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I was going through this answer on Stack Overflow. I came to know about existence of OrderedSet in Python. I would like to know how it is implemented internally. Is it similar to hash table implementation of sets?
Also, what is the time complexity of some of the common operations like insert, delete, find, etc.?
From the documentation available here
Implementation based on a doubly linked link and an internal
dictionary. This design gives OrderedSet the same big-Oh running times
as regular sets including O(1) adds, removes, and lookups as well as
O(n) iteration.
There is also a discussion on the topic, see Does Python have an ordered set?
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If code is divided into too many segments, can this make the program slow?
For example - Creating a separate file for just a single function.
In my case, I'm using Python, and suppose there are two functions that I need in the main.py file. If I placed them in different files (just containing the function).
(Suppose) Also, If I'm using the same library for the two functions and I've divided the functions into separate files.
How can this affect efficiency? (Machine performance-wise and Team-wise).
It depends on the language, the framework you use etc. However, dividing the code too much can make it unreadable, which is (most of the time) the bigger problem. Since most of the time you will (or should) be working in a team, you should consider how readable your code would be for them.
However, answering this in a definite way is difficult. You should ask a Senior developer on your team for guidelines.
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Often you see question asked about a better method of doing something, or just generally a looping question and very often the top answers will use some form of convoluted list/dict/tuple comprehension that takes longer for others to understand than create themselves. While a simple and understandable loop could have just been made.
Since it cannot provide any speed benefits that I could imagine, is there any use of it in python other than to look smart or be Pythonic?
Thanks.
I believe the goal in this case to make your code as concise and efficient as possible. At times it can seem convoluted, but the computer looping through multiple lines as opposed to a single line adds processing time, which in large applications and across many iterations can cause some delays.
Additionally, although it seems harder to understand initially, for an outside individual reading your code, it's much quicker for them to read simplified expressions than pages of loops to get an idea of what you're attempting to accomplish.
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I am wondering what I should do for the purpose of my project.
I am gonna operate on about 100 000 rows, every time.
what I wanted to do is to create an object "{}" and then, if I need to search for a value, just call it , for example
data['2018']['09']['Marketing']['AccountName']
the second option is to pull everyting into an array "[]" and in case I need to pull value, I will create a function to go through the array and sum numbers for specific parameters.
But don't know which method is faster.
Will be thankful if you can shed some light on this
Thanks in advance,
If performance (speed) is an issue, Python might not be the ideal choice...
Otherwise:
Might I suggest the use of a proper database, such as SQLLite (which comes shipped with Python).
And maybe SQLAlchemy as an abstraction layer. (https://docs.sqlalchemy.org/en/latest/orm/tutorial.html)
After all, they were made exactly for this kind of tasks.
If that seems overkill: Have a look at Pandas.
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If list comprehension is better than filter, as it performs slighly better and is considered more readable (arguably, in my opinion), why does filter even exist?
I use it all the time, but if the consensus is that list comprehensions are better, what are the reasons why we have the filter function?
Way, way back in the day, way before we had list comprehensions, some guy who liked functional programming wrote up map and filter and submitted the change, and it got put in. That's about it.
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Suppose i have a list of hundred natural numbers, set of hundred natural numbers and dictionary of hundred natural numbers (assuming both key and value are natural numbers). I want to access an element in these data types. Which will be the more efficient and faster way to access it? I know i can use some performance tools like timeit or cprofile etc to check the performance but how will i know which data type to choose and when?
At a high overview:
list lookups are O(n)
Where n is the length of the list.
dict lookups are O(1)
This is basic Big O notation or Complexity.