Python and creating Excel-like tables of data - python

I am relatively new to (Python) programming and would like some direction for my current project.
I have been learning to web scrape and parse data. I am now sitting on a program that can create lots of information in the form of lists and dictionaries.
I now need a way to create formatted tables and output to some sort of web-based system. I am looking at tables of about 10-40 rows and 20 columns of alphanumeric (names and numbers) data. If I can produce basic bar/line charts that would be useful. It also needs to be entirely automated - the program will run once a day and download information. I need it to output seamlessly in report form to something like dropbox that I can access on-the-go. The table template will always be the same and will be heavily formatted (colouring mostly, like Excel conditional formatting).
I am also keen to learn to create web apps and I'm wondering if this is something I can do with this project? I'm not sure what I'll need to do and I'd like some direction. I'm pretty new to programming and the jargon is tough to wade through. Is it possible to create a website that takes automated input and creates good-looking data tables? Is this efficient? What are the available tools for this? If not efficient what are the other available options?

Related

MES system for manufacturing plant

I am working on creating an app that access PLC to gather data and then display it on the screen with the option of viewing the data as an interactive graph. There has been some work done in python that collects data, store them in excel and at the end process the data to get some meaning full data points and visualization using matplotlib.
They have used tkinter to be the interface to get data and display the current values on 2 screens, I have plans to update the program to store the data in a database and query them as per the needs using python.
What would be the ideal GUI package and the data visualization tools I can use to make the app interactive and easy to use on a PC?
As for as why I chose python, I feel comfortable using python.
Thank you guys in advance!
It's not a free solution, but you might want to look at Ignition [link]
I have been using it for several years now and it has some great depth and flexibility for capturing data from most brands of PLCs and then displaying the data on a HMI, webpage, uploading to SQL, and so on.
The scripting language is jython, so you should feel very comfortable there. You can also try if for free for two-hours at a time. Then reset the trial as many times as you like.

Automating jupyter notebook visualizations

I have created a lot of visualization for a transaction data set of my company using jupyter notebook. Now I want to make that code reusable so that after my internship other people from my company will be able to generate those visualizations on next quarter data set. Also, they don't know python. Hence I need to create something that grabs data and throws the visualization without much hassle. How can I do this.
On top of my head i could think of many ideas here are some of them.
Using python interface (tkinter) that accepts the data from the user and show visualization.
Another solution could be you can create a web app using flask and accept the data from the user and visualize it.
If all of this is not possible you could easily create a script that accepts user data and then separate in 2 categories or dataframe for eg. object datatypes and numerical datatype and then plot using for loops.

Best way to link excel workbooks feeding into each other for memory efficiency. Python?

I am building a tool which displays and compares data for a non specialist audience. I have to automate the whole procedure as much as possible.
I am extracting select data from several large data sets, processing it into a format that is useful and then displaying it in a variety of ways. The problem i foresee is in the updating of the model.
I don't really want the user to have to do anything more than download the relevant files from the relevant database, re-name and save them to a location and the spreadsheet should do the rest. Then the user will be able to look at the data in a variety of ways, perform a few different analytical functions, depending on what they are looking at. Output some graphs etc
Although some database exported files wont be that large, other data will be being pulled from very large xml or csv files (500000x50 cells) and there are several arrays working on the pulled data once it has been chopped down to the minimum possible. So it will be necessary to open and update several files in order, so that the data in the user control panel is up to date and not all at once so that the user machine freezes.
At the moment I am building all of this just using excel formulas.
My question is how best to do the updating and feeding bit. Perhaps some kind of controller program built with python? I don't know Python but i have other reasons to learn it.
Any advice would be very welcome.
Thanks

Extract webscraped python data to SQLite, excel or xml?

I'm kinda new to Python and webscraping but I'm currently at a point where I need to extract data to a database. Can someone tell me the pros and cons by using sqlite, excel or xml?
I've read that sqlite should be the fastest, so I may go for that database structure, but can someone then tell me what IDE you use to handle sqlite data after I've extracted it from python?
Edit: I hope my post makes sense. I'm currently trying to use a web scraper from here: https://github.com/gingeleski/odds-portal-scraper
Thanks in advance.
For the short term, Excel is a good way to examine your data and prototype analysis and visualizations. It gets old using it for very large datasets, or multiple similar datasets. Basically as soon as you start doing the same thing more than twice or writing VB code you should switch to the pandas/matplotlib solution.
It looks like the scraper you are using already puts the results in an SQLITE database, but if you have your data in a list or dictionary, I'd suggest using pandas to do calculations and matplotlib for visualizations, as that will give you a robust, extensible solution over the long term. It is very easy to read and write data between an SQLITE database and pandas.
A good way of viewing the data in the DB is a must. I'm currently using SQLiteStudio.
When you say IDE, I'm assuming you're looking for a way to view the SQLite data? If so, DBeaver is a free, open source SQL client. You could use this to view the data quite easily.

migrating data from tomcat .dbx files

I want to migrate data from an old Tomcat/Jetty website to a new one which runs on Python & Django. Ideally I would like to populate the new website by directly reading the data from the old database and storing them in the new one.
Problem is that the database I was given comes in the form of a bunch of WEB-INF/data/*.dbx and I didn't find any way to read them. So, I have a few questions.
Which format do the WEB-INF/data/*.dbx use?
Is there a python module for directly reading from the WEB-INF/data/*.dbx files?
Is there some external tool for dumpint the WEB-INF/data/*.dbx to an ascii format that will be parsable by python?
If someone has attempted a similar data migration, how does it compare against scraping the data from the old website? (assuming that all important data can be scraped)
Thanks!
The ".dbx" suffix has been used by various softwares over the years so it could be almost anything. The only way to know what you really have here is to browse the source code of the legacy java app (or the relevant doc or ask the author etc).
wrt/ scraping, it's probably going to be a lot of a pain for not much results, depending on the app.

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