Dilcia19
commented
3 years ago Have a draft:
For our analysis of California’s layoff notices, we collected data from the Employment Development Department. From their website, we gathered records from as far back as 2014 to the end of 2020.
Once we had our data, we sought to answer a few questions: How many people were laid off in 2020? How did that compare to the amount of layoffs in 2019? Can the numbers show how weak hard a county was hit just by comparing the layoff numbers?
In order to answer these questions, we identified possible duplicates from the data by standardizing it and comparing the data. We then removed the duplicates and kept the data that did not fall under the duplication criteria where layoff county, layoff number, layoff notice dates, and layoff city matched.
After that, we used programming to crunch the numbers and figure out where the biggest impacts from layoffs were being felt. We used these numbers as a way to contextualize the already difficult situation for people who are in danger of being evicted or those dealing with housing instability due to the pandemic.
Write a side bar explaining how we did our analysis for people not necessarily familiar with programming or jupyter notebook.