Day 60 | #66daysofdata Working on an investment portfolio app.

Day 61 | #66daysofdata Worked on conditional statements to highlight areas in my investment allocations that are gaining or losing value.

Day 62 | #66daysofdata Reviewing the differences between regular methods in a function versus class methods: - Regular methods take the instance as the first argument, passed as "self". - Class methods take the class as the first argument, passed as "cls".

Day 63 | #66daysofdata Read the following article and learned about the aggregate function, STRING_AGG(), as well as ROLLUP() which is a sub-clause of GROUP BY. towardsdev.com/useful-databas…

Day 64 | #66daysofdata Dealing with data being transformed incorrectly. I.e., leading zeros from a .CSV format being dropped when being converted from a .TXT pipe delimited format.

Day 65 | #66daysofdata Did more work on my investment portfolio project. Hand picked a few stocks that I would like to track percent changes over different time intervals.

well, something's got to give sooner or later!

Day 66 | #66daysofdata And with that, this challenge is complete. Admittedly, the latter half of the challenge saw a steep drop in length of study/practice. Either way, I've accomplished my primary goal, which was to integrate Python automation to optimize my day-to-day job.

Having some fun with Streamlit. Using Pandas to import yfinance data on stocks of interest, exporting that data into a .csv, and then writing it into Streamlit. Created a few derived columns based on rolling average calculations for different time intervals. #66daysofdata

More code refactoring. While building my projects up, I start in Jupyter notebooks. But D.R.Y principle is typically not followed during this process. Eventually, I copy the code to a .py file to find areas to optimize design. #66daysofdata

Worked more with my yfinance library. There's just so many different methods to explore, I can't seem to put it down. After merging a couple of dataframes, I pulled the data into Streamlit with a few conditional statements to denote different colors based on percent change.

Git Workflow 👇

Own companies that own brands

Worked with AG Grid today, which allowed for a more interactive Streamlit dataframe. This introduced much better sorting and filtering utility. I especially like the capability to select rows within the main table, to then output those rows into a separate table for review.

The tech industry is going through a terrible churn right now with more than 120,000 layoffs in 2022. But, what makes it worse is the overall direction. September ~6,000 layoffs October ~ 12,500 layoffs November ~ 45,000 layoffs with still a week to go!

All it took was a few Fed hikes!