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I have spent the last six months learning python as a way to automate my working environment. So far I have automated data extraction and report downloading from various web-based sources through the use of webcrawlers, interacted with excel files, created visual representations of data through matplotlib, and removed almost all the monotony from bank reconciliation.
I now come to a new task which takes up a large amount of my daily workload. We use an accounts program called Sage 50 Accounts. I effectively want to begin to learn how to manipulate the data contained within this program so that my daily thought patterns can be put into Python code.
Because this hasn't been done, there's no pre-made API. So my question is:
When wishing to interact with a new program through Python, how does a programmer begin such an inquiry?
Please accept that this question is only vague and general because I'm incredibly new to such a task.
SData is Sage's general data access API layer and should suit your purposes.
Otherwise you might need to invest in or obtain a Sage Development SDK.
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For background, I am somewhat of a self-taught Python developer with only some formal training with a few CS courses in school.
In my job right now, I am working on a Python program that will automatically parse information from a very large text file (thousands of lines) that's a output result of a simulation software. I would like to be doing test driven development (TDD) but I am having a hard time understanding how to write proper unit tests.
My trouble is, the output of some of my functions (units) are massive data structures that are parsed versions of the text file. I could go through and create those outputs manually and then test but it would take a lot of time. The whole point of a parser is to save time and create structured outputs. Only testing I've been doing so far is trial and error manually which is also cumbersome.
So my question is, are there more intuitive ways to create tests for parsers?
Thank you in advance for any help!
Usually parsers are tested using a regression testing system. You create sample input sets and verify that the output is correct. Then you put the input and output in libraries. Each time you modify the code, you run the regression test system over the library to see if anything changes.
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I would like to ask a question about transferring data from c++ to python in real time.
My situation is :
1) I am generating data every 1 ms in c++,
2) I would like to stack data for certain amount of time and make a dataset,
3) I would like to run some machine learning algorithm written in Python without turning of c++ program.
So far, I have thought about several things :
option 1 ) Save the dataset as a txt file and read it from python. But this seems too slow due to I/O process.
option 2 ) Use IPC such as zeromq. I am quite new to IPC, so I am not sure if it is the thing I really want. Also, among the multiple methods (mmap, shared memory, message queue, ...), I do not know which one is the best shot for me.
option 3 ) Use UDP. From my understanding UDP sometimes sends the same data twice, or skips the data, or mixes data up (e.g. previous time step data arrives later)
Is there any recommendations I need to search and study?
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I am a python beginner and I am a little experienced in OO-programming in Java and PHP and also fucntional programming in R . Thus, my question is considering the general usage of python scripts in everyday use-cases.
I want to "learn" how to think/approach a problem that I do experience when facing a situation with my software where a "script" could help me out or improve something.
For instance, I've heard friends talking about their self-made python scripts to evenly mute the audio of movies to avoid loud outliers in explosive scenes, etc. Another example, in my case righ tnow, is to filter out certain pictures with no GPS-time meta information for the timezone in order to sort these fotos in accordance with the others.
I really want to get the essence and recipe based on the aforementioned examples to better integrate Python in my everyday life and get an intuitive feeling for it. (i.e. how would a simple script look like that takes a picture, filters out its meta data, and does something -> where do I have to run the script so I can call the function with these .JPG files as its arguments?).
I would also be glad if some of you could recommend some practical tutorials or literature.
Thank you in advance :)
P.S. I know it is not a concrete question but rather it is intended to get a glimpse on a wide field of usage and thinking - but I want to get this essential take away that motivates me and shows me the direction.
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I'd like to write a program that indexes my pdf and music files on my hard drive(not server). I plan to do this via perl or python, or both. I'll basically be writing a crawler for my desctop. The user interface will be in JavaFx, which I think quite fluent in. I've done a couple of projects in JavaFx. I have not done anything in perl/ python. I however, have done a few lines of code in them while teaching myself the syntax.
The question is what topics should I start my research in when embarking on writing a crawler. I've seen quite a number of tutorials online on crawlers but all do web page indexing. Plus what modules should I look into?
In python to find the files you can use os.walk - the examples in the help are very helpful.
Assuming that you are looking to do more than just locate the files and get their names you will need to look into getting some more information about the contents, there are python libraries that can get text from pdf files such as PDFMiner and pdfquery.
Likewise there are numerous python tools that can get you some more information on your music files.
It all depends on how you are planning on indexing them.
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I want to turn an existing python-based implementation (a data analysis tool on a stream of events) into a storm topology.
During the research phase my team and I used python pandas a lot to develop a prototype of our tool and found it to be very helpful in terms of programmer productivity. Now we want to create a storm topology that does the same thing and aim to reuse our existing python modules as bolts or at least make an informed decision about whether it is a good idea performance-wise to do so.
Any restrictions on using a python script depending on external libs as a Storm Bolt on a cluster? Also, does anybody have a feeling for what the performance penalty will be for using an interpreted and non-JVM language like Python instead of Java for our bolts? The pandas library itself is designed with high-performance in mind.
Thanks