I used to upload csv, excel, json or geojson files in my a postegreSQL using Python/Django.
I noticed that the scripts is redundant and sometimes difficult to maintain when we need to update key or columns. Is there a way to use design pattern? I have never used it before.
Any suggestion or links could be hep!
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Thanks for taking the time to read my question.
I am working on a personal project to learn python scripting for excel, and I want to learn how to move data from one workbook to another.
In this example, I am emulating a company employee ledger that has name, position, address, and more (The organizations is by row so every employee takes up one row). But the project is to have a selected number of people be transferred to a new ledger (another excel file). So I have a list of emails in a .txt file (it could even be another excel file but I thought .txt would be easier), and I would want the script to run through the .txt file, get the emails, and look for any rows that have a matching email address(all emails are in cell 'B'). And if any are found, then copy that entire row to the new excel file.
I tried a lot of ways to make this work, but I could not figure it out. I am really new to python so I am not even sure if this is possible. Would really appreciate some help!
You have essentially two packages that will allow manipulation of Excel files. For reading in data and performing analysis the standard package for use is pandas. You can save the files as .xlsx however you are only really working with base table data and not the file itself (IE, you are extracing data FROM the file, not working WITH the file)
However what you need is really to perform manipulation on Excel files directly which is better done with openpyxl
You can also read files (such as your text file) using with open function that is native to Python and is not a third party import like pandas or openpyxl.
Part of learning to program includes learning how to use documentation.
As such, here is the documentation you require with sufficient examples to learn openpyxl: https://openpyxl.readthedocs.io/en/stable/
And you can learn about pandas here: https://pandas.pydata.org/docs/user_guide/index.html
And you can learn about python with open here: https://docs.python.org/3/tutorial/inputoutput.html
Hope this helps.
EDIT: It's possible I or another person can give you a specific example using your data / code etc, but you would have to provide it fully. Since you're learning, I suggest using the documentation or youtube.
We're creating gamma-cat, an open data collection for gamma-ray astronomy, and are looking for advice (here, or links to resources, formats, tools, packages) how to best set it up.
The data we have consists of measurements for different sources, from different papers. It's pretty heterogeneous, sometimes there's data for multiple sources in one paper, for each source there's usually several papers, sometimes there's no spectrum, sometimes one, sometimes many, ...
Currently we just collect the data in an input folder as YAML and CSV files, and now we'd like to expose it to users. Mainly access from Python, but also from Javascript and accessible from a static website.
The question is what format and organisation we should use for the data, and if there's any Python packages that will help us generate the output files as a set of linked data, as well as Python and Javascript packages that will help us access it?
We would like to get multiple "views" or simple "queries" of the data, e.g. "list of all sources", "list of all papers", "list of all spectra for source X", "spectrum A from paper B for source C".
For format, probably JSON would be a good choice? Although YAML is a bit nicer to read, and it's possible to have comments and ordered maps. We're storing the output files in a git repo, and have had a lot of meaningless diffs for JSON files because key order changes all the time.
To make the datasets discoverable and linked, I don't know what to use. I found e.g. http://jsonapi.org/ but that seems to be for REST APIs, not for just a series of flat JSON files on a static webserver? Maybe it could still be used that way?
I also found http://json-ld.org/ which looks relevant, but also pretty complex. Would either of those or something else be a good choice?
And finally, we'd like to generate the linked and discoverable files in output from just a bunch of somewhat organised YAML and CSV files in input using Python scripts. So far we just wrote a bunch of Python classes or scripts based on Python dicts / lists and YAML / JSON files. Is there a Python package that would help with that task of generating the linked data files?
Apologies for the long and complex question! I hope it's still in scope for SO and someone will have some advice to share.
Judging from the breadth of your question, you are new to linked data. The least "strange" format for you might be the Data Package. In the most common case it's just a zip archive of a CSV file and JSON metadata. It has a Python package.
If you have queries to the data, you should settle for a database (triplestore) with a SPARQL endpoint. Take a look at Fuseki. You can then use Turtle or RDF/XML for file export.
If the data comes from some kind of a tool, you can model the domain it represents using Eclipse Lyo (tutorial).
These tools are maintained by 3 different communities, you can reach out to their user mailing lists separately if you have further questions about them.
I want to enter data into a Microsoft Excel Spreadsheet, and for that data to interact and write itself to other documents and webforms.
With success, I am pulling data from an Excel spreadsheet using xlwings. Right now, I’m stuck working with .docx files. The goal here is to write the Excel data into specific parts of a Microsoft Word .docx file template and create a new file.
My specific question is:
Can you modify just a text string(s) in a word/document.xml file and still maintain the integrity and functionality of its .docx encasement? It seems that there are numerous things that can change in the XML code when making even the slightest change to a Word document. I've been working with python-docx and lxml, but I'm not sure if what I seek to do is possible via this route.
Any suggestions or experiences to share would be greatly appreciated. I feel I've read every article that is easily discoverable through a google search at least 5 times.
Let me know if anything needs clarification.
Some things to note:
I started getting into coding about 2 months ago. I’ve been doing it intensively for that time and I feel I’m picking up the essential concepts, but there are severe gaps in my knowledge.
Here are my tools:
Yosemite 10.10,
Microsoft Office 2011 for Mac
You probably need to be more specific, but the short answer is, in principle, yes.
At a certain level, all python-docx does is modify strings in the XML. A couple things though:
The XML you create needs to remain well-formed and valid according to the schema. So if you change the text enclosed in a <w:t> element, for example, that works fine. Conversely, if you inject a bunch of random XML at an arbitrary point in one of the .xml parts, that will corrupt the file.
The XML "files", known as parts that make up a .docx file are contained in a Zip archive known as a package. You must unpackage and repackage that set of parts properly in order to have a valid .docx file afterward. python-docx takes care of all those details for you, but if you're going directly at the .docx file you'll need to take care of that yourself.
I need to create some excel tables, but these tables don't have simple look.
There are some pictures, some special fonts etc.
But the complicated parts are static, that means always the same.
So my idea was, I will create an excel-template with these tricky parts and then from python just insert dynamic data to this template.
I am working with pandas framework, but I didn't find a way how to do that with or without this framework.
Any idea?
There isn't an easy way to do this with any of the usual "direct file manipulation" libraries in Python (xlrd, xlwt, XlsxWriter, OpenPyXL; these are what pandas uses). The reason is that the structure of a workbook file is such that it's impossible or prohibitively difficult (depending on whether you're talking about .xls or .xlsx) to do anything resembling "in-place" editing, short of re-implementing Excel itself.
So for what you're trying to do, your best option is to let Excel do the work. (I'm assuming you can run Excel, since you mention that you'd like to create Excel templates.) There are ways to automate Excel, the most straightforward probably being Microsoft's VBA or VBScript. But if you want to do it in Python, you can, using PyWin32 or pywinauto.
I'm programming a MySQL database with Web interface for remote access. I used Django as a framework. But now, I want to generate some reports using the MySQL data and modify them after generating. Therefore, I automatically think of exporting data to or importing from Word. The thing is, how I do this?
I have seen several options. One of them, using Python-docx, a library to generate docx documents in Python. I could have a problem with this, because the generated reports will be large, with lots of images, tables, pages, etc. I worked with xlsxwriter, and when the files were large it took long time to generate de xlsx. I don't know if Python-docx would be the better solution.
Other option is to import data directly from Microsoft Word, using some software for this concrete purpose or using a macro VBA. I have programmed some example code with VBA to import data of MySQL using connectors ODBC and it's immediately possible, but there is thousand of objects and classes of VBA Word to learn.
Exposed the problem, any tips or suggestions??? Thanks in advance!
Another option is to generate HTML & open as a word document.
If you take a document similar to what you want to generate & save as HTML you will see what word does. Take this file as a template for your documents