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Does anyone know of any useful resources to learn the structure of python code? When I say structure, I'm referring to the ingredients necessary to make the code correct, so a combination of syntax and the order of terms etc.
For example, to understand the type of a pandas dataframe, df, (I know, I've already identified that it's a dataframe, but just hypothetically), we type: type(df). For the shape, we write df.shape.
So, in the latter, why do we put df first, as in type(df)? Yet in the former, we put df last, and in parenthesis as in type(df). So, mixing these commands up, one may write df.type and expect to get an answer, but they wouldn't. So how do we learn these rules? I have searched high and low for some guidance, but I find very little; a lot of the guides assume you have an appreciation of these rules and don't explain them.
I'm not asking for help on this particular problem. Rather, I'm looking for a resource that would enable me to understand this, and the multitude of other rules better.
I don't have a Computer Science background, so my answer might be unorthodox or incomplete. There are things which come across as inconsistent when you use Python, and these things often trick me when I write new code. So you can easily find yourself looking up the same stuff time and time again.
However, I would say that the more you practice, the less likely it is to make mistakes or be confused. One good tip is to create a OneNote page with all the snippets that usually trip you up. Maybe by going back to that same snippet you will end up saving yourself time and fix those concepts in memory. It will cost you nothing, so give it a try.
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I work in insurance as an actuary, and one thing we deal with is models, it usually involve bunch of input tables, then a calculation or projection process to produce certain output in interests.
It is usually more complex then usual excel reports you see in business world.
Now given the fast calc speed & effienciency in oher programming platform (C#, python, C++, Julia...etc)
I really want to use other platform to either
replicate the certain computational intensive process which usually takes 2-3 hrs as it go back and forth between bunch of excel sheets and doing an iterative process and find the solution
call or control the excel process
I understand python can control for part 2 using openpyxl , but for 1. which are the easiest to replicate with? here i meant easy to convert.
thanks many~
i am playing around python but did not expect can replicate the complex iteration process easily
This question will almost certainly get closed as StackOverflow does not allow asking for recommended software.
There's an active actuary group in Julia, their website is here:
https://juliaactuary.org/
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I just finished reading some books about pandas, numpy & matplotlib and thought about getting some practice.
Unfortunately, I am a bit lost on how to start.
Can anyone recommend a site, which provides csv-files to practice on?
I have found some sites like kaggle or data.gov, but they just have files, which are already cleaned etc.
I'm also open to other ways on how to practice those libraries.
Grateful for every answer.
Best regards
Just Search the Web. Using e.g. Google seach for Pandas tutorial,
Numpy tutorial and so on.
Even the home site of matplotlib contains a couple of introductory
tutorials. See e.g. https://matplotlib.org/tutorials/index.html
A good source of knowledge is also stackoverflow itself.
You could try finding some interesting datasets on: icpsr.umich.edu
In my experience, these sets often contain missing values and values that need formatting, which could be good for practice.
Some datasets are public, but many require will require special credentials. If you are a university student, this often qualifies.
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I've been studying python for data science for about 5 months now. But I get really stucked when it comes to matplotlib. There's always so many options to do anything, and I can't see a well defined path to do anything. Does anyone have this problem too and knows how to deal with it?
I had the same problem sometime back. I just picked the Boston Housing Prices dataset and kept practicing on that. If you work on it enough you will be able to create all types of plots for the EDA and get good practice. Of course after a certain point it can get boring , thats when you jump to a dataset in an area of your interests, in my case it was movie reviews.
Below is the link to the housing prices data.
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
I think your question is stating that you are bored and do not have any projects to make. If that is correct, there are many datasets available on sites like Kaggle that have open-source datasets for practice programmers.
in programming in general " There's always so many options to do anything".
i recommend to you that read library and understand their functions and classes in a glance, then go and solve some problems from websites or give a real project if you can. if your code works do not worry and go ahead.
after these try and error you have a lot of real idea about various problems and you recognize difference between these options and pros and cons of them. like me three years ago.
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So I have a scraper that gets articles. However, it doesn't always work properly. I want to get better at checking when it doesn't work. For example, the following is something like I want it to scrape:
Hello. This is a sequence of sentences that are put together. They don't have to follow this exact format, but something very close to this would be nice! Just basically stuff like this put together with the occasional weird formatting, which depends on what is scraped.
But I might also get something that is obviously not text:
REGISTER | LOGIN | LOGOUT | Sign in to your account Forgot your password? {* #signInForm *}....
Is there any python library that checks the general format of strings? Basically, I am scraping articles and want to see if the text scraped is article-y. If there isn't a python library, would the best way to go be some sort of regex matching? Is this possible to do reasonably well?
Any help would be greatly appreciated, thanks!!
[edit] if you voted to close, do you mind leaving a comment as to why? Reason being: There is no stack exchange for NLP. Hence, where else can I ask this question? Thanks.
There are many ways to do this, and without seeing a lot more of your data predicting the correct way will be difficult.
That said, here's one simple strategy: split the text into words and check if it statistically looks like writing as opposed to boilerplate. For example, in English for any sufficiently long piece of writing roughly 5% of the tokens should be the word the. For short pieces of text this is less reliable but based on your examples above a very simple check along these lines ("do a|an|the make up more than 1% of the tokens?") may work.
For more sophisticated methods you can look at a list of boilerplate removal libraries here.
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My goal is to send emails entirely from python. I want to do it all from scratch, maybe go as far as building an email server in python if someone hasn't done it already. I want to do this because I'm basically tired of using Postfix or the common email providers with the standard SMTP/POP/IMAP libraries. Also, another reason I want to try to do this is because I want to try and understand better how the email protocols work.
I'm not entirely sure where to start. Maybe I should take a look at the Postfix source code and try and make a python SMTP server. I know it would be much easier to just stick with the standard way of doing it instead of building from scratch, but like I said, this is more of an educational study for me to learn how it all works, I will very likely never use it in production.
So, give me ideas guys. Where should I start? If you know of an article that may enlighten me, please post it. --Thanks
It's been done before, but you can always make one if you want.
smtpd was a Python Module of the Week.
This is some good reading that was provided in a similar SO question here.
I've used this before when I was working on a project.