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How can I have a database with thousands of tables with varying number of columns that are all of the same class in Django / SQLAlchemy ORM?

I have financial statement data on thousands of different companies. Some of the companies have data only for 2019, but for some I have decade long data. Each company financial statement have its own table structured as follows with columns in bold:
lineitem---2019---2018---2017
2...............1000....800.....600
3206...........700....300....-200
56.................50....100.....100
200...........1200......90.....700
This structure is preferred over more of a flat file structure like lineitem-year-amount since one query gives me the correct structure of the output for a financial statement table. lineitem is a foreignkey linking to the primary key of a mapping table with over 10,000 records. 3206 can for example mean "Debt to credit instituions". I also have a companyIndex table which has the company ID, company name, and table name. I am able to get the data into the database and make queries using sqlite3 in python, but advanced queries is somewhat of a challenge at times, not to mention that it can take a lot of time and not be very readable. I like the potential of using ORM in Django or SQLAlchemy. The ORM in SQLAlchemy seems to want me to know the name of the table I am about to create and want me to know how many columns to create, but I don't know that since I have a script that parses a datadump in csv which includes the company ID and financial statement data for the number of years it has operated. Also, one year later I will have to update the table with one additional year of data.
I have been watching and reading tutorials Django and SQLAlchemy, but have not been able to try it out too much in practise due to this initial problem which is a prerequisite for succeding in my project. I have googled and googled, and checked stackoverflow for a solution, but not found any solved questions (which is really surprising since I always find the solution on here).
So how can I insert the data using Django/SQLAlchemy given the structure I plan to have it fit into? How can I have the selected table(s) (based on company ID or company name) be an object(s) in ORM just like any other object allowing me the select the data I want at the granularity level I want?
Ideally there is a solution to this in Django, but since I haven't found anything I suspect there is not or that how I have structured the database is insanity.

You cannot find a solution because there is none.
You are mixing the input data format with the table schema.
You establish an initial database table schema and then add data as rows to the tables.
You never touch the database table columns again, unless you decide that the schema has to be altered to support different, usually additional functionality in the application, because for example, at a certain point in the application lifetime, new attributes become required for data. Not because there is more data, wich simply translates to new data rows in one or more tables.
So first you decide about a proper schema for database tables, based on the data records you will be reading or importing from somewhere.
Then you make sure the database is normalized until 3rd normal form.
You really have to understand this. Haven't read it, just skimmed over but I assume it is correct. This is fundamental database knowledge you cannot escape. After learning it right and with practice it becomes second nature and you will apply the rules without even noticing.
Then your problems will vanish, and you can do what you want with whatever relational database or ORM you want to use.
The only remaining problem is that input data needs validation, and sometimes it is not given to us in the proper form. So the program, or an initial import procedure, or further data import operations, may need to give data some massaging before writing the proper data rows into the existing tables.

Flask website backend structure guidance assistance?

I have a basic personal project website that I am looking to learn some web dev fundamentals with and database (SQL) fundamentals as well (If SQL is even the right technology to use??).
I have the basic skeleton up and running but as I am new to this, I want to make sure I am doing it in the most efficient and "correct" way possible.
Currently the site has a main index (landing) page and from there the user can select one of a few subpages. For the sake of understanding, each of these sub pages represents a different surf break and they each display relevant info about that particular break i.e. wave height, wind, tide.
As I have already been able to successfully scrape this data, my main questions revolve around how would I go about inserting this data into a database for future use (historical graphs, trends)? How would I ensure data is added to this database in a continuous manner (once/day)? How would I use data that was scraped from an earlier time, say at noon, to be displayed/used at 12:05 PM rather than scraping it again?
Any other tips, guidance, or resources you can point me to are much appreciated.

This kind of data is called time series. There are specialized database engines for time series, but with a not-extreme volume of observations - (timestamp, wave heigh, wind, tide, which break it is) tuples - a SQL database will be perfectly fine.
Try to model your data as a table in Postgres or MySQL. Start by making a table and manually inserting some fake data in a GUI client for your database. When it looks right, you have your schema. The corresponding CREATE TABLE statement is your DDL. You should be able to write SELECT queries against your table that yield the data you want to show on your webapp. If these queries are awkward, it's a sign that your schema needs revision. Save your DDL. It's (sort of) part of your source code. I imagine two tables: a listing of surf breaks, and a listing of observations. Each row in the listing of observations would reference the listing of surf breaks. If you're on a Mac, Sequel Pro is a decent tool for playing around with a MySQL database, and playing around is probably the best way to learn to use one.
Next, try to insert data to the table from a Python script. Starting with fake data is fine, but mold your Python script to read from your upstream source (the result of scraping) and insert into the table. What does your scraping code output? Is it a function you can call? A CSV you can read? That'll dictate how this script works.
It'll help if this import script is idempotent: you can run it multiple times and it won't make a mess by inserting duplicate rows. It'll also help if this is incremental: once your dataset grows large, it will be very expensive to recompute the whole thing. Try to deal with importing a specific interval at a time. A command-line tool is fine. You can specify the interval as a command-line argument, or figure out out from the current time.
The general problem here, loading data from one system into another on a regular schedule, is called ETL. You have a very simple case of it, and can use very simple tools, but if you want to read about it, that's what it's called. If instead you could get a continuous stream of observations - say, straight from the sensors - you would have a streaming ingestion problem.
You can use the Linux subsystem cron to make this script run on a schedule. You'll want to know whether it ran successfully - this opens a whole other can of worms about monitoring and alerting. There are various open-source systems that will let you emit metrics from your programs, basically a "hey, this happened" tick, see these metrics plotted on graphs, and ask to be emailed/texted/paged if something is happening too frequently or too infrequently. (These systems are, incidentally, one of the main applications of time-series databases). Don't get bogged down with this upfront, but keep it in mind. Statsd, Grafana, and Prometheus are some names to get you started Googling in this direction. You could also simply have your script send an email on success or failure, but people tend to start ignoring such emails.
You'll have written some functions to interact with your database engine. Extract these in a Python module. This forms the basis of your Data Access Layer. Reuse it in your Flask application. This will be easiest if you keep all this stuff in the same Git repository. You can use your chosen database engine's Python client directly, or you can use an abstraction layer like SQLAlchemy. This decision is controversial and people will have opinions, but just pick one. Whatever database API you pick, please learn what a SQL injection attack is and how to use user-supplied data in queries without opening yourself up to SQL injection. Your database API's documentation should cover the latter.
The / page of your Flask application will be based on a SQL query like SELECT * FROM surf_breaks. Render a link to the break-specific page for each one.
You'll have another page like /breaks/n where n identifies a surf break (an integer that increments as you insert surf break rows is customary). This page will be based on a query like SELECT * FROM observations WHERE surf_break_id = n. In each case, you'll call functions in your Data Access Layer for a list of rows, and then in a template, iterate through those rows and render some HTML. There are various Javascript and Python graphing libraries you can feed this list of rows into and get graphs out of (client side or server side). If you're interested in something like a week-over-week change, you should be able to express that in one SQL query and get that dataset directly from the database engine.
For performance, try not to get in a situation where more than one SQL query happens during a page load. By default, you'll be doing some unnecessary work by going back to the database and recomputing the page every time someone requests it. If this becomes a problem, you can add a reverse proxy cache in front of your Flask app. In your case this is easy, since nothing users do to the app cause its content to change. Simply invalidate the cache when you import new data.

User friendly SQLite database csv file import update solution

I was wondering if there is a way to allow a user to export a SQLite database as a .csv file, make some changes to it in a program like Excel, then upload that .csv file back to the table it came from using a record UPDATE method.
Currently I have a client that needed an inventory and pricing management system for their e-commerce store. I designed a database system and logic in Python 3 and SQLite. The system from a programming standpoint works flawlessly.
The problem I have is that there are some less then technical office staff that need to edit things like product markup within the database. Currently, I have them setup with SQLite DB Browser, from there they can edit products one at a time and write the changes to the database. They can also export tables to a .csv file for data manipulation in Excel.
The main issue is getting that .csv file back into the table it was exported from using an UPDATE method. When importing a .csv file to a table in SQLite DB Browser there is no way to perform an update import. It can only insert new rows by default and do to my table constraints that is a problem.
I like SQLite DB Browser because it is clean and simple and does exactly what I need. However, as soon as you have to edit more then one thing at a time and filter information in more complicated ways it starts to lack the functionality needed.
Is there a solution out there for SQLite DB Browser to tackle this problem? Is there a better software option all together to interact with a SQLite database that would give me that last bit of functionality?

Have you tried SQLiteForExcel? however, some coding is required.

So after researching some off the shelf options I found that the Devart Excel Add Ins did exactly what I needed. They are paid add ins, however, they seem to support almost all modern databases including SQlite. Once the add in is installed you can connect to a database and manipulate the data returned just like normal in Excel including bulk edits and advanced filtering, all changes are highlighted and can easily be written to the database with one click.
Overall I thought it was a pretty solid solution and everyone seems to be very happy with it as it made interacting with a database intuitive and non threatening to the more technically challenged.

Couchdb/Mongodb Application/Logic layer, like Oracle DB

At my work, we use Oracle for our database. Which works great. I am not the main db admin, but I do work with it. One thing I like is that the DB has a built in logic layer using PL/SQL which ca handle logic related to saving the data and retrieve it. I really like this because it allows our MVC application (PHP/Zend Framework) to be lighter, and makes it easier to tie in another platform into the data, such as desktop or mobile.
Although, I have a personal project where I want to use couchdb or mongodb, and I want to try and accomplish a similar goal. outside of the mvc/framework, I want to have an API layer that the main applications talk to. they dont actually talk directly to the database. They specify the design document (couchdb) or something similar for mongo, to get the results. And that API layer will validate the incoming data and make sure that data itself is saved and updated properly. Such as saving a new user, in the framework I only need to send a json obejct with the keys/values that need to be saved and the api layer saves the data in the proper places where needed.
This API would probably have a UI, but only for administrative purposes and to make my life easier. In general it will always reply with json strings, or pre-rendered/cached html in some cases. Since each api layer would be specific to the application anyways.
I was wondering if anyone has done anything like this, or had any tips on nethods I could accomplish this. I am currently looking to write my application in python, and the front end will likely be something like Angularjs. Although I am also looking at node.js for a back end.

We do this exact thing at my current job. We have MongoDB on the back end, a RESTful API on top of it and then PHP/Zend on the front end.
Most of our data is read only, so we import that data into MongoDB and then the RESTful API (in Java) just serves it up.
Some things to think about with this approach:
Write generic sorting/paging logic in your API. You'll need this for lists of data. The user can pass in things like http://yourapi.com/entity/1?pageSize=10&page=3.
Make sure to create appropriate indexes in Mongo to match what people will query on. Imagine you are storing users. Make an index in Mongo on the user id field, or just use the _id field that is already indexed in all your calls.
Make sure to include all relevant data in a given document. Mongo doesn't do joins like you're used to in Oracle. Just keep in mind modeling data is very different with a document database.
You seem to want to write a layer (the middle tier API) that is database agnostic. That's a good goal. Just be careful not to let Mongo specific terminology creep into your exposed API. Mongo has specific operators/concepts that you'll need to mask with more generic terms. For example, they have a $set operator. Don't expose that directly.
Finally after having a decent amount of experience with CouchDB and Mongo, I'd definitely go with Mongo.

Python: RE vs. Query

I am building a website using Django, and this website uses blocks which are enabled for a certain page.
Right now I use a textfield containing paths were a block is enabled. When a page is requested, Django retrieves all blocks from database and does re.search on the TextField.
However, I was wondering if it is not a better idea to use a separate DB table for block/paths, were each row contains a single path and reference to a block, in terms of overhead.

A seperate DB table is definitely the "right" way to do it, because mysql has to send all the data from your TEXT fields every time you query. As you add more rows and the TEXT fields get bigger, you'll start to notice performance issues and eventually crash the server. Also, you'll be able to use VARCHAR and add a unique index to the paths, making lookups lightning fast.

I am not exactly familiar with Django, but if I am understanding the situation correctly, you should use a table.
In fact this is exactly the kind of use that DB software is designed and optimized for.
No worries. It will actually be faster.
By doing the search yourself, you are trying to implement part of the DB logic on your own. Fun, certainly, but not so fast. :)
Here are some nice links on designing a database:
http://dev.mysql.com/tech-resources/articles/intro-to-normalization.html
http://en.wikipedia.org/wiki/Third_normal_form
Hope this helps. Good luck. :-)