Skip to main content

Flattening An Array

How do you flatten an array?  That is, how do you take something like this:

[1, [2, [3, [4]]]]

and change it into something like this?

[1, 2, 3, 4]

The first thing that occurs to me is that you would go through each element of the given array and check to see whether it is an array.  So we go through:

1 (NO)
[2, [3, [4]]] (YES)

We're done with the non-arrays -- we can keep them somewhere (another array) but the arrays require further processing.  We process them in exactly the way we processed the first array.  The trick is figuring out how to do that organically: we need the computer to keep processing elements of the array and members of those members recursively (!) on down to the elements.

What I think you would do is create a general function that processes an array into elements, then call that function not only on the array itself, but on the members of that array.  How would that look?

Well, if we have an element, we don't call it.  But if we have an array, we do call it.  It gives us back a result that we can parse through, saving the good stuff and continuing to process the bad stuff.  So for that second element processing would look like this:

[2, [3, [4]]] -- Call =>
2, [3, [4]] -- Keep 2 =>
[3, [4]] -- Call =>
3, [4] -- Keep 3 =>
[4] -- Call
4 -- Keep 4
DONE!

So in short, the function returns an array, which we loop through, keeping all the items that are non-arrays and processing the rest.  How does it return that array?  It just loops through the items and stores them, I guess -- nothing fancy.

I'll update as I think more about this -- I think this is on the right track, but I'm not yet convinced it would work.  Maybe there's some way to do it with just loops so that you don't have to bother with recursive functions at all...

Comments

Popular posts from this blog

Getting Geodata From Google's API

The apps I'm going to be analyzing are part of Dr. Charles Severance's MOOC on Python and Databases and work together according to the following structure (which applies both in this specific case and more generally to any application that creates and interprets a database using online data). The data source, in this case, is Google's Google Maps Geocoding API.  The "package" has two components: geoload.py  and geodump.py .  geoload.py  reads a list of locations from a file -- addresses for which we would like geographical information -- requests information about them from Google, and stores the information on a database ( geodata.db ).  geodump.py  reads and parses data from the database in JSON, then loads that into a javascript file.  The javascript is then used to create a web page on which the data is visualized as a series of points on the world-map.  Dr. Severance's course focuses on Python, so I'm only going to work my way through ...

The Jump Algorithm

Meetup Went to a Coding Whiteboard Meetup tonight.  It was pretty great.  One of the leaders was even a CS master's student.  At first, honestly, I felt a little bit frustrated, especially because everyone around me seemed to be using pretty high level concepts / approaches that I wasn't familiar with.  But I found someone and relentlessly talked him through his approach until we both kind of realized there were issues in the problem we hadn't worked out yet.  I guess it just reinforces my feeling that when something seems too difficult, if you can, you need to find someone and force him to explain it to you in terms you can understand.  If the people around you really understand what they're about, they will have no problem and you'll learn a lot (assuming they're patient, I guess).  If they don't, you'll realize you aren't as alone as you thought you were.  Bit of the old Socrates. Problem So imagine you have an array with a bunch of numbe...

Throughput, Latency, and Pipelines: Diagnosis Of A Fallacy

Source here . Latency is the time it takes for a job to complete from start to finish -- for example, if we're downloading a file from a server, we might define the latency of the download as the amount of time it takes from the initial request for the file to the time the last byte is received. Throughput is a measure of how much can be completed in a given time.  Following the same example, how many files could we download in an hour? What is the relationship between latency and throughput?   It make take, again, 1 hour to download a file.  Notice already that we have a relationship between some amount of work that can be completed and time -- if the file is, say, 2 GB, it takes 1 hour to download 2 GB in our example.  What we really want to do here is generalize: how long does it take to download 10 GB?  How many GB can we download in ten hours or one minute?  Generalizing over time, we derive latency; generalizing over work completed, we derive the...