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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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def mean(x):
return(sum(x)/len(x))
def variance(x):
x_mean = mean(x)
return sum((x-x_mean)**2)/(len(x)-1)
def standard_deviation(x):
return math.sqrt(variance(x))
The functions above build on each other. They depend on the previous function. What is a good way to implement this in Python? Should I use a class which has these functions? Are there other options?
Because they are widely applicable, keep them as they are
Many parts of a program may need to calculate these statistics, and it will save wordiness to not have to get them out of a class. Moreover, the functions actually don't need any class-stored data: they would simply be static methods of a class. (Which in the old days, we would have simply called "functions"!)
If they needed to store internal information to work correctly, that is a good reason to put them into a class
The advantage in that case is that it is more obvious to the programmer what information is being shared. Moreover, you might want to create two or more instances that had different sets of shared data. That is not the case here.
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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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Simply put, what are the preferred practices for writing larger python applications that use pandas dataframes as its primary method for data representation?
I often find myself struggling to maintain inconsistencies in dataframes, sometimes invariants leak through in data, datatypes are not what you expect etc.
I'm wondering just what are the best practices for writing larger, stable applications in pandas? I want to take advantage of array-representation in data for speed, but I also want to make sure that there's a way to further define the "bounds" of dataframe, what it should have in it, in a clean way.
Assertions on receiving a dataframe from a caller.
Forcing a dataframe parameter to have specific dtypes.
Defining a dataframe "type" based upon the columns it has.
Opportunities for OOP, at the dataframe level
Also, sorry for the vague nature of this. I'm starting on a project, and I want to ask this question before I get too far off course. I've been burned in the past with regards to not enforcing enough of a structure when it comes to dataframes.
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I am intermediate in R and a beginner in Python. However my core abilities lie less in data analysis and more in programming and developing large software systems in teams, and I don't have time to become an expert in both.
Given the advances in the Python world in numpy, scipy, pandas, and its prevalence in data science and in general programming, I think I need to concentrate on Python (even though I enjoy R a lot), and accept that for some tasks I might be 75% as efficient, say, as I would be in R. I'd find this efficiency loss acceptable in order to be a master of one language rather than intermediate at both.
However I don't know enough about either language to really be sure of my facts. I would be very interested in hearing from anyone who is experienced in both R and Python and can say what would be the significant disadvantages, if any, of dropping R in favour of Python?
Edit 5: this question on stats.stackexchange is similar and has some great answers.
(Edits 3-4: reverted content/title to original question, which was closed. The original question attracted a lot of expert comment, my attempt to narrow the question to reopen it failed, and I'd prefer to have these comments below the original text they were commenting on.)