

Planet Python

IslandT: Return the highest volume of traffic during peak hour

In this article, we are going to create a function which will return a list of tuples that consist of a particular hour and the highest traffic volume for that particular hour. The stat has been taken every 10 minutes in each hour. For example, at 4.00pm the total numbers of traffics that pass through a junction for every 10 minutes are as follows: [23, 22, 45, 66, 54, 33]. The traffic volume measurement in this example will begin at 4.00pm and end at 8.00pm. Below is the solution to this problem.

 def traffic_count(array):      # first we will create the traffic volume list for 4pm, 5pm, 6pm and 7pm within a big list      count = 0     arr_stat = []     while(count < 24):         arr_stat.append(array[count:count+6])         count += 6      # then we will create the tuple which consists of time and the peak traffic at that hour     max = 0     arrs = []     time = 4          for elem in arr_stat:         for item in elem:             if max < item : #find out the higest stat                 max = item         arrs.append(((str(time) + ":00pm"), max))         time += 1         max = 0      return arrs 
 traffic_count([23,24,34,45,43,23,57,34,65,12,19,45, 54,65,54,43,89,48,42,55,22,69,23,93]) # if you enter the above data into the function then you will get below outcome! 

list of tuples consist of time vs traffic volume

Give your thought regarding this solution in the comment box below this post!

Planet Python